From 93647fb43c34252e724512fa57fb6d15cbbad2df Mon Sep 17 00:00:00 2001 From: NootNoot0 <161794667+NootNoot0@users.noreply.github.com> Date: Mon, 24 Jun 2024 15:57:59 +0800 Subject: [PATCH 1/3] Add files via upload --- kubeflow/LICENSE | 201 + kubeflow/README.md | 354 + kubeflow/examples/0.helloworld/flows.json | 101 + .../examples/0.helloworld/flows_cred.json | 3 + kubeflow/examples/0.helloworld/helloworld.py | 6 + kubeflow/examples/0.helloworld/package.json | 6 + kubeflow/examples/0.helloworld/settings.js | 498 + .../examples/1.connect-kubeflow/flows.json | 368 + .../1.connect-kubeflow/flows_cred.json | 3 + .../node_modules/nodepipe/LSTM.html | 149 + .../node_modules/nodepipe/LSTM.js | 66 + .../node_modules/nodepipe/LSTM0.js | 147 + .../node_modules/nodepipe/decisionTree0.js | 147 + .../node_modules/nodepipe/decisiontree.html | 149 + .../node_modules/nodepipe/decisiontree.js | 66 + .../node_modules/nodepipe/fl.html | 149 + .../node_modules/nodepipe/fl.js | 66 + 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kubeflow/examples/docker-compose.yaml | 33 + kubeflow/examples/package-lock.json | 10782 ++++++++++++++++ kubeflow/examples/package.json | 127 + kubeflow/examples/run.sh | 15 + kubeflow/examples/scripts/entrypoint.sh | 14 + 63 files changed, 17733 insertions(+) create mode 100644 kubeflow/LICENSE create mode 100644 kubeflow/README.md create mode 100644 kubeflow/examples/0.helloworld/flows.json create mode 100644 kubeflow/examples/0.helloworld/flows_cred.json create mode 100644 kubeflow/examples/0.helloworld/helloworld.py create mode 100644 kubeflow/examples/0.helloworld/package.json create mode 100644 kubeflow/examples/0.helloworld/settings.js create mode 100644 kubeflow/examples/1.connect-kubeflow/flows.json create mode 100644 kubeflow/examples/1.connect-kubeflow/flows_cred.json create mode 100644 kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.html create mode 100644 kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js create mode 100644 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kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/decisionTree.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/fl.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/helloworld.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/install.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/kfp_example.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/kfp_login.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/mnist.py create mode 100644 kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml create mode 100644 kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml create mode 100644 kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml create mode 100644 kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml create mode 100644 kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml create mode 100644 kubeflow/examples/1.connect-kubeflow/py/requirements.txt create mode 100644 kubeflow/examples/1.connect-kubeflow/settings.js create mode 100644 kubeflow/examples/Dockerfile create mode 100644 kubeflow/examples/README.md create mode 100644 kubeflow/examples/build.sh create mode 100644 kubeflow/examples/docker-compose.yaml create mode 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Please refer the following example to launch an individual example: + +1. In terminal (If you on Windows system, please use WSL) +``` +$ git clone https://github.com/NightLightTw/kube-nodered.git +``` +2. Enter target folder +``` +cd kube-nodered/examples +``` +3. Enter account information and start +``` +KUBEFLOW_HOST= \ +KUBEFLOW_USERNAME= \ +KUBEFLOW_PASSWORD= \ +./run.sh +``` +> **Info:** Here please use 1.connect-kubeflow + +## Install dependencies + +1. Then you can go to UI, check it out: http://127.0.0.1:1880/ +![](https://hackmd.io/_uploads/HJ8Rbmdya.png) + +2. Click the “install dependency” button to install dependency items such as specific python libraries and wait for its completion +![](https://hackmd.io/_uploads/rygsMmuy6.png) + +3. Click the “list experiments” button to test the environment work! +![](https://hackmd.io/_uploads/HyXxfX_kp.png) + +## Using our nodes +Switch to the "three-pipeline" flow and press the button to trigger the pipeline process + +![](https://hackmd.io/_uploads/Hkr4mXdk6.png) + +On kubeflow: +![](https://hackmd.io/_uploads/H1_hEX_JT.png) + + + +> **Info:** If the environment variable does not work, please fill in the account password directly in the python file + + +## Test python files to interact with kubeflow +``` +# Open another terminal and check docker status +docker ps +#enter container +docker exec -it bash +#enter document folder +cd /data/1.connect-kubeflow/py/api_examples +#execute function +python3 +``` + +You can test the file in api_example +> **Info:** Some of these files require a custom name, description, or assigned id in + +## Possible problems and solution +Q1: MissingSchema Invalid URL '' + +A1: This problem means that the login information is not accessed correctly, which may be caused by the environment variable not being read. +You can directly override the login information of the specified file + +ex:![](https://hackmd.io/_uploads/ryx59rejBh.jpg) + +Change to your own login information +``` +host = "https://example@test.com" +username = "test01" +password = "123456" +``` + +# Modify your own custom nodes/pipeline +![implementation architecture](https://hackmd.io/_uploads/H1ZLgUsH2.png) + +## Kubeflow part +### Custom make pipeline’s yaml file +Please refer to [Kubeflow implementation：add Random Forest algorithm](https://hackmd.io/@Nhi7So-lTz2m5R6pHyCLcA/Sk1eZFTbh) + +### Take changing randomForest.py as an example + +Modify using your own yaml file path +> **Info:** Line 66: uploadfile='pipelines/only_randomforest.yaml' + +> **Info:** Line 122~129 use json parser for filtering different outputs from get_run() + +```python= +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for only_randomforest." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/only_randomforest.yaml' + name='pipeline-' + random_suffix() + description="This is a only_randomForest pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'run_only_randomForest' + random_suffix() + description = "This is a only_randomForest run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'random-forest-classifier-Accuracy': #change parameter + output_value = parameter['value'] + + if output_value is not None: + print(f"Random Forest Classifier Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) +``` + +## Node-red part +**Package nodered pyshell node** + +**A node mainly consists of two files** +* **Javascript file(.js)** +define what the node does +* **HTML file(.html)** +Define the properties of the node and the windows and help messages in the Node-RED editor + +**When finally package into npm module, will need package.json** + + +### **package.json** +A standard file for describing the content of node.js modules + +A standard package.json can be generated using npm init. This command will ask a series of questions to find a reasonable default value. When asked for the name of the module name: enter the example name node-red-contrib- + +When it is established, you need to manually add the node-red attribute +*p.s. Where the example files need to be changed * + + + +```json= +{ + "name": "node-red-contrib-pythonshell-custom", + ... + "node-red": { + "nodes": { + "decisionTree": "decisiontree.js", + "randomForest": "randomforest.js", + "logisticRegression": "logisticregression.js" + "":"" + } + }, + ... +} + +``` +### **HTML** +```javascript= + + + + + + + \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js new file mode 100644 index 000000000..d16c5fe18 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js @@ -0,0 +1,66 @@ +/** + * Copyright 2014 Sense Tecnic Systems, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + **/ + +var util = require("util"); +var httpclient; +var PythonshellNode = require('./LSTM0'); + +module.exports = function(RED) { + "use strict"; + + function PythonshellInNode(n) { + RED.nodes.createNode(this,n); + + var node = this; + node.config = n; // copy config to the backend so that down bellow we can have a reference + + var pyNode = new PythonshellNode(n); + + pyNode.setStatusCallback(node.status.bind(node)) + + node.on("input",function(msg) { + pyNode.onInput(msg, function(result){ + node.send(result); + }, function(err){ + node.error(err); + }); + }); + + node.on('close', ()=>pyNode.onClose()); + } + + RED.nodes.registerType("LSTM", PythonshellInNode); + + RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { + var node = RED.nodes.getNode(req.params.id); + if (node != null) { + try { + if (node.config.continuous){// see above comment + node.receive({payload: 'pythonshell@close'}) + } else { + node.receive(); + } + res.sendStatus(200); + } catch(err) { + res.sendStatus(500); + node.error(RED._("pythonshell.failed",{error:err.toString()})); + } + } else { + res.sendStatus(404); + } + }); + +} diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js new file mode 100644 index 000000000..86c0742f4 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js @@ -0,0 +1,147 @@ +var fs = require("fs"); + +function PythonshellInNode(config) { + if (!config.pyfile){ + throw 'pyfile not present'; + } + this.pythonExec = config.python3 ? "python3" : "python"; + this.pyfile = '/data/1.connect-kubeflow/py/LSTM.py'; + this.virtualenv = config.virtualenv; + + if (!fs.existsSync(this.pyfile)) { + throw 'pyfile not exist'; + } + + if (this.virtualenv && !fs.existsSync(this.virtualenv)){ + throw 'configured virtualenv not exist, consider remove or change'; + } + + this.stdInData = config.stdInData; + this.continuous = this.stdInData ? true : config.continuous; + this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); + this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); + this.spawn = require('child_process').spawn; + this.onStatus = ()=>{} +} + +PythonshellInNode.prototype.onInput = function(msg, out, err) { + payload = msg.payload || ''; + if (typeof payload === 'object'){ + payload = JSON.stringify(payload); + } else if (typeof payload !== 'string'){ + payload = payload.toString(); + } + + if (payload === 'pythonshell@close'){ + if (this.py != null){ + this.onClose() + return + } else { + // trigger new execution + payload = '' + } + } + + if (this.continuous && !this.stdInData && this.py != null){ + this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) + return + } + + var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec + + if (this.stdInData){ + if (!this.py){ + this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { + cwd: this.pydir, + detached: true + }); + this.firstExecution = true + } else { + this.firstExecution = false + } + } else { + this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { + cwd: this.pydir + }); + } + + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + + // subsequence message, no need to setup callbacks + if (this.stdInData && !this.firstExecution){ + this.py.stdin.write(payload + '\n') + return + } + + var py = this.py; + var dataString = ''; + var errString = ''; + + py.stdout.on('data', data => { + clearTimeout(this.standbyTimer) + + this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) + + let dataStr = data.toString(); + + dataString += dataStr; + + if (dataString.endsWith("\n")){ + if (this.continuous){ + msg.payload = dataString; + out(msg); + dataString = '' + } + } + + this.standbyTimer = setTimeout(()=>{ + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + }, 2000) + + }); + + py.stderr.on('data', data => { + errString += String(data);// just a different way to do it + this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) + }); + + py.stderr.on('error', console.log) + py.stdout.on('error', console.log) + py.stdin.on('error', console.log) + py.on('error', console.log) + + py.on('close', code =>{ + if (code){ + err('exit code: ' + code + ', ' + errString); + this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) + } else if (!this.continuous){ + msg.payload = dataString.trim(); + out(msg); + this.onStatus({fill:"green",shape:"dot",text:"Done"}) + } else { + this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) + } + this.py = null + setTimeout(()=>{ + this.onStatus({}) + }, 2000) + }); + + if (this.stdInData){ + py.stdin.write(payload + '\n') + } +}; + +PythonshellInNode.prototype.onClose = function() { + if (this.py){ + this.py.kill() + this.py = null + } +}; + +PythonshellInNode.prototype.setStatusCallback = function(callback) { + this.onStatus = callback +}; + + +module.exports = PythonshellInNode \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js new file mode 100644 index 000000000..7a8f3a690 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js @@ -0,0 +1,147 @@ +var fs = require("fs"); + +function PythonshellInNode(config) { + if (!config.pyfile){ + throw 'pyfile not present'; + } + this.pythonExec = config.python3 ? "python3" : "python"; + this.pyfile = "/data/1.connect-kubeflow/py/decisionTree.py"; + this.virtualenv = config.virtualenv; + + if (!fs.existsSync(this.pyfile)) { + throw 'pyfile not exist'; + } + + if (this.virtualenv && !fs.existsSync(this.virtualenv)){ + throw 'configured virtualenv not exist, consider remove or change'; + } + + this.stdInData = config.stdInData; + this.continuous = this.stdInData ? true : config.continuous; + this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); + this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); + this.spawn = require('child_process').spawn; + this.onStatus = ()=>{} +} + +PythonshellInNode.prototype.onInput = function(msg, out, err) { + payload = msg.payload || ''; + if (typeof payload === 'object'){ + payload = JSON.stringify(payload); + } else if (typeof payload !== 'string'){ + payload = payload.toString(); + } + + if (payload === 'pythonshell@close'){ + if (this.py != null){ + this.onClose() + return + } else { + // trigger new execution + payload = '' + } + } + + if (this.continuous && !this.stdInData && this.py != null){ + this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) + return + } + + var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec + + if (this.stdInData){ + if (!this.py){ + this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { + cwd: this.pydir, + detached: true + }); + this.firstExecution = true + } else { + this.firstExecution = false + } + } else { + this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { + cwd: this.pydir + }); + } + + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + + // subsequence message, no need to setup callbacks + if (this.stdInData && !this.firstExecution){ + this.py.stdin.write(payload + '\n') + return + } + + var py = this.py; + var dataString = ''; + var errString = ''; + + py.stdout.on('data', data => { + clearTimeout(this.standbyTimer) + + this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) + + let dataStr = data.toString(); + + dataString += dataStr; + + if (dataString.endsWith("\n")){ + if (this.continuous){ + msg.payload = dataString; + out(msg); + dataString = '' + } + } + + this.standbyTimer = setTimeout(()=>{ + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + }, 2000) + + }); + + py.stderr.on('data', data => { + errString += String(data);// just a different way to do it + this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) + }); + + py.stderr.on('error', console.log) + py.stdout.on('error', console.log) + py.stdin.on('error', console.log) + py.on('error', console.log) + + py.on('close', code =>{ + if (code){ + err('exit code: ' + code + ', ' + errString); + this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) + } else if (!this.continuous){ + msg.payload = dataString.trim(); + out(msg); + this.onStatus({fill:"green",shape:"dot",text:"Done"}) + } else { + this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) + } + this.py = null + setTimeout(()=>{ + this.onStatus({}) + }, 2000) + }); + + if (this.stdInData){ + py.stdin.write(payload + '\n') + } +}; + +PythonshellInNode.prototype.onClose = function() { + if (this.py){ + this.py.kill() + this.py = null + } +}; + +PythonshellInNode.prototype.setStatusCallback = function(callback) { + this.onStatus = callback +}; + + +module.exports = PythonshellInNode \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html new file mode 100644 index 000000000..2045d7745 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html @@ -0,0 +1,149 @@ +f + + + + + + + \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js new file mode 100644 index 000000000..1727299e0 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js @@ -0,0 +1,66 @@ +/** + * Copyright 2014 Sense Tecnic Systems, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + **/ + +var util = require("util"); +var httpclient; +var PythonshellNode = require('./decisionTree0'); + +module.exports = function(RED) { + "use strict"; + + function PythonshellInNode(n) { + RED.nodes.createNode(this,n); + + var node = this; + node.config = n; // copy config to the backend so that down bellow we can have a reference + + var pyNode = new PythonshellNode(n); + + pyNode.setStatusCallback(node.status.bind(node)) + + node.on("input",function(msg) { + pyNode.onInput(msg, function(result){ + node.send(result); + }, function(err){ + node.error(err); + }); + }); + + node.on('close', ()=>pyNode.onClose()); + } + + RED.nodes.registerType("decisionTree", PythonshellInNode); + + RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { + var node = RED.nodes.getNode(req.params.id); + if (node != null) { + try { + if (node.config.continuous){// see above comment + node.receive({payload: 'pythonshell@close'}) + } else { + node.receive(); + } + res.sendStatus(200); + } catch(err) { + res.sendStatus(500); + node.error(RED._("pythonshell.failed",{error:err.toString()})); + } + } else { + res.sendStatus(404); + } + }); + +} diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html new file mode 100644 index 000000000..35f46a295 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html @@ -0,0 +1,149 @@ +f + + + + + + + \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js new file mode 100644 index 000000000..edabfa19e --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js @@ -0,0 +1,66 @@ +/** + * Copyright 2014 Sense Tecnic Systems, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + **/ + +var util = require("util"); +var httpclient; +var PythonshellNode = require('./fl0'); + +module.exports = function(RED) { + "use strict"; + + function PythonshellInNode(n) { + RED.nodes.createNode(this,n); + + var node = this; + node.config = n; // copy config to the backend so that down bellow we can have a reference + + var pyNode = new PythonshellNode(n); + + pyNode.setStatusCallback(node.status.bind(node)) + + node.on("input",function(msg) { + pyNode.onInput(msg, function(result){ + node.send(result); + }, function(err){ + node.error(err); + }); + }); + + node.on('close', ()=>pyNode.onClose()); + } + + RED.nodes.registerType("fl", PythonshellInNode); + + RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { + var node = RED.nodes.getNode(req.params.id); + if (node != null) { + try { + if (node.config.continuous){// see above comment + node.receive({payload: 'pythonshell@close'}) + } else { + node.receive(); + } + res.sendStatus(200); + } catch(err) { + res.sendStatus(500); + node.error(RED._("pythonshell.failed",{error:err.toString()})); + } + } else { + res.sendStatus(404); + } + }); + +} diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js new file mode 100644 index 000000000..ee8b3f222 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js @@ -0,0 +1,147 @@ +var fs = require("fs"); + +function PythonshellInNode(config) { + if (!config.pyfile){ + throw 'pyfile not present'; + } + this.pythonExec = config.python3 ? "python3" : "python"; + this.pyfile = "/data/1.connect-kubeflow/py/fl.py"; + this.virtualenv = config.virtualenv; + + if (!fs.existsSync(this.pyfile)) { + throw 'pyfile not exist'; + } + + if (this.virtualenv && !fs.existsSync(this.virtualenv)){ + throw 'configured virtualenv not exist, consider remove or change'; + } + + this.stdInData = config.stdInData; + this.continuous = this.stdInData ? true : config.continuous; + this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); + this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); + this.spawn = require('child_process').spawn; + this.onStatus = ()=>{} +} + +PythonshellInNode.prototype.onInput = function(msg, out, err) { + payload = msg.payload || ''; + if (typeof payload === 'object'){ + payload = JSON.stringify(payload); + } else if (typeof payload !== 'string'){ + payload = payload.toString(); + } + + if (payload === 'pythonshell@close'){ + if (this.py != null){ + this.onClose() + return + } else { + // trigger new execution + payload = '' + } + } + + if (this.continuous && !this.stdInData && this.py != null){ + this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) + return + } + + var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec + + if (this.stdInData){ + if (!this.py){ + this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { + cwd: this.pydir, + detached: true + }); + this.firstExecution = true + } else { + this.firstExecution = false + } + } else { + this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { + cwd: this.pydir + }); + } + + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + + // subsequence message, no need to setup callbacks + if (this.stdInData && !this.firstExecution){ + this.py.stdin.write(payload + '\n') + return + } + + var py = this.py; + var dataString = ''; + var errString = ''; + + py.stdout.on('data', data => { + clearTimeout(this.standbyTimer) + + this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) + + let dataStr = data.toString(); + + dataString += dataStr; + + if (dataString.endsWith("\n")){ + if (this.continuous){ + msg.payload = dataString; + out(msg); + dataString = '' + } + } + + this.standbyTimer = setTimeout(()=>{ + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + }, 2000) + + }); + + py.stderr.on('data', data => { + errString += String(data);// just a different way to do it + this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) + }); + + py.stderr.on('error', console.log) + py.stdout.on('error', console.log) + py.stdin.on('error', console.log) + py.on('error', console.log) + + py.on('close', code =>{ + if (code){ + err('exit code: ' + code + ', ' + errString); + this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) + } else if (!this.continuous){ + msg.payload = dataString.trim(); + out(msg); + this.onStatus({fill:"green",shape:"dot",text:"Done"}) + } else { + this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) + } + this.py = null + setTimeout(()=>{ + this.onStatus({}) + }, 2000) + }); + + if (this.stdInData){ + py.stdin.write(payload + '\n') + } +}; + +PythonshellInNode.prototype.onClose = function() { + if (this.py){ + this.py.kill() + this.py = null + } +}; + +PythonshellInNode.prototype.setStatusCallback = function(callback) { + this.onStatus = callback +}; + + +module.exports = PythonshellInNode \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html new file mode 100644 index 000000000..322e0e99d --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html @@ -0,0 +1,149 @@ +f + + + + + + + \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js new file mode 100644 index 000000000..3c3e04c13 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js @@ -0,0 +1,66 @@ +/** + * Copyright 2014 Sense Tecnic Systems, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + **/ + +var util = require("util"); +var httpclient; +var PythonshellNode = require('./mnist0'); + +module.exports = function(RED) { + "use strict"; + + function PythonshellInNode(n) { + RED.nodes.createNode(this,n); + + var node = this; + node.config = n; // copy config to the backend so that down bellow we can have a reference + + var pyNode = new PythonshellNode(n); + + pyNode.setStatusCallback(node.status.bind(node)) + + node.on("input",function(msg) { + pyNode.onInput(msg, function(result){ + node.send(result); + }, function(err){ + node.error(err); + }); + }); + + node.on('close', ()=>pyNode.onClose()); + } + + RED.nodes.registerType("mnist", PythonshellInNode); + + RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { + var node = RED.nodes.getNode(req.params.id); + if (node != null) { + try { + if (node.config.continuous){// see above comment + node.receive({payload: 'pythonshell@close'}) + } else { + node.receive(); + } + res.sendStatus(200); + } catch(err) { + res.sendStatus(500); + node.error(RED._("pythonshell.failed",{error:err.toString()})); + } + } else { + res.sendStatus(404); + } + }); + +} diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js new file mode 100644 index 000000000..58ebc34a3 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js @@ -0,0 +1,147 @@ +var fs = require("fs"); + +function PythonshellInNode(config) { + if (!config.pyfile){ + throw 'pyfile not present'; + } + this.pythonExec = config.python3 ? "python3" : "python"; + this.pyfile = "/data/1.connect-kubeflow/py/mnist.py"; + this.virtualenv = config.virtualenv; + + if (!fs.existsSync(this.pyfile)) { + throw 'pyfile not exist'; + } + + if (this.virtualenv && !fs.existsSync(this.virtualenv)){ + throw 'configured virtualenv not exist, consider remove or change'; + } + + this.stdInData = config.stdInData; + this.continuous = this.stdInData ? true : config.continuous; + this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); + this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); + this.spawn = require('child_process').spawn; + this.onStatus = ()=>{} +} + +PythonshellInNode.prototype.onInput = function(msg, out, err) { + payload = msg.payload || ''; + if (typeof payload === 'object'){ + payload = JSON.stringify(payload); + } else if (typeof payload !== 'string'){ + payload = payload.toString(); + } + + if (payload === 'pythonshell@close'){ + if (this.py != null){ + this.onClose() + return + } else { + // trigger new execution + payload = '' + } + } + + if (this.continuous && !this.stdInData && this.py != null){ + this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) + return + } + + var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec + + if (this.stdInData){ + if (!this.py){ + this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { + cwd: this.pydir, + detached: true + }); + this.firstExecution = true + } else { + this.firstExecution = false + } + } else { + this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { + cwd: this.pydir + }); + } + + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + + // subsequence message, no need to setup callbacks + if (this.stdInData && !this.firstExecution){ + this.py.stdin.write(payload + '\n') + return + } + + var py = this.py; + var dataString = ''; + var errString = ''; + + py.stdout.on('data', data => { + clearTimeout(this.standbyTimer) + + this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) + + let dataStr = data.toString(); + + dataString += dataStr; + + if (dataString.endsWith("\n")){ + if (this.continuous){ + msg.payload = dataString; + out(msg); + dataString = '' + } + } + + this.standbyTimer = setTimeout(()=>{ + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + }, 2000) + + }); + + py.stderr.on('data', data => { + errString += String(data);// just a different way to do it + this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) + }); + + py.stderr.on('error', console.log) + py.stdout.on('error', console.log) + py.stdin.on('error', console.log) + py.on('error', console.log) + + py.on('close', code =>{ + if (code){ + err('exit code: ' + code + ', ' + errString); + this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) + } else if (!this.continuous){ + msg.payload = dataString.trim(); + out(msg); + this.onStatus({fill:"green",shape:"dot",text:"Done"}) + } else { + this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) + } + this.py = null + setTimeout(()=>{ + this.onStatus({}) + }, 2000) + }); + + if (this.stdInData){ + py.stdin.write(payload + '\n') + } +}; + +PythonshellInNode.prototype.onClose = function() { + if (this.py){ + this.py.kill() + this.py = null + } +}; + +PythonshellInNode.prototype.setStatusCallback = function(callback) { + this.onStatus = callback +}; + + +module.exports = PythonshellInNode \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json new file mode 100644 index 000000000..fcf46cd21 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json @@ -0,0 +1,30 @@ +{ + "name": "node-red-contrib-pythonshell-custom", + "version": "1.0.0", + "description": "modified version of node-red-contrib-pythonshell with additional features", + "scripts": { + "test": "./node_modules/mocha/bin/mocha" + }, + "keywords": [ + "distributed", + "python", + "node", + "node-red" + ], + "author": "WuChunYen", + "license": "ISC", + "dependencies": {}, + "node-red": { + "nodes": { + "decisionTree": "decisiontree.js", + "randomForest": "randomforest.js", + "logisticRegression": "logisticregression.js", + "fl": "fl.js", + "mnist": "mnist.js", + "LSTM": "LSTM.js" + } + }, + "devDependencies": { + "mocha": "^5.0.4" + } +} diff --git a/kubeflow/examples/1.connect-kubeflow/package.json b/kubeflow/examples/1.connect-kubeflow/package.json new file mode 100644 index 000000000..b788fd491 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/package.json @@ -0,0 +1,6 @@ +{ + "name": "node-red-project", + "description": "A Node-RED Project", + "version": "0.0.1", + "private": true +} \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/LSTM.py b/kubeflow/examples/1.connect-kubeflow/py/LSTM.py new file mode 100644 index 000000000..0eb2cb76a --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/LSTM.py @@ -0,0 +1,132 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "https://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for LSTM." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/LSTM_pipeline.yaml' + name='pipeline-' + random_suffix() + description="This is a LSTM pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'LSTM' + random_suffix() + description = "This is a LSTM run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'random-forest-classifier-Accuracy': #change parameter + output_value = parameter['value'] + + if output_value is not None: + print(f"Random Forest Classifier Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) diff --git a/kubeflow/examples/1.connect-kubeflow/py/README.md b/kubeflow/examples/1.connect-kubeflow/py/README.md new file mode 100644 index 000000000..06bb167e4 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/README.md @@ -0,0 +1,18 @@ +## How to use +``` +KUBEFLOW_HOST= \ +KUBEFLOW_USERNAME= \ +KUBEFLOW_PASSWORD= \ +python3 +``` +## three pipelines +流程架構: +create_experiment -> upload_pipeline -> create_run -> get_run -> filter -> result +### decisionTree.py +功能：以乳癌資料集訓練decisionTree模型並回傳準確率 + +### logisticRegression.py 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__future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="" # str | The ID of the name to be create. + description='' # str | The description experiment. + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + api_response = api_instance.create_experiment(body) + pprint(api_response) + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py new file mode 100644 index 000000000..b02691de7 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py @@ -0,0 +1,58 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from pprint import pprint +from kfp_server_api.rest import ApiException +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.RunServiceApi(api_client) + pipeline_id = '' # str | The ID of the pipeline. + experiment_id = '' # str | The ID of the experiment. + display_name = '' # str | The name of the run to be create. + description = '' # str | The description of run. + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + + try: + # Creates a new run. + api_response = api_instance.create_run(body) + pprint(api_response) + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py new file mode 100644 index 000000000..31d9205c7 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py @@ -0,0 +1,51 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.ExperimentServiceApi(api_client) + experiment_id = '' # str | The ID of the experiment to be deleted. + + try: + # Deletes an experiment without deleting the experiment's runs and recurring runs. + # To avoid unexpected behaviors, delete an experiment's runs and recurring runs before deleting the experiment. + api_response = api_instance.delete_experiment(experiment_id) + pprint(api_response) + except ApiException as e: + print("Exception when calling ExperimentServiceApi->delete_experiment: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py new file mode 100644 index 000000000..c04d595ef --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py @@ -0,0 +1,50 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.PipelineServiceApi(api_client) + id = '' # str | The ID of the pipeline to be deleted. + + try: + # Deletes a pipeline and its pipeline versions. + api_response = api_instance.delete_pipeline(id) + pprint(api_response) + except ApiException as e: + print("Exception when calling PipelineServiceApi->delete_pipeline: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py new file mode 100644 index 000000000..aefc7dca3 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py @@ -0,0 +1,50 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.RunServiceApi(api_client) + id = '' # str | The ID of the run to be deleted. + + try: + # Deletes a run. + api_response = api_instance.delete_run(id) + pprint(api_response) + except ApiException as e: + print("Exception when calling RunServiceApi->delete_run: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py new file mode 100644 index 000000000..f62c56bdc --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py @@ -0,0 +1,64 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces +import json + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.RunServiceApi(api_client) + run_id = '' # str | The ID of the run to be retrieved. + + try: + # Finds a specific run by ID. + api_response = api_instance.get_run(run_id) + output = api_response.pipeline_runtime.workflow_manifest + output = json.loads(output) + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + output_value = None + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'decision-tree-classifier-Accuracy': + output_value = parameter['value'] + break + + if output_value is not None: + print(f"Decision Tree Classifier Accuracy: {output_value}") + else: + print("Parameter not found.") + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py new file mode 100644 index 000000000..7c95abcf0 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py @@ -0,0 +1,93 @@ +import re +from urllib.parse import urlsplit + +import requests + +# NOTE: the following code is referred from https://github.com/kubeflow/website/issues/2916 +def get_istio_auth_session(url: str, username: str, password: str) -> dict: + """ + Determine if the specified URL is secured by Dex and try to obtain a session cookie. + WARNING: only Dex `staticPasswords` and `LDAP` authentication are currently supported + (we default default to using `staticPasswords` if both are enabled) + + :param url: Kubeflow server URL, including protocol + :param username: Dex `staticPasswords` or `LDAP` username + :param password: Dex `staticPasswords` or `LDAP` password + :return: auth session information + """ + # define the default return object + auth_session = { + "endpoint_url": url, # KF endpoint URL + "redirect_url": None, # KF redirect URL, if applicable + "dex_login_url": None, # Dex login URL (for POST of credentials) + "is_secured": None, # True if KF endpoint is secured + "session_cookie": None # Resulting session cookies in the form "key1=value1; key2=value2" + } + + # use a persistent session (for cookies) + with requests.Session() as s: + + ################ + # Determine if Endpoint is Secured + ################ + resp = s.get(url, allow_redirects=True) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {url}" + ) + + auth_session["redirect_url"] = resp.url + + # if we were NOT redirected, then the endpoint is UNSECURED + if len(resp.history) == 0: + auth_session["is_secured"] = False + return auth_session + else: + auth_session["is_secured"] = True + + ################ + # Get Dex Login URL + ################ + redirect_url_obj = urlsplit(auth_session["redirect_url"]) + + # if we are at `/auth?=xxxx` path, we need to select an auth type + if re.search(r"/auth$", redirect_url_obj.path): + # default to "staticPasswords" auth type + redirect_url_obj = redirect_url_obj._replace( + path=re.sub(r"/auth$", "/auth/local", redirect_url_obj.path) + ) + + # if we are at `/auth/xxxx/login` path, then no further action is needed (we can use it for login POST) + if re.search(r"/auth/.*/login$", redirect_url_obj.path): + auth_session["dex_login_url"] = redirect_url_obj.geturl() + + # else, we need to be redirected to the actual login page + else: + # this GET should redirect us to the `/auth/xxxx/login` path + resp = s.get(redirect_url_obj.geturl(), allow_redirects=True) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {redirect_url_obj.geturl()}" + ) + + # set the login url + auth_session["dex_login_url"] = resp.url + + ################ + # Attempt Dex Login + ################ + resp = s.post( + auth_session["dex_login_url"], + data={"login": username, "password": password}, + allow_redirects=True + ) + if len(resp.history) == 0: + raise RuntimeError( + f"Login credentials were probably invalid - " + f"No redirect after POST to: {auth_session['dex_login_url']}" + ) + + # store the session cookies in a "key1=value1; key2=value2" string + auth_session["session_cookie"] = "; ".join([f"{c.name}={c.value}" for c in s.cookies]) + + return auth_session \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py new file mode 100644 index 000000000..3c7c0dfdc --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py @@ -0,0 +1,19 @@ +import os +import requests + +from typing import TypedDict + +def retrieve_namespaces(host: str, auth_session: TypedDict) -> str: + workgroup_endpoint = os.path.join(host, "api/workgroup/env-info") + + cookies = {} + cookie_tokens = auth_session["session_cookie"].split("=") + print(cookie_tokens[0]) + cookies[cookie_tokens[0]]=cookie_tokens[1] + resp = requests.get(workgroup_endpoint, cookies=cookies) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {workgroup_endpoint}" + ) + return [ns["namespace"] for ns in resp.json()["namespaces"] if + ns["role"]=="owner"] \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py new file mode 100644 index 000000000..2e4729bea --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py @@ -0,0 +1,44 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.ExperimentServiceApi(api_client) + resource_reference_key_type = "NAMESPACE" + resource_reference_key_id = namespaces[0] + list_experiment_response = api_instance.list_experiment(resource_reference_key_type=resource_reference_key_type, resource_reference_key_id=resource_reference_key_id) + for experiment in list_experiment_response.experiments: + pprint(experiment) diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py new file mode 100644 index 000000000..6e2be4b5e --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py @@ -0,0 +1,43 @@ +from __future__ import print_function +import string +import time +import kfp_server_api +import os +import requests +import kfp +import json +from pprint import pprint +from kfp_server_api.rest import ApiException +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + api_instance = kfp_server_api.PipelineServiceApi(api_client) + list_pipeline_response = api_instance.list_pipelines() + for pipelines in list_pipeline_response.pipelines: + print(pipelines) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py new file mode 100644 index 000000000..410cf50aa --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py @@ -0,0 +1,52 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +from kfp_server_api.rest import ApiException + +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='' # The yaml file in your local path. + name='pipeline-' + random_suffix() + description='' # str | The description of pipeline. + try: + api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + print(api_response) + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py b/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py new file mode 100644 index 000000000..16293f1d6 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py @@ -0,0 +1,132 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "http://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for only_decision_tree." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/only_decision_tree.yaml' + name='pipeline-' + random_suffix() + description="This is a only_decision_tree pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'run_only_decision_tree' + random_suffix() + description = "This is a only_decision_tree run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'decision-tree-classifier-Accuracy': + output_value = parameter['value'] + + if output_value is not None: + print(f"Decision Tree Classifier Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) diff --git a/kubeflow/examples/1.connect-kubeflow/py/fl.py b/kubeflow/examples/1.connect-kubeflow/py/fl.py new file mode 100644 index 000000000..6aeb83f9d --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/fl.py @@ -0,0 +1,132 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "https://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for fl." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/fl_pipeline.yaml' + name='pipeline-' + random_suffix() + description="This is a fl pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'fl' + random_suffix() + description = "This is a fl run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'client-last_accuracy': + output_value = parameter['value'] + + if output_value is not None: + print(f"fl Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) diff --git a/kubeflow/examples/1.connect-kubeflow/py/helloworld.py b/kubeflow/examples/1.connect-kubeflow/py/helloworld.py new file mode 100644 index 000000000..97e823fb5 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/helloworld.py @@ -0,0 +1,6 @@ +import sys + +while True: + line = sys.stdin.readline() + print('this is send from python') + print(line) diff --git a/kubeflow/examples/1.connect-kubeflow/py/install.py b/kubeflow/examples/1.connect-kubeflow/py/install.py new file mode 100644 index 000000000..470d38321 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/install.py @@ -0,0 +1,23 @@ +import os +import subprocess + +def python3_version(): + return subprocess.check_call(["python3", "--version"]) + +def which(command): + return subprocess.check_call(["which", command]) + +def pip3_install_requirements(): + return subprocess.check_call(["pip3", "install", "-r", "requirements.txt", + "--user"]) + +def pip3_install_kfp(): + return subprocess.check_call(["pip3", "install", + "git+https://github.com/kubeflow/pipelines.git@1.8.19#subdirectory=backend/api/python_http_client", + "--user"]) + +python3_version() +pip3_install_requirements() +pip3_install_kfp() + +print("done") diff --git a/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py b/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py new file mode 100644 index 000000000..da7ef822f --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py @@ -0,0 +1,44 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +from kfp_server_api.rest import ApiException + +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "http://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.ExperimentServiceApi(api_client) + resource_reference_key_type = "NAMESPACE" + resource_reference_key_id = namespaces[0] + list_experiment_response = api_instance.list_experiment(resource_reference_key_type=resource_reference_key_type, resource_reference_key_id=resource_reference_key_id) + for experiment in list_experiment_response.experiments: + print(experiment) diff --git a/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py b/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py new file mode 100644 index 000000000..7c95abcf0 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py @@ -0,0 +1,93 @@ +import re +from urllib.parse import urlsplit + +import requests + +# NOTE: the following code is referred from https://github.com/kubeflow/website/issues/2916 +def get_istio_auth_session(url: str, username: str, password: str) -> dict: + """ + Determine if the specified URL is secured by Dex and try to obtain a session cookie. + WARNING: only Dex `staticPasswords` and `LDAP` authentication are currently supported + (we default default to using `staticPasswords` if both are enabled) + + :param url: Kubeflow server URL, including protocol + :param username: Dex `staticPasswords` or `LDAP` username + :param password: Dex `staticPasswords` or `LDAP` password + :return: auth session information + """ + # define the default return object + auth_session = { + "endpoint_url": url, # KF endpoint URL + "redirect_url": None, # KF redirect URL, if applicable + "dex_login_url": None, # Dex login URL (for POST of credentials) + "is_secured": None, # True if KF endpoint is secured + "session_cookie": None # Resulting session cookies in the form "key1=value1; key2=value2" + } + + # use a persistent session (for cookies) + with requests.Session() as s: + + ################ + # Determine if Endpoint is Secured + ################ + resp = s.get(url, allow_redirects=True) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {url}" + ) + + auth_session["redirect_url"] = resp.url + + # if we were NOT redirected, then the endpoint is UNSECURED + if len(resp.history) == 0: + auth_session["is_secured"] = False + return auth_session + else: + auth_session["is_secured"] = True + + ################ + # Get Dex Login URL + ################ + redirect_url_obj = urlsplit(auth_session["redirect_url"]) + + # if we are at `/auth?=xxxx` path, we need to select an auth type + if re.search(r"/auth$", redirect_url_obj.path): + # default to "staticPasswords" auth type + redirect_url_obj = redirect_url_obj._replace( + path=re.sub(r"/auth$", "/auth/local", redirect_url_obj.path) + ) + + # if we are at `/auth/xxxx/login` path, then no further action is needed (we can use it for login POST) + if re.search(r"/auth/.*/login$", redirect_url_obj.path): + auth_session["dex_login_url"] = redirect_url_obj.geturl() + + # else, we need to be redirected to the actual login page + else: + # this GET should redirect us to the `/auth/xxxx/login` path + resp = s.get(redirect_url_obj.geturl(), allow_redirects=True) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {redirect_url_obj.geturl()}" + ) + + # set the login url + auth_session["dex_login_url"] = resp.url + + ################ + # Attempt Dex Login + ################ + resp = s.post( + auth_session["dex_login_url"], + data={"login": username, "password": password}, + allow_redirects=True + ) + if len(resp.history) == 0: + raise RuntimeError( + f"Login credentials were probably invalid - " + f"No redirect after POST to: {auth_session['dex_login_url']}" + ) + + # store the session cookies in a "key1=value1; key2=value2" string + auth_session["session_cookie"] = "; ".join([f"{c.name}={c.value}" for c in s.cookies]) + + return auth_session \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py b/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py new file mode 100644 index 000000000..3c7c0dfdc --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py @@ -0,0 +1,19 @@ +import os +import requests + +from typing import TypedDict + +def retrieve_namespaces(host: str, auth_session: TypedDict) -> str: + workgroup_endpoint = os.path.join(host, "api/workgroup/env-info") + + cookies = {} + cookie_tokens = auth_session["session_cookie"].split("=") + print(cookie_tokens[0]) + cookies[cookie_tokens[0]]=cookie_tokens[1] + resp = requests.get(workgroup_endpoint, cookies=cookies) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {workgroup_endpoint}" + ) + return [ns["namespace"] for ns in resp.json()["namespaces"] if + ns["role"]=="owner"] \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/mnist.py b/kubeflow/examples/1.connect-kubeflow/py/mnist.py new file mode 100644 index 000000000..fc2911a05 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/mnist.py @@ -0,0 +1,132 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "https://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for mnist." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/mnist_pipeline.yaml' + name='pipeline-' + random_suffix() + description="This is a mnist pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'mnist' + random_suffix() + description = "This is a mnist run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'client-last_accuracy': + output_value = parameter['value'] + + if output_value is not None: + print(f"fl Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml new file mode 100644 index 000000000..11f53462f --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml @@ -0,0 +1,651 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: fl-test- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2024-01-02T05:53:41.650368', + pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "kubeflow-user-thu01", + "name": "namespace", "optional": true}], "name": "FL test"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: fl-test + templates: + - name: client + container: + args: [--batch, '1', '----output-paths', /tmp/outputs/last_accuracy/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def client(batch):\n import json\n import requests\n import time\n\ + \ import pandas as pd\n import numpy as np\n import tensorflow as\ + \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ + \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ + \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ + \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ + \ tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import\ + \ Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras\ + \ import backend as K\n\n normal_url='https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'\ + \ \n abnormal_url='https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download'\n\ + \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ + \ num_features = len(normal_data.columns)\n print(num_features)\n \ + \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ + \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ + \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ + \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ + \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ + \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ + \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ + \n input_shape = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n\ + \ @staticmethod\n def build(shape, classes):\n model\ + \ = Sequential()\n model.add(LSTM(units=64, input_shape=input_shape,\ + \ return_sequences=True))\n model.add(Dropout(0.2))\n \ + \ model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n \ + \ model.add(Dense(units=num_classes, activation='softmax'))\n\n \ + \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ + \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ + \ #The client should have its own data, not like this. It's a lazy method.\n\ + \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ + \n #seperate shard into data and labels lists\n data, label\ + \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ + \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ + \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ + \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ + \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ + \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ + \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ + \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ + \ \n optimizer=optimizer, \n metrics=metrics)\n\ + \n if(comm_round == 0):\n history = client_model.fit(dataset,\ + \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ + \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ + \ test_loss, test_accuracy = client_model.evaluate(dataset)\n \ + \ print(f'Test accuracy: {test_accuracy}')\n\n local_weight = client_model.get_weights()\n\ + \ local_weight = [np.array(w).tolist() for w in local_weight]\n\n \ + \ client_data = {\"local_count\": local_count,'bs': bs, 'local_weight':\ + \ json.dumps(local_weight)}\n\n while True:\n try:\n \ + \ weight = (requests.post(server_url,data=client_data))\n\n \ + \ if weight.status_code == 200:\n print(f\"\ + exist\")\n\n break\n else:\n \ + \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ + \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ + \ data = weight.json()\n avg_weight = data.get('result')\n \ + \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ + \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ + \ \n try:\n response = requests.get(shutdown_url)\n except\ + \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ + \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ + \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ + \ str:\n if isinstance(float_value, str):\n return float_value\n\ + \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ + \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ + \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ + _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ + --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ + _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ + \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ + _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ + \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + outputs: + parameters: + - name: client-last_accuracy + valueFrom: {path: /tmp/outputs/last_accuracy/data} + artifacts: + - {name: client-last_accuracy, path: /tmp/outputs/last_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": + "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' + || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def client(batch):\n import json\n import requests\n import time\n import + pandas as pd\n import numpy as np\n import tensorflow as tf\n from + tensorflow.keras.models import Sequential\n from tensorflow.keras.layers + import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from + tensorflow.keras.layers import Activation\n from tensorflow.keras.layers + import Flatten\n from tensorflow.keras.layers import Dense\n from + tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import + Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras + import backend as K\n\n normal_url=''https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'' + \n abnormal_url=''https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download''\n normal_data + = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features + = len(normal_data.columns)\n print(num_features)\n normal_label = + np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, + 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label + = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data + = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), + num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data + = list(zip(data, data_label))\n data_length=len(full_data)\n\n input_shape + = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n @staticmethod\n def + build(shape, classes):\n model = Sequential()\n model.add(LSTM(units=64, + input_shape=input_shape, return_sequences=True))\n model.add(Dropout(0.2))\n model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n model.add(Dense(units=num_classes, + activation=''softmax''))\n\n return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] + #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] + #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data + len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate + shard into data and labels lists\n data, label = zip(*data_shard)\n dataset + = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return + dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs + = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics + = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for + comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model + = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, + \n metrics=metrics)\n\n if(comm_round == 0):\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n\n test_loss, test_accuracy + = client_model.evaluate(dataset)\n print(f''Test accuracy: {test_accuracy}'')\n\n local_weight + = client_model.get_weights()\n local_weight = [np.array(w).tolist() + for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': + bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight + = (requests.post(server_url,data=client_data))\n\n if weight.status_code + == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server + error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not + exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight + = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight + = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response + = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already + shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef + _serialize_float(float_value: float) -> str:\n if isinstance(float_value, + str):\n return float_value\n if not isinstance(float_value, (float, + int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + float.''.format(\n str(float_value), str(type(float_value))))\n return + str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', + description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, + required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": + "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": + "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": + "1"}'} + - name: client-2 + container: + args: [--batch, '2', '----output-paths', /tmp/outputs/last_accuracy/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def client(batch):\n import json\n import requests\n import time\n\ + \ import pandas as pd\n import numpy as np\n import tensorflow as\ + \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ + \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ + \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ + \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ + \ tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import\ + \ Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras\ + \ import backend as K\n\n normal_url='https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'\ + \ \n abnormal_url='https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download'\n\ + \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ + \ num_features = len(normal_data.columns)\n print(num_features)\n \ + \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ + \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ + \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ + \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ + \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ + \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ + \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ + \n input_shape = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n\ + \ @staticmethod\n def build(shape, classes):\n model\ + \ = Sequential()\n model.add(LSTM(units=64, input_shape=input_shape,\ + \ return_sequences=True))\n model.add(Dropout(0.2))\n \ + \ model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n \ + \ model.add(Dense(units=num_classes, activation='softmax'))\n\n \ + \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ + \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ + \ #The client should have its own data, not like this. It's a lazy method.\n\ + \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ + \n #seperate shard into data and labels lists\n data, label\ + \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ + \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ + \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ + \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ + \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ + \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ + \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ + \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ + \ \n optimizer=optimizer, \n metrics=metrics)\n\ + \n if(comm_round == 0):\n history = client_model.fit(dataset,\ + \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ + \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ + \ test_loss, test_accuracy = client_model.evaluate(dataset)\n \ + \ print(f'Test accuracy: {test_accuracy}')\n\n local_weight = client_model.get_weights()\n\ + \ local_weight = [np.array(w).tolist() for w in local_weight]\n\n \ + \ client_data = {\"local_count\": local_count,'bs': bs, 'local_weight':\ + \ json.dumps(local_weight)}\n\n while True:\n try:\n \ + \ weight = (requests.post(server_url,data=client_data))\n\n \ + \ if weight.status_code == 200:\n print(f\"\ + exist\")\n\n break\n else:\n \ + \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ + \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ + \ data = weight.json()\n avg_weight = data.get('result')\n \ + \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ + \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ + \ \n try:\n response = requests.get(shutdown_url)\n except\ + \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ + \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ + \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ + \ str:\n if isinstance(float_value, str):\n return float_value\n\ + \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ + \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ + \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ + _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ + --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ + _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ + \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ + _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ + \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + outputs: + artifacts: + - {name: client-2-last_accuracy, path: /tmp/outputs/last_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": + "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' + || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def client(batch):\n import json\n import requests\n import time\n import + pandas as pd\n import numpy as np\n import tensorflow as tf\n from + tensorflow.keras.models import Sequential\n from tensorflow.keras.layers + import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from + tensorflow.keras.layers import Activation\n from tensorflow.keras.layers + import Flatten\n from tensorflow.keras.layers import Dense\n from + tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import + Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras + import backend as K\n\n normal_url=''https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'' + \n abnormal_url=''https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download''\n normal_data + = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features + = len(normal_data.columns)\n print(num_features)\n normal_label = + np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, + 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label + = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data + = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), + num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data + = list(zip(data, data_label))\n data_length=len(full_data)\n\n input_shape + = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n @staticmethod\n def + build(shape, classes):\n model = Sequential()\n model.add(LSTM(units=64, + input_shape=input_shape, return_sequences=True))\n model.add(Dropout(0.2))\n model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n model.add(Dense(units=num_classes, + activation=''softmax''))\n\n return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] + #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] + #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data + len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate + shard into data and labels lists\n data, label = zip(*data_shard)\n dataset + = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return + dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs + = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics + = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for + comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model + = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, + \n metrics=metrics)\n\n if(comm_round == 0):\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n\n test_loss, test_accuracy + = client_model.evaluate(dataset)\n print(f''Test accuracy: {test_accuracy}'')\n\n local_weight + = client_model.get_weights()\n local_weight = [np.array(w).tolist() + for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': + bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight + = (requests.post(server_url,data=client_data))\n\n if weight.status_code + == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server + error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not + exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight + = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight + = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response + = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already + shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef + _serialize_float(float_value: float) -> str:\n if isinstance(float_value, + str):\n return float_value\n if not isinstance(float_value, (float, + int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + float.''.format(\n str(float_value), str(type(float_value))))\n return + str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', + description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, + required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": + "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": + "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": + "2"}'} + - name: delete-service + resource: + action: delete + flags: [--wait=false] + manifest: | + apiVersion: v1 + kind: Service + metadata: + name: http-service + spec: + ports: + - port: 80 + protocol: TCP + targetPort: 8080 + selector: + app: http-service + type: NodePort + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: fl-test + dag: + tasks: + - {name: client, template: client} + - {name: client-2, template: client-2} + - name: delete-service + template: delete-service + dependencies: [server] + - {name: http-service, template: http-service} + - name: server + template: server + dependencies: [http-service] + - name: show-results + template: show-results + dependencies: [client] + arguments: + parameters: + - {name: client-last_accuracy, value: '{{tasks.client.outputs.parameters.client-last_accuracy}}'} + - name: http-service + resource: + action: create + manifest: | + apiVersion: v1 + kind: Service + metadata: + name: http-service + spec: + ports: + - port: 5000 + protocol: TCP + targetPort: 8080 + selector: + app: http-service + outputs: + parameters: + - name: http-service-manifest + valueFrom: {jsonPath: '{}'} + - name: http-service-name + valueFrom: {jsonPath: '{.metadata.name}'} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: server + container: + args: [] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'flask' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'flask' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def server():\n import json\n import pandas as pd\n import numpy\ + \ as np\n import pickle\n import threading\n import time\n import\ + \ tensorflow as tf\n from flask import Flask, jsonify,request\n import\ + \ os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list\ + \ = []\n global_value = { #Share variable\n 'last_run_statue'\ + \ : False, #last run finish or not\n 'data_statue' : None,\ + \ #global_count finish or not\n 'global_count' : None,\n\ + \ 'scale_statue' : None,\n 'weight_statue'\ + \ : None,\n 'average_weights' : None,\n \ + \ 'shutdown' : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n \ + \ init_lock = threading.Lock()\n clients_local_count_lock = threading.Lock()\n\ + \ scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock\ + \ = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n\ + \ def before_request():\n print('get request')\n\n @app.route('/data',\ + \ methods=['POST'])\n def flask_server():\n with init_lock: #check\ + \ last run is finish and init varible\n\n while True:\n\n \ + \ if(len(clients_local_count)==0 and global_value['last_run_statue']\ + \ == False):#init the variable by first client enter\n \ + \ global_value['last_run_statue'] = True\n global_value['data_statue']\ + \ = False\n global_value['scale_statue'] = False\n \ + \ global_value['weight_statue'] = False\n \ + \ break\n\n elif(global_value['last_run_statue'] == True):\n\ + \ break\n time.sleep(3)\n\n local_count\ + \ = int(request.form.get('local_count')) #get data\n bs =\ + \ int(request.form.get('bs'))\n local_weight = json.loads(request.form.get('local_weight'))\n\ + \ local_weight = [np.array(lst) for lst in local_weight]\n\n \ + \ def scale_model_weights(weight, scalar):\n weight_final = []\n\ + \ steps = len(weight)\n for i in range(steps):\n \ + \ weight_final.append(scalar * weight[i])\n return\ + \ weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n \ + \ avg_grad = list()\n #get the average grad accross all\ + \ client gradients\n for grad_list_tuple in zip(*scaled_weight_list):\n\ + \ layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)\n\ + \ avg_grad.append(layer_mean)\n\n return avg_grad\n\ + \n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\ + \n with scaled_local_weight_list_lock:\n while True:\n\n\ + \ if (len(clients_local_count) == NUM_OF_CLIENTS and global_value['data_statue']\ + \ != True):\n global_value['last_run_statue'] = False\n\ + \ sum_of_local_count=sum(clients_local_count)\n\n \ + \ global_value['global_count'] = sum_of_local_count \n\n\ + \ scaling_factor=local_count/global_value['global_count']\n\ + \ scaled_weights = scale_model_weights(local_weight, scaling_factor)\n\ + \ scaled_local_weight_list.append(scaled_weights)\n\n \ + \ global_value['scale_statue'] = True \n \ + \ global_value['data_statue'] = True\n break\n \ + \ elif (global_value['data_statue'] == True and global_value['scale_statue']\ + \ == True):\n scaling_factor=local_count/global_value['global_count']\n\ + \ scaled_weights =scale_model_weights(local_weight, scaling_factor)\n\ + \ scaled_local_weight_list.append(scaled_weights)\n\n \ + \ break\n time.sleep(1)\n\n with cal_weight_lock:\n\ + \n while True:\n if(len(scaled_local_weight_list)\ + \ == NUM_OF_CLIENTS and global_value['weight_statue'] != True):\n\n \ + \ global_value['average_weights'] = sum_scaled_weights(scaled_local_weight_list)\n\ + \ global_value['weight_statue'] = True\n \ + \ global_value['average_weights'] = json.dumps([np.array(w).tolist()\ + \ for w in global_value['average_weights']])\n\n break\n\ + \n elif(global_value['weight_statue'] == True):\n\n \ + \ break\n\n time.sleep(1)\n\n clients_local_count.clear()\n\ + \ scaled_local_weight_list.clear()\n\n return jsonify({'result':\ + \ (global_value['average_weights'])})\n\n @app.route('/shutdown', methods=['GET'])\n\ + \ def shutdown_server():\n global_value['shutdown'] +=1 \n \ + \ with shutdown_lock:\n while True:\n if(global_value['shutdown']\ + \ == NUM_OF_CLIENTS):\n os._exit(0)\n \ + \ return 'Server shutting down...'\n time.sleep(1)\n\n \ + \ app.run(host=\"0.0.0.0\", port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Server',\ + \ description='')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs =\ + \ server(**_parsed_args)\n" + image: tensorflow/tensorflow + ports: + - {containerPort: 8080, name: my-port} + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + metadata: + labels: + app: http-service + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": [], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 + -m pip install --quiet --no-warn-script-location ''flask'' ''pandas'' || + PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''flask'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def server():\n import json\n import pandas as pd\n import numpy + as np\n import pickle\n import threading\n import time\n import + tensorflow as tf\n from flask import Flask, jsonify,request\n import + os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list + = []\n global_value = { #Share variable\n ''last_run_statue'' + : False, #last run finish or not\n ''data_statue'' : + None, #global_count finish or not\n ''global_count'' + : None,\n ''scale_statue'' : None,\n ''weight_statue'' + : None,\n ''average_weights'' : None,\n ''shutdown'' + : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n init_lock = threading.Lock()\n clients_local_count_lock + = threading.Lock()\n scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock + = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n def + before_request():\n print(''get request'')\n\n @app.route(''/data'', + methods=[''POST''])\n def flask_server():\n with init_lock: #check + last run is finish and init varible\n\n while True:\n\n if(len(clients_local_count)==0 + and global_value[''last_run_statue''] == False):#init the variable by first + client enter\n global_value[''last_run_statue''] = True\n global_value[''data_statue''] + = False\n global_value[''scale_statue''] = False\n global_value[''weight_statue''] + = False\n break\n\n elif(global_value[''last_run_statue''] + == True):\n break\n time.sleep(3)\n\n local_count + = int(request.form.get(''local_count'')) #get data\n bs + = int(request.form.get(''bs''))\n local_weight = json.loads(request.form.get(''local_weight''))\n local_weight + = [np.array(lst) for lst in local_weight]\n\n def scale_model_weights(weight, + scalar):\n weight_final = []\n steps = len(weight)\n for + i in range(steps):\n weight_final.append(scalar * weight[i])\n return + weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n avg_grad + = list()\n #get the average grad accross all client gradients\n for + grad_list_tuple in zip(*scaled_weight_list):\n layer_mean + = tf.math.reduce_sum(grad_list_tuple, axis=0)\n avg_grad.append(layer_mean)\n\n return + avg_grad\n\n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\n with + scaled_local_weight_list_lock:\n while True:\n\n if + (len(clients_local_count) == NUM_OF_CLIENTS and global_value[''data_statue''] + != True):\n global_value[''last_run_statue''] = False\n sum_of_local_count=sum(clients_local_count)\n\n global_value[''global_count''] + = sum_of_local_count \n\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights + = scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n global_value[''scale_statue''] + = True \n global_value[''data_statue''] = True\n break\n elif + (global_value[''data_statue''] == True and global_value[''scale_statue''] + == True):\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights + =scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n break\n time.sleep(1)\n\n with + cal_weight_lock:\n\n while True:\n if(len(scaled_local_weight_list) + == NUM_OF_CLIENTS and global_value[''weight_statue''] != True):\n\n global_value[''average_weights''] + = sum_scaled_weights(scaled_local_weight_list)\n global_value[''weight_statue''] + = True\n global_value[''average_weights''] = json.dumps([np.array(w).tolist() + for w in global_value[''average_weights'']])\n\n break\n\n elif(global_value[''weight_statue''] + == True):\n\n break\n\n time.sleep(1)\n\n clients_local_count.clear()\n scaled_local_weight_list.clear()\n\n return + jsonify({''result'': (global_value[''average_weights''])})\n\n @app.route(''/shutdown'', + methods=[''GET''])\n def shutdown_server():\n global_value[''shutdown''] + +=1 \n with shutdown_lock:\n while True:\n if(global_value[''shutdown''] + == NUM_OF_CLIENTS):\n os._exit(0)\n return + ''Server shutting down...''\n time.sleep(1)\n\n app.run(host=\"0.0.0.0\", + port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Server'', + description='''')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs + = server(**_parsed_args)\n"], "image": "tensorflow/tensorflow"}}, "name": + "Server"}', pipelines.kubeflow.org/component_ref: '{}'} + - name: show-results + container: + args: [--test-acc, '{{inputs.parameters.client-last_accuracy}}', '----output-paths', + /tmp/outputs/test_accuracy/data] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def show_results(test_acc): + return([test_acc]) + + def _serialize_float(float_value: float) -> str: + if isinstance(float_value, str): + return float_value + if not isinstance(float_value, (float, int)): + raise TypeError('Value "{}" has type "{}" instead of float.'.format( + str(float_value), str(type(float_value)))) + return str(float_value) + + import argparse + _parser = argparse.ArgumentParser(prog='Show results', description='') + _parser.add_argument("--test-acc", dest="test_acc", type=float, required=True, default=argparse.SUPPRESS) + _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1) + _parsed_args = vars(_parser.parse_args()) + _output_files = _parsed_args.pop("_output_paths", []) + + _outputs = show_results(**_parsed_args) + + _output_serializers = [ + _serialize_float, + + ] + + import os + for idx, output_file in enumerate(_output_files): + try: + os.makedirs(os.path.dirname(output_file)) + except OSError: + pass + with open(output_file, 'w') as f: + f.write(_output_serializers[idx](_outputs[idx])) + image: python:3.7 + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + inputs: + parameters: + - {name: client-last_accuracy} + outputs: + artifacts: + - {name: show-results-test_accuracy, path: /tmp/outputs/test_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--test-acc", {"inputValue": "test_acc"}, "----output-paths", + {"outputPath": "test_accuracy"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def show_results(test_acc):\n return([test_acc])\n\ndef _serialize_float(float_value: + float) -> str:\n if isinstance(float_value, str):\n return float_value\n if + not isinstance(float_value, (float, int)):\n raise TypeError(''Value + \"{}\" has type \"{}\" instead of float.''.format(\n str(float_value), + str(type(float_value))))\n return str(float_value)\n\nimport argparse\n_parser + = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--test-acc\", + dest=\"test_acc\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = show_results(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "python:3.7"}}, "inputs": [{"name": "test_acc", "type": "Float"}], + "name": "Show results", "outputs": [{"name": "test_accuracy", "type": "Float"}]}', + pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"test_acc": + "{{inputs.parameters.client-last_accuracy}}"}'} + arguments: + parameters: + - {name: namespace, value: kubeflow-user-thu01} + serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml new file mode 100644 index 000000000..8381600f3 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml @@ -0,0 +1,639 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: fl-test- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-12-13T03:33:40.267552', + pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "kubeflow-user-thu01", + "name": "namespace", "optional": true}], "name": "FL test"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: fl-test + templates: + - name: client + container: + args: [--batch, '1', '----output-paths', /tmp/outputs/last_accuracy/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def client(batch):\n import json\n import requests\n import time\n\ + \ import pandas as pd\n import numpy as np\n import tensorflow as\ + \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ + \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ + \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ + \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ + \ tensorflow.keras.optimizers import SGD\n from tensorflow.keras import\ + \ backend as K\n\n normal_url='https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'\ + \ \n abnormal_url='https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy'\n\ + \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ + \ num_features = len(normal_data.columns)\n print(num_features)\n \ + \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ + \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ + \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ + \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ + \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ + \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ + \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ + \n class SimpleMLP:\n @staticmethod\n def build(shape, classes):\n\ + \ model = Sequential()\n model.add(Conv1D(filters=4,\ + \ kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n\ + \ model.add(Flatten())\n model.add(Dense(8, activation=\"\ + relu\"))\n model.add(Dense(2, activation = 'softmax'))\n\n \ + \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ + \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ + \ #The client should have its own data, not like this. It's a lazy method.\n\ + \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ + \n #seperate shard into data and labels lists\n data, label\ + \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ + \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ + \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ + \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ + \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ + \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ + \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ + \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ + \ \n optimizer=optimizer, \n metrics=metrics)\n\ + \n if(comm_round == 0):\n history = client_model.fit(dataset,\ + \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ + \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ + \ local_weight = client_model.get_weights()\n local_weight =\ + \ [np.array(w).tolist() for w in local_weight]\n\n client_data = {\"\ + local_count\": local_count,'bs': bs, 'local_weight': json.dumps(local_weight)}\n\ + \n while True:\n try:\n weight = (requests.post(server_url,data=client_data))\n\ + \n if weight.status_code == 200:\n print(f\"\ + exist\")\n\n break\n else:\n \ + \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ + \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ + \ data = weight.json()\n avg_weight = data.get('result')\n \ + \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ + \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ + \ \n try:\n response = requests.get(shutdown_url)\n except\ + \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ + \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ + \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ + \ str:\n if isinstance(float_value, str):\n return float_value\n\ + \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ + \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ + \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ + _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ + --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ + _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ + \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ + _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ + \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + outputs: + parameters: + - name: client-last_accuracy + valueFrom: {path: /tmp/outputs/last_accuracy/data} + artifacts: + - {name: client-last_accuracy, path: /tmp/outputs/last_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": + "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' + || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def client(batch):\n import json\n import requests\n import time\n import + pandas as pd\n import numpy as np\n import tensorflow as tf\n from + tensorflow.keras.models import Sequential\n from tensorflow.keras.layers + import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from + tensorflow.keras.layers import Activation\n from tensorflow.keras.layers + import Flatten\n from tensorflow.keras.layers import Dense\n from + tensorflow.keras.optimizers import SGD\n from tensorflow.keras import + backend as K\n\n normal_url=''https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'' + \n abnormal_url=''https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy''\n normal_data + = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features + = len(normal_data.columns)\n print(num_features)\n normal_label = + np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, + 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label + = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data + = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), + num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data + = list(zip(data, data_label))\n data_length=len(full_data)\n\n class + SimpleMLP:\n @staticmethod\n def build(shape, classes):\n model + = Sequential()\n model.add(Conv1D(filters=4, kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n model.add(Flatten())\n model.add(Dense(8, + activation=\"relu\"))\n model.add(Dense(2, activation = ''softmax''))\n\n return + model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] + #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] + #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data + len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate + shard into data and labels lists\n data, label = zip(*data_shard)\n dataset + = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return + dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs + = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics + = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for + comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model + = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, + \n metrics=metrics)\n\n if(comm_round == 0):\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n\n local_weight + = client_model.get_weights()\n local_weight = [np.array(w).tolist() + for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': + bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight + = (requests.post(server_url,data=client_data))\n\n if weight.status_code + == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server + error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not + exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight + = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight + = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response + = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already + shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef + _serialize_float(float_value: float) -> str:\n if isinstance(float_value, + str):\n return float_value\n if not isinstance(float_value, (float, + int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + float.''.format(\n str(float_value), str(type(float_value))))\n return + str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', + description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, + required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": + "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": + "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": + "1"}'} + - name: client-2 + container: + args: [--batch, '2', '----output-paths', /tmp/outputs/last_accuracy/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def client(batch):\n import json\n import requests\n import time\n\ + \ import pandas as pd\n import numpy as np\n import tensorflow as\ + \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ + \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ + \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ + \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ + \ tensorflow.keras.optimizers import SGD\n from tensorflow.keras import\ + \ backend as K\n\n normal_url='https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'\ + \ \n abnormal_url='https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy'\n\ + \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ + \ num_features = len(normal_data.columns)\n print(num_features)\n \ + \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ + \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ + \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ + \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ + \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ + \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ + \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ + \n class SimpleMLP:\n @staticmethod\n def build(shape, classes):\n\ + \ model = Sequential()\n model.add(Conv1D(filters=4,\ + \ kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n\ + \ model.add(Flatten())\n model.add(Dense(8, activation=\"\ + relu\"))\n model.add(Dense(2, activation = 'softmax'))\n\n \ + \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ + \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ + \ #The client should have its own data, not like this. It's a lazy method.\n\ + \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ + \n #seperate shard into data and labels lists\n data, label\ + \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ + \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ + \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ + \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ + \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ + \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ + \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ + \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ + \ \n optimizer=optimizer, \n metrics=metrics)\n\ + \n if(comm_round == 0):\n history = client_model.fit(dataset,\ + \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ + \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ + \ local_weight = client_model.get_weights()\n local_weight =\ + \ [np.array(w).tolist() for w in local_weight]\n\n client_data = {\"\ + local_count\": local_count,'bs': bs, 'local_weight': json.dumps(local_weight)}\n\ + \n while True:\n try:\n weight = (requests.post(server_url,data=client_data))\n\ + \n if weight.status_code == 200:\n print(f\"\ + exist\")\n\n break\n else:\n \ + \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ + \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ + \ data = weight.json()\n avg_weight = data.get('result')\n \ + \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ + \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ + \ \n try:\n response = requests.get(shutdown_url)\n except\ + \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ + \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ + \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ + \ str:\n if isinstance(float_value, str):\n return float_value\n\ + \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ + \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ + \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ + _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ + --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ + _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ + \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ + _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ + \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + outputs: + artifacts: + - {name: client-2-last_accuracy, path: /tmp/outputs/last_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": + "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' + || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def client(batch):\n import json\n import requests\n import time\n import + pandas as pd\n import numpy as np\n import tensorflow as tf\n from + tensorflow.keras.models import Sequential\n from tensorflow.keras.layers + import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from + tensorflow.keras.layers import Activation\n from tensorflow.keras.layers + import Flatten\n from tensorflow.keras.layers import Dense\n from + tensorflow.keras.optimizers import SGD\n from tensorflow.keras import + backend as K\n\n normal_url=''https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'' + \n abnormal_url=''https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy''\n normal_data + = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features + = len(normal_data.columns)\n print(num_features)\n normal_label = + np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, + 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label + = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data + = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), + num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data + = list(zip(data, data_label))\n data_length=len(full_data)\n\n class + SimpleMLP:\n @staticmethod\n def build(shape, classes):\n model + = Sequential()\n model.add(Conv1D(filters=4, kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n model.add(Flatten())\n model.add(Dense(8, + activation=\"relu\"))\n model.add(Dense(2, activation = ''softmax''))\n\n return + model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] + #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] + #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data + len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate + shard into data and labels lists\n data, label = zip(*data_shard)\n dataset + = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return + dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs + = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics + = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for + comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model + = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, + \n metrics=metrics)\n\n if(comm_round == 0):\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n\n local_weight + = client_model.get_weights()\n local_weight = [np.array(w).tolist() + for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': + bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight + = (requests.post(server_url,data=client_data))\n\n if weight.status_code + == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server + error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not + exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight + = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight + = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response + = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already + shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef + _serialize_float(float_value: float) -> str:\n if isinstance(float_value, + str):\n return float_value\n if not isinstance(float_value, (float, + int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + float.''.format(\n str(float_value), str(type(float_value))))\n return + str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', + description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, + required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": + "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": + "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": + "2"}'} + - name: delete-service + resource: + action: delete + flags: [--wait=false] + manifest: | + apiVersion: v1 + kind: Service + metadata: + name: http-service + spec: + ports: + - port: 80 + protocol: TCP + targetPort: 8080 + selector: + app: http-service + type: NodePort + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: fl-test + dag: + tasks: + - {name: client, template: client} + - {name: client-2, template: client-2} + - name: delete-service + template: delete-service + dependencies: [server] + - {name: http-service, template: http-service} + - name: server + template: server + dependencies: [http-service] + - name: show-results + template: show-results + dependencies: [client] + arguments: + parameters: + - {name: client-last_accuracy, value: '{{tasks.client.outputs.parameters.client-last_accuracy}}'} + - name: http-service + resource: + action: create + manifest: | + apiVersion: v1 + kind: Service + metadata: + name: http-service + spec: + ports: + - port: 5000 + protocol: TCP + targetPort: 8080 + selector: + app: http-service + outputs: + parameters: + - name: http-service-manifest + valueFrom: {jsonPath: '{}'} + - name: http-service-name + valueFrom: {jsonPath: '{.metadata.name}'} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: server + container: + args: [] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'flask' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'flask' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def server():\n import json\n import pandas as pd\n import numpy\ + \ as np\n import pickle\n import threading\n import time\n import\ + \ tensorflow as tf\n from flask import Flask, jsonify,request\n import\ + \ os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list\ + \ = []\n global_value = { #Share variable\n 'last_run_statue'\ + \ : False, #last run finish or not\n 'data_statue' : None,\ + \ #global_count finish or not\n 'global_count' : None,\n\ + \ 'scale_statue' : None,\n 'weight_statue'\ + \ : None,\n 'average_weights' : None,\n \ + \ 'shutdown' : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n \ + \ init_lock = threading.Lock()\n clients_local_count_lock = threading.Lock()\n\ + \ scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock\ + \ = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n\ + \ def before_request():\n print('get request')\n\n @app.route('/data',\ + \ methods=['POST'])\n def flask_server():\n with init_lock: #check\ + \ last run is finish and init varible\n\n while True:\n\n \ + \ if(len(clients_local_count)==0 and global_value['last_run_statue']\ + \ == False):#init the variable by first client enter\n \ + \ global_value['last_run_statue'] = True\n global_value['data_statue']\ + \ = False\n global_value['scale_statue'] = False\n \ + \ global_value['weight_statue'] = False\n \ + \ break\n\n elif(global_value['last_run_statue'] == True):\n\ + \ break\n time.sleep(3)\n\n local_count\ + \ = int(request.form.get('local_count')) #get data\n bs =\ + \ int(request.form.get('bs'))\n local_weight = json.loads(request.form.get('local_weight'))\n\ + \ local_weight = [np.array(lst) for lst in local_weight]\n\n \ + \ def scale_model_weights(weight, scalar):\n weight_final = []\n\ + \ steps = len(weight)\n for i in range(steps):\n \ + \ weight_final.append(scalar * weight[i])\n return\ + \ weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n \ + \ avg_grad = list()\n #get the average grad accross all\ + \ client gradients\n for grad_list_tuple in zip(*scaled_weight_list):\n\ + \ layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)\n\ + \ avg_grad.append(layer_mean)\n\n return avg_grad\n\ + \n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\ + \n with scaled_local_weight_list_lock:\n while True:\n\n\ + \ if (len(clients_local_count) == NUM_OF_CLIENTS and global_value['data_statue']\ + \ != True):\n global_value['last_run_statue'] = False\n\ + \ sum_of_local_count=sum(clients_local_count)\n\n \ + \ global_value['global_count'] = sum_of_local_count \n\n\ + \ scaling_factor=local_count/global_value['global_count']\n\ + \ scaled_weights = scale_model_weights(local_weight, scaling_factor)\n\ + \ scaled_local_weight_list.append(scaled_weights)\n\n \ + \ global_value['scale_statue'] = True \n \ + \ global_value['data_statue'] = True\n break\n \ + \ elif (global_value['data_statue'] == True and global_value['scale_statue']\ + \ == True):\n scaling_factor=local_count/global_value['global_count']\n\ + \ scaled_weights =scale_model_weights(local_weight, scaling_factor)\n\ + \ scaled_local_weight_list.append(scaled_weights)\n\n \ + \ break\n time.sleep(1)\n\n with cal_weight_lock:\n\ + \n while True:\n if(len(scaled_local_weight_list)\ + \ == NUM_OF_CLIENTS and global_value['weight_statue'] != True):\n\n \ + \ global_value['average_weights'] = sum_scaled_weights(scaled_local_weight_list)\n\ + \ global_value['weight_statue'] = True\n \ + \ global_value['average_weights'] = json.dumps([np.array(w).tolist()\ + \ for w in global_value['average_weights']])\n\n break\n\ + \n elif(global_value['weight_statue'] == True):\n\n \ + \ break\n\n time.sleep(1)\n\n clients_local_count.clear()\n\ + \ scaled_local_weight_list.clear()\n\n return jsonify({'result':\ + \ (global_value['average_weights'])})\n\n @app.route('/shutdown', methods=['GET'])\n\ + \ def shutdown_server():\n global_value['shutdown'] +=1 \n \ + \ with shutdown_lock:\n while True:\n if(global_value['shutdown']\ + \ == NUM_OF_CLIENTS):\n os._exit(0)\n \ + \ return 'Server shutting down...'\n time.sleep(1)\n\n \ + \ app.run(host=\"0.0.0.0\", port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Server',\ + \ description='')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs =\ + \ server(**_parsed_args)\n" + image: tensorflow/tensorflow + ports: + - {containerPort: 8080, name: my-port} + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + metadata: + labels: + app: http-service + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": [], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 + -m pip install --quiet --no-warn-script-location ''flask'' ''pandas'' || + PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''flask'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def server():\n import json\n import pandas as pd\n import numpy + as np\n import pickle\n import threading\n import time\n import + tensorflow as tf\n from flask import Flask, jsonify,request\n import + os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list + = []\n global_value = { #Share variable\n ''last_run_statue'' + : False, #last run finish or not\n ''data_statue'' : + None, #global_count finish or not\n ''global_count'' + : None,\n ''scale_statue'' : None,\n ''weight_statue'' + : None,\n ''average_weights'' : None,\n ''shutdown'' + : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n init_lock = threading.Lock()\n clients_local_count_lock + = threading.Lock()\n scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock + = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n def + before_request():\n print(''get request'')\n\n @app.route(''/data'', + methods=[''POST''])\n def flask_server():\n with init_lock: #check + last run is finish and init varible\n\n while True:\n\n if(len(clients_local_count)==0 + and global_value[''last_run_statue''] == False):#init the variable by first + client enter\n global_value[''last_run_statue''] = True\n global_value[''data_statue''] + = False\n global_value[''scale_statue''] = False\n global_value[''weight_statue''] + = False\n break\n\n elif(global_value[''last_run_statue''] + == True):\n break\n time.sleep(3)\n\n local_count + = int(request.form.get(''local_count'')) #get data\n bs + = int(request.form.get(''bs''))\n local_weight = json.loads(request.form.get(''local_weight''))\n local_weight + = [np.array(lst) for lst in local_weight]\n\n def scale_model_weights(weight, + scalar):\n weight_final = []\n steps = len(weight)\n for + i in range(steps):\n weight_final.append(scalar * weight[i])\n return + weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n avg_grad + = list()\n #get the average grad accross all client gradients\n for + grad_list_tuple in zip(*scaled_weight_list):\n layer_mean + = tf.math.reduce_sum(grad_list_tuple, axis=0)\n avg_grad.append(layer_mean)\n\n return + avg_grad\n\n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\n with + scaled_local_weight_list_lock:\n while True:\n\n if + (len(clients_local_count) == NUM_OF_CLIENTS and global_value[''data_statue''] + != True):\n global_value[''last_run_statue''] = False\n sum_of_local_count=sum(clients_local_count)\n\n global_value[''global_count''] + = sum_of_local_count \n\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights + = scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n global_value[''scale_statue''] + = True \n global_value[''data_statue''] = True\n break\n elif + (global_value[''data_statue''] == True and global_value[''scale_statue''] + == True):\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights + =scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n break\n time.sleep(1)\n\n with + cal_weight_lock:\n\n while True:\n if(len(scaled_local_weight_list) + == NUM_OF_CLIENTS and global_value[''weight_statue''] != True):\n\n global_value[''average_weights''] + = sum_scaled_weights(scaled_local_weight_list)\n global_value[''weight_statue''] + = True\n global_value[''average_weights''] = json.dumps([np.array(w).tolist() + for w in global_value[''average_weights'']])\n\n break\n\n elif(global_value[''weight_statue''] + == True):\n\n break\n\n time.sleep(1)\n\n clients_local_count.clear()\n scaled_local_weight_list.clear()\n\n return + jsonify({''result'': (global_value[''average_weights''])})\n\n @app.route(''/shutdown'', + methods=[''GET''])\n def shutdown_server():\n global_value[''shutdown''] + +=1 \n with shutdown_lock:\n while True:\n if(global_value[''shutdown''] + == NUM_OF_CLIENTS):\n os._exit(0)\n return + ''Server shutting down...''\n time.sleep(1)\n\n app.run(host=\"0.0.0.0\", + port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Server'', + description='''')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs + = server(**_parsed_args)\n"], "image": "tensorflow/tensorflow"}}, "name": + "Server"}', pipelines.kubeflow.org/component_ref: '{}'} + - name: show-results + container: + args: [--test-acc, '{{inputs.parameters.client-last_accuracy}}', '----output-paths', + /tmp/outputs/test_accuracy/data] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def show_results(test_acc): + return([test_acc]) + + def _serialize_float(float_value: float) -> str: + if isinstance(float_value, str): + return float_value + if not isinstance(float_value, (float, int)): + raise TypeError('Value "{}" has type "{}" instead of float.'.format( + str(float_value), str(type(float_value)))) + return str(float_value) + + import argparse + _parser = argparse.ArgumentParser(prog='Show results', description='') + _parser.add_argument("--test-acc", dest="test_acc", type=float, required=True, default=argparse.SUPPRESS) + _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1) + _parsed_args = vars(_parser.parse_args()) + _output_files = _parsed_args.pop("_output_paths", []) + + _outputs = show_results(**_parsed_args) + + _output_serializers = [ + _serialize_float, + + ] + + import os + for idx, output_file in enumerate(_output_files): + try: + os.makedirs(os.path.dirname(output_file)) + except OSError: + pass + with open(output_file, 'w') as f: + f.write(_output_serializers[idx](_outputs[idx])) + image: python:3.7 + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + inputs: + parameters: + - {name: client-last_accuracy} + outputs: + artifacts: + - {name: show-results-test_accuracy, path: /tmp/outputs/test_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--test-acc", {"inputValue": "test_acc"}, "----output-paths", + {"outputPath": "test_accuracy"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def show_results(test_acc):\n return([test_acc])\n\ndef _serialize_float(float_value: + float) -> str:\n if isinstance(float_value, str):\n return float_value\n if + not isinstance(float_value, (float, int)):\n raise TypeError(''Value + \"{}\" has type \"{}\" instead of float.''.format(\n str(float_value), + str(type(float_value))))\n return str(float_value)\n\nimport argparse\n_parser + = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--test-acc\", + dest=\"test_acc\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = show_results(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "python:3.7"}}, "inputs": [{"name": "test_acc", "type": "Float"}], + "name": "Show results", "outputs": [{"name": "test_accuracy", "type": "Float"}]}', + pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"test_acc": + "{{inputs.parameters.client-last_accuracy}}"}'} + arguments: + parameters: + - {name: namespace, value: kubeflow-user-thu01} + serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml new file mode 100644 index 000000000..b8f353c80 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml @@ -0,0 +1,211 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: mnist-pipeline- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-12-26T13:06:02.352865', + pipelines.kubeflow.org/pipeline_spec: '{"description": "A pipeline to train a + model on mnist dataset and start a tensorboard.", "name": "mnist pipeline"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: mnist-pipeline + templates: + - name: create-tensorboard-visualization + container: + args: [] + command: + - sh + - -ex + - -c + - | + log_dir="$0" + output_metadata_path="$1" + pod_template_spec="$2" + image="$3" + + mkdir -p "$(dirname "$output_metadata_path")" + + echo ' + { + "outputs" : [{ + "type": "tensorboard", + "source": "'"$log_dir"'", + "image": "'"$image"'", + "pod_template_spec": '"$pod_template_spec"' + }] + } + ' >"$output_metadata_path" + - volume://mypvc/logs + - /tmp/outputs/mlpipeline-ui-metadata/data + - '{"spec": {"containers": [{"volumeMounts": [{"mountPath": "/data", "name": + "mypvc"}], "resources": {"requests": {"cpu": "250m"}, "limits": {"cpu": "500m"}}}], + "serviceAccountName": "default-editor", "volumes": [{"name": "mypvc", "persistentVolumeClaim": + {"claimName": "my-awesome-kf-workshop-1703595962"}}]}}' + - footprintai/tensorboard:2.7.0 + image: alpine + resources: + limits: {cpu: '1'} + requests: {cpu: '1'} + outputs: + artifacts: + - {name: mlpipeline-ui-metadata, path: /tmp/outputs/mlpipeline-ui-metadata/data} + metadata: + annotations: {author: Alexey Volkov , canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/tensorboard/prepare_tensorboard/component.yaml', + pipelines.kubeflow.org/component_spec: '{"description": "Pre-creates Tensorboard + visualization for a given Log dir URI.\nThis way the Tensorboard can be + viewed before the training completes.\nThe output Log dir URI should be + passed to a trainer component that will write Tensorboard logs to that directory.\n", + "implementation": {"container": {"command": ["sh", "-ex", "-c", "log_dir=\"$0\"\noutput_metadata_path=\"$1\"\npod_template_spec=\"$2\"\nimage=\"$3\"\n\nmkdir + -p \"$(dirname \"$output_metadata_path\")\"\n\necho ''\n {\n \"outputs\" + : [{\n \"type\": \"tensorboard\",\n \"source\": \"''\"$log_dir\"''\",\n \"image\": + \"''\"$image\"''\",\n \"pod_template_spec\": ''\"$pod_template_spec\"''\n }]\n }\n'' + >\"$output_metadata_path\"\n", {"inputValue": "Log dir URI"}, {"outputPath": + "mlpipeline-ui-metadata"}, {"inputValue": "Pod Template Spec"}, {"inputValue": + "Image"}], "image": "alpine"}}, "inputs": [{"name": "Log dir URI", "type": + "String"}, {"default": "", "name": "Image", "type": "String"}, {"default": + "null", "name": "Pod Template Spec", "type": "String"}], "metadata": {"annotations": + {"author": "Alexey Volkov ", "canonical_location": + "https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/tensorboard/prepare_tensorboard/component.yaml"}}, + "name": "Create Tensorboard visualization", "outputs": [{"name": "mlpipeline-ui-metadata", + "type": "kfp.v1.ui-metadata"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "cc3c37c54619129e4f57e4564bc5df0ba9719a305e6145238f2ae7e54d87f2ef", "url": + "https://raw.githubusercontent.com/kubeflow/pipelines/1b107eb4bb2510ecb99fd5f4fb438cbf7c96a87a/components/contrib/tensorflow/tensorboard/prepare_tensorboard/component.yaml"}', + pipelines.kubeflow.org/arguments.parameters: '{"Image": "footprintai/tensorboard:2.7.0", + "Log dir URI": "volume://mypvc/logs", "Pod Template Spec": "{\"spec\": {\"containers\": + [{\"volumeMounts\": [{\"mountPath\": \"/data\", \"name\": \"mypvc\"}], \"resources\": + {\"requests\": {\"cpu\": \"250m\"}, \"limits\": {\"cpu\": \"500m\"}}}], + \"serviceAccountName\": \"default-editor\", \"volumes\": [{\"name\": \"mypvc\", + \"persistentVolumeClaim\": {\"claimName\": \"my-awesome-kf-workshop-1703595962\"}}]}}"}'} + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: mnist-func + container: + args: [--log-folder, /data, '----output-paths', /tmp/outputs/logdir/data] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def mnist_func(log_folder):\n\n print('mnist_func:', log_folder)\n \ + \ import tensorflow as tf\n import json\n mnist = tf.keras.datasets.mnist\n\ + \ (x_train,y_train), (x_test, y_test) = mnist.load_data()\n x_train,\ + \ x_test = x_train/255.0, x_test/255.0\n\n def create_model():\n \ + \ return tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape\ + \ = (28,28)),\n tf.keras.layers.Dense(512, activation = 'relu'),\n\ + \ tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10,\ + \ activation = 'softmax')\n ])\n model = create_model()\n model.compile(optimizer='adam',\n\ + \ loss='sparse_categorical_crossentropy',\n \ + \ metrics=['accuracy'])\n import datetime\n import os\n\n ###\ + \ add tensorboard logout callback\n log_dir = os.path.join(log_folder,\ + \ \"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n tensorboard_callback\ + \ = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n \ + \ ######\n\n model.fit(x=x_train, \n y=y_train, \n \ + \ epochs=5, \n validation_data=(x_test, y_test), \n \ + \ callbacks=[tensorboard_callback])\n\n print('At least tensorboard\ + \ callbacks are correct')\n print('logdir:', log_dir)\n return ([log_dir])\n\ + \ndef _serialize_str(str_value: str) -> str:\n if not isinstance(str_value,\ + \ str):\n raise TypeError('Value \"{}\" has type \"{}\" instead of\ + \ str.'.format(\n str(str_value), str(type(str_value))))\n return\ + \ str_value\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Mnist\ + \ func', description='')\n_parser.add_argument(\"--log-folder\", dest=\"log_folder\"\ + , type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"\ + ----output-paths\", dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args\ + \ = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"_output_paths\"\ + , [])\n\n_outputs = mnist_func(**_parsed_args)\n\n_output_serializers = [\n\ + \ _serialize_str,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow:2.0.0-py3 + resources: + limits: {cpu: '1'} + requests: {cpu: '1'} + volumeMounts: + - {mountPath: /data, name: mypvc} + inputs: + parameters: + - {name: mypvc-name} + outputs: + artifacts: + - {name: mnist-func-logdir, path: /tmp/outputs/logdir/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--log-folder", {"inputValue": "log_folder"}, "----output-paths", + {"outputPath": "logdir"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def mnist_func(log_folder):\n\n print(''mnist_func:'', log_folder)\n import + tensorflow as tf\n import json\n mnist = tf.keras.datasets.mnist\n (x_train,y_train), + (x_test, y_test) = mnist.load_data()\n x_train, x_test = x_train/255.0, + x_test/255.0\n\n def create_model():\n return tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape + = (28,28)),\n tf.keras.layers.Dense(512, activation = ''relu''),\n tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10, + activation = ''softmax'')\n ])\n model = create_model()\n model.compile(optimizer=''adam'',\n loss=''sparse_categorical_crossentropy'',\n metrics=[''accuracy''])\n import + datetime\n import os\n\n ### add tensorboard logout callback\n log_dir + = os.path.join(log_folder, \"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n tensorboard_callback + = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n ######\n\n model.fit(x=x_train, + \n y=y_train, \n epochs=5, \n validation_data=(x_test, + y_test), \n callbacks=[tensorboard_callback])\n\n print(''At + least tensorboard callbacks are correct'')\n print(''logdir:'', log_dir)\n return + ([log_dir])\n\ndef _serialize_str(str_value: str) -> str:\n if not isinstance(str_value, + str):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + str.''.format(\n str(str_value), str(type(str_value))))\n return + str_value\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Mnist + func'', description='''')\n_parser.add_argument(\"--log-folder\", dest=\"log_folder\", + type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = mnist_func(**_parsed_args)\n\n_output_serializers + = [\n _serialize_str,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow:2.0.0-py3"}}, "inputs": [{"name": "log_folder", + "type": "String"}], "name": "Mnist func", "outputs": [{"name": "logdir", + "type": "String"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"log_folder": + "/data"}'} + volumes: + - name: mypvc + persistentVolumeClaim: {claimName: '{{inputs.parameters.mypvc-name}}'} + - name: mnist-pipeline + dag: + tasks: + - {name: create-tensorboard-visualization, template: create-tensorboard-visualization} + - name: mnist-func + template: mnist-func + dependencies: [create-tensorboard-visualization, mypvc] + arguments: + parameters: + - {name: mypvc-name, value: '{{tasks.mypvc.outputs.parameters.mypvc-name}}'} + - {name: mypvc, template: mypvc} + - name: mypvc + resource: + action: create + manifest: | + apiVersion: v1 + kind: PersistentVolumeClaim + metadata: + name: '{{workflow.name}}-my-awesome-kf-workshop-1703595962' + spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: 1Gi + outputs: + parameters: + - name: mypvc-manifest + valueFrom: {jsonPath: '{}'} + - name: mypvc-name + valueFrom: {jsonPath: '{.metadata.name}'} + - name: mypvc-size + valueFrom: {jsonPath: '{.status.capacity.storage}'} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + arguments: + parameters: [] + serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml new file mode 100644 index 000000000..b1a4a1e25 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml @@ -0,0 +1,131 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: only-decision-tree- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.20, pipelines.kubeflow.org/pipeline_compilation_time: '2023-04-28T21:42:00.682134', + pipelines.kubeflow.org/pipeline_spec: '{"description": "Applies Decision Tree + for classification problem.", "name": "only_decision_tree"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.20} +spec: + entrypoint: only-decision-tree + templates: + - name: decision-tree-classifier + container: + args: [] + command: [python, decision_tree.py, --data, /tmp/inputs/Data/data, --accuracy, + /tmp/outputs/Accuracy/data] + image: lightnighttw/kubeflow:decision_tree_v2 + resources: + limits: + cpu: 2 + inputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/inputs/Data/data} + outputs: + parameters: + - name: decision-tree-classifier-Accuracy + valueFrom: {path: /tmp/outputs/Accuracy/data} + artifacts: + - {name: decision-tree-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains + a decision tree classifier", "implementation": {"container": {"command": + ["python", "decision_tree.py", "--data", {"inputPath": "Data"}, "--accuracy", + {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:decision_tree_v2"}}, + "inputs": [{"description": "Path where data is stored.", "name": "Data", + "type": "LocalPath"}], "name": "Decision Tree classifier", "outputs": [{"description": + "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "c5c232a9654213b3b222693949b71a5d561d04ed09543c20b6f8f2eab651ae0b", "url": + "decision_tree/decision_tree.yaml"}'} + - name: download-data-function + container: + args: [] + command: [python, download_data.py, --data, /tmp/outputs/Data/data] + image: lightnighttw/kubeflow:download_data + resources: + limits: + cpu: 2 + outputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/outputs/Data/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Download + toy data from sklearn datasets", "implementation": {"container": {"command": + ["python", "download_data.py", "--data", {"outputPath": "Data"}], "image": + "lightnighttw/kubeflow:download_data"}}, "name": "Download Data Function", + "outputs": [{"description": "Path where data will be stored.", "name": "Data", + "type": "LocalPath"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "467750defdccfec51c3af2a7eb853f74235f5f97329006d72bf33ff6e15ed02d", "url": + "download_data/download_data.yaml"}'} + - name: only-decision-tree + dag: + tasks: + - name: decision-tree-classifier + template: decision-tree-classifier + dependencies: [download-data-function] + arguments: + artifacts: + - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} + - {name: download-data-function, template: download-data-function} + - name: show-results + template: show-results + dependencies: [decision-tree-classifier] + arguments: + parameters: + - {name: decision-tree-classifier-Accuracy, value: '{{tasks.decision-tree-classifier.outputs.parameters.decision-tree-classifier-Accuracy}}'} + - name: show-results + container: + args: [--decision-tree, '{{inputs.parameters.decision-tree-classifier-Accuracy}}'] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def show_results(decision_tree): + # the results are shown. + + print(f"Decision tree (accuracy): {decision_tree}") + + import argparse + _parser = argparse.ArgumentParser(prog='Show results', description='') + _parser.add_argument("--decision-tree", dest="decision_tree", type=float, required=True, default=argparse.SUPPRESS) + _parsed_args = vars(_parser.parse_args()) + + _outputs = show_results(**_parsed_args) + image: python:3.7 + resources: + limits: + cpu: 2 + inputs: + parameters: + - {name: decision-tree-classifier-Accuracy} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--decision-tree", {"inputValue": "decision_tree"}], "command": + ["sh", "-ec", "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 + -u \"$program_path\" \"$@\"\n", "def show_results(decision_tree):\n # + the results are shown.\n\n print(f\"Decision tree (accuracy): {decision_tree}\")\n\nimport + argparse\n_parser = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--decision-tree\", + dest=\"decision_tree\", type=float, required=True, default=argparse.SUPPRESS)\n_parsed_args + = vars(_parser.parse_args())\n\n_outputs = show_results(**_parsed_args)\n"], + "image": "python:3.7"}}, "inputs": [{"name": "decision_tree", "type": "Float"}], + "name": "Show results"}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"decision_tree": + "{{inputs.parameters.decision-tree-classifier-Accuracy}}"}'} + arguments: + parameters: [] + serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml new file mode 100644 index 000000000..2e10bcefa --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml @@ -0,0 +1,229 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: three-pipeline- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-04-28T12:05:44.365082', + pipelines.kubeflow.org/pipeline_spec: '{"description": "Applies Decision Tree, + random forest and Logistic Regression for classification problem.", "name": + "Three Pipeline"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: three-pipeline + templates: + - name: decision-tree-classifier + container: + args: [] + command: [python, decision_tree.py, --data, /tmp/inputs/Data/data, --accuracy, + /tmp/outputs/Accuracy/data] + image: lightnighttw/kubeflow:decision_tree_v2 + resources: + limits: + cpu: 2 + inputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/inputs/Data/data} + outputs: + parameters: + - name: decision-tree-classifier-Accuracy + valueFrom: {path: /tmp/outputs/Accuracy/data} + artifacts: + - {name: decision-tree-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains + a decision tree classifier", "implementation": {"container": {"command": + ["python", "decision_tree.py", "--data", {"inputPath": "Data"}, "--accuracy", + {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:decision_tree_v2"}}, + "inputs": [{"description": "Path where data is stored.", "name": "Data", + "type": "LocalPath"}], "name": "Decision Tree classifier", "outputs": [{"description": + "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "c5c232a9654213b3b222693949b71a5d561d04ed09543c20b6f8f2eab651ae0b", "url": + "decision_tree/decision_tree.yaml"}'} + - name: download-data-function + container: + args: [] + command: [python, download_data.py, --data, /tmp/outputs/Data/data] + image: lightnighttw/kubeflow:download_data + resources: + limits: + cpu: 2 + outputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/outputs/Data/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Download + toy data from sklearn datasets", "implementation": {"container": {"command": + ["python", "download_data.py", "--data", {"outputPath": "Data"}], "image": + "lightnighttw/kubeflow:download_data"}}, "name": "Download Data Function", + "outputs": [{"description": "Path where data will be stored.", "name": "Data", + "type": "LocalPath"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "467750defdccfec51c3af2a7eb853f74235f5f97329006d72bf33ff6e15ed02d", "url": + "download_data/download_data.yaml"}'} + - name: logistic-regression-classifier + container: + args: [] + command: [python, logistic_regression.py, --data, /tmp/inputs/Data/data, --accuracy, + /tmp/outputs/Accuracy/data] + image: lightnighttw/kubeflow:logistic_regression + resources: + limits: + cpu: 2 + inputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/inputs/Data/data} + outputs: + parameters: + - name: logistic-regression-classifier-Accuracy + valueFrom: {path: /tmp/outputs/Accuracy/data} + artifacts: + - {name: logistic-regression-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains + a Logistic Regression Classifier", "implementation": {"container": {"command": + ["python", "logistic_regression.py", "--data", {"inputPath": "Data"}, "--accuracy", + {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:logistic_regression"}}, + "inputs": [{"description": "Path where data is stored.", "name": "Data", + "type": "LocalPath"}], "name": "Logistic Regression Classifier", "outputs": + [{"description": "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', + pipelines.kubeflow.org/component_ref: '{"digest": "a8d1e77d07d18a75bef200aee96f35136833fc4bb535f33fd949a307beb094c2", + "url": "logistic_regression/logistic_regression.yaml"}'} + - name: random-forest-classifier + container: + args: [] + command: [python, randomforest.py, --data, /tmp/inputs/Data/data, --accuracy, + /tmp/outputs/Accuracy/data] + image: lightnighttw/kubeflow:random_forest_v4 + resources: + limits: + cpu: 2 + inputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/inputs/Data/data} + outputs: + parameters: + - name: random-forest-classifier-Accuracy + valueFrom: {path: /tmp/outputs/Accuracy/data} + artifacts: + - {name: random-forest-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Train + a random forest classifier", "implementation": {"container": {"command": + ["python", "randomforest.py", "--data", {"inputPath": "Data"}, "--accuracy", + {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:random_forest_v4"}}, + "inputs": [{"description": "Path where data is stored.", "name": "Data", + "type": "LocalPath"}], "name": "Random Forest classifier", "outputs": [{"description": + "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "b49b12da3371976eddf41d662685bb49d71b419d516de65efdd90938d2c706bc", "url": + "randomForest/random_forest.yaml"}'} + - name: show-results + container: + args: [--decision-tree, '{{inputs.parameters.decision-tree-classifier-Accuracy}}', + --logistic-regression, '{{inputs.parameters.logistic-regression-classifier-Accuracy}}', + --random-forest, '{{inputs.parameters.random-forest-classifier-Accuracy}}'] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def show_results(decision_tree, logistic_regression, random_forest): + # Given the outputs from decision_tree and logistic regression components + # the results are shown. + + print(f"Decision tree (accuracy): {decision_tree}") + print(f"Logistic regression (accuracy): {logistic_regression}") + print(f"Random forest (accuracy): {random_forest}") + + import argparse + _parser = argparse.ArgumentParser(prog='Show results', description='') + _parser.add_argument("--decision-tree", dest="decision_tree", type=float, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--logistic-regression", dest="logistic_regression", type=float, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--random-forest", dest="random_forest", type=float, required=True, default=argparse.SUPPRESS) + _parsed_args = vars(_parser.parse_args()) + + _outputs = show_results(**_parsed_args) + image: python:3.7 + resources: + limits: + cpu: 2 + inputs: + parameters: + - {name: decision-tree-classifier-Accuracy} + - {name: logistic-regression-classifier-Accuracy} + - {name: random-forest-classifier-Accuracy} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--decision-tree", {"inputValue": "decision_tree"}, "--logistic-regression", + {"inputValue": "logistic_regression"}, "--random-forest", {"inputValue": + "random_forest"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def show_results(decision_tree, logistic_regression, random_forest):\n # + Given the outputs from decision_tree and logistic regression components\n # + the results are shown.\n\n print(f\"Decision tree (accuracy): {decision_tree}\")\n print(f\"Logistic + regression (accuracy): {logistic_regression}\")\n print(f\"Random forest + (accuracy): {random_forest}\")\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Show + results'', description='''')\n_parser.add_argument(\"--decision-tree\", + dest=\"decision_tree\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--logistic-regression\", + dest=\"logistic_regression\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--random-forest\", + dest=\"random_forest\", type=float, required=True, default=argparse.SUPPRESS)\n_parsed_args + = vars(_parser.parse_args())\n\n_outputs = show_results(**_parsed_args)\n"], + "image": "python:3.7"}}, "inputs": [{"name": "decision_tree", "type": "Float"}, + {"name": "logistic_regression", "type": "Float"}, {"name": "random_forest", + "type": "Float"}], "name": "Show results"}', pipelines.kubeflow.org/component_ref: '{}', + pipelines.kubeflow.org/arguments.parameters: '{"decision_tree": "{{inputs.parameters.decision-tree-classifier-Accuracy}}", + "logistic_regression": "{{inputs.parameters.logistic-regression-classifier-Accuracy}}", + "random_forest": "{{inputs.parameters.random-forest-classifier-Accuracy}}"}'} + - name: three-pipeline + dag: + tasks: + - name: decision-tree-classifier + template: decision-tree-classifier + dependencies: [download-data-function] + arguments: + artifacts: + - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} + - {name: download-data-function, template: download-data-function} + - name: logistic-regression-classifier + template: logistic-regression-classifier + dependencies: [download-data-function] + arguments: + artifacts: + - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} + - name: random-forest-classifier + template: random-forest-classifier + dependencies: [download-data-function] + arguments: + artifacts: + - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} + - name: show-results + template: show-results + dependencies: [decision-tree-classifier, logistic-regression-classifier, random-forest-classifier] + arguments: + parameters: + - {name: decision-tree-classifier-Accuracy, value: '{{tasks.decision-tree-classifier.outputs.parameters.decision-tree-classifier-Accuracy}}'} + - {name: logistic-regression-classifier-Accuracy, value: '{{tasks.logistic-regression-classifier.outputs.parameters.logistic-regression-classifier-Accuracy}}'} + - {name: random-forest-classifier-Accuracy, value: '{{tasks.random-forest-classifier.outputs.parameters.random-forest-classifier-Accuracy}}'} + arguments: + parameters: [] + serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/requirements.txt b/kubeflow/examples/1.connect-kubeflow/py/requirements.txt new file mode 100644 index 000000000..0a8547bcc --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/py/requirements.txt @@ -0,0 +1,2 @@ +setuptools +wheel diff --git a/kubeflow/examples/1.connect-kubeflow/settings.js b/kubeflow/examples/1.connect-kubeflow/settings.js new file mode 100644 index 000000000..332013ee1 --- /dev/null +++ b/kubeflow/examples/1.connect-kubeflow/settings.js @@ -0,0 +1,498 @@ +/** + * This is the default settings file provided by Node-RED. + * + * It can contain any valid JavaScript code that will get run when Node-RED + * is started. + * + * Lines that start with // are commented out. + * Each entry should be separated from the entries above and below by a comma ',' + * + * For more information about individual settings, refer to the documentation: + * https://nodered.org/docs/user-guide/runtime/configuration + * + * The settings are split into the following sections: + * - Flow File and User Directory Settings + * - Security + * - Server Settings + * - Runtime Settings + * - Editor Settings + * - Node Settings + * + **/ + +module.exports = { + +/******************************************************************************* + * Flow File and User Directory Settings + * - flowFile + * - credentialSecret + * - flowFilePretty + * - userDir + * - nodesDir + ******************************************************************************/ + + /** The file containing the flows. If not set, defaults to flows_.json **/ + flowFile: 'flows.json', + + /** By default, credentials are encrypted in storage using a generated key. To + * specify your own secret, set the following property. + * If you want to disable encryption of credentials, set this property to false. + * Note: once you set this property, do not change it - doing so will prevent + * node-red from being able to decrypt your existing credentials and they will be + * lost. + */ + //credentialSecret: "a-secret-key", + credentialSecret: process.env.NODE_RED_CREDENTIAL_SECRET, + + /** By default, the flow JSON will be formatted over multiple lines making + * it easier to compare changes when using version control. + * To disable pretty-printing of the JSON set the following property to false. + */ + flowFilePretty: true, + + /** By default, all user data is stored in a directory called `.node-red` under + * the user's home directory. To use a different location, the following + * property can be used + */ + //userDir: '/home/nol/.node-red/', + + /** Node-RED scans the `nodes` directory in the userDir to find local node files. + * The following property can be used to specify an additional directory to scan. + */ + //nodesDir: '/home/nol/.node-red/nodes', + +/******************************************************************************* + * Security + * - adminAuth + * - https + * - httpsRefreshInterval + * - requireHttps + * - httpNodeAuth + * - httpStaticAuth + ******************************************************************************/ + + /** To password protect the Node-RED editor and admin API, the following + * property can be used. See http://nodered.org/docs/security.html for details. + */ + //adminAuth: { + // type: "credentials", + // users: [{ + // username: "admin", + // password: "$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN.", + // permissions: "*" + // }] + //}, + + /** The following property can be used to enable HTTPS + * This property can be either an object, containing both a (private) key + * and a (public) certificate, or a function that returns such an object. + * See http://nodejs.org/api/https.html#https_https_createserver_options_requestlistener + * for details of its contents. + */ + + /** Option 1: static object */ + //https: { + // key: require("fs").readFileSync('privkey.pem'), + // cert: require("fs").readFileSync('cert.pem') + //}, + + /** Option 2: function that returns the HTTP configuration object */ + // https: function() { + // // This function should return the options object, or a Promise + // // that resolves to the options object + // return { + // key: require("fs").readFileSync('privkey.pem'), + // cert: require("fs").readFileSync('cert.pem') + // } + // }, + + /** If the `https` setting is a function, the following setting can be used + * to set how often, in hours, the function will be called. That can be used + * to refresh any certificates. + */ + //httpsRefreshInterval : 12, + + /** The following property can be used to cause insecure HTTP connections to + * be redirected to HTTPS. + */ + //requireHttps: true, + + /** To password protect the node-defined HTTP endpoints (httpNodeRoot), + * including node-red-dashboard, or the static content (httpStatic), the + * following properties can be used. + * The `pass` field is a bcrypt hash of the password. + * See http://nodered.org/docs/security.html#generating-the-password-hash + */ + //httpNodeAuth: {user:"user",pass:"$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN."}, + //httpStaticAuth: {user:"user",pass:"$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN."}, + +/******************************************************************************* + * Server Settings + * - uiPort + * - uiHost + * - apiMaxLength + * - httpServerOptions + * - httpAdminRoot + * - httpAdminMiddleware + * - httpNodeRoot + * - httpNodeCors + * - httpNodeMiddleware + * - httpStatic + ******************************************************************************/ + + /** the tcp port that the Node-RED web server is listening on */ + uiPort: process.env.PORT || 1880, + + /** By default, the Node-RED UI accepts connections on all IPv4 interfaces. + * To listen on all IPv6 addresses, set uiHost to "::", + * The following property can be used to listen on a specific interface. For + * example, the following would only allow connections from the local machine. + */ + //uiHost: "127.0.0.1", + + /** The maximum size of HTTP request that will be accepted by the runtime api. + * Default: 5mb + */ + //apiMaxLength: '5mb', + + /** The following property can be used to pass custom options to the Express.js + * server used by Node-RED. For a full list of available options, refer + * to http://expressjs.com/en/api.html#app.settings.table + */ + //httpServerOptions: { }, + + /** By default, the Node-RED UI is available at http://localhost:1880/ + * The following property can be used to specify a different root path. + * If set to false, this is disabled. + */ + //httpAdminRoot: '/admin', + + /** The following property can be used to add a custom middleware function + * in front of all admin http routes. For example, to set custom http + * headers. It can be a single function or an array of middleware functions. + */ + // httpAdminMiddleware: function(req,res,next) { + // // Set the X-Frame-Options header to limit where the editor + // // can be embedded + // //res.set('X-Frame-Options', 'sameorigin'); + // next(); + // }, + + + /** Some nodes, such as HTTP In, can be used to listen for incoming http requests. + * By default, these are served relative to '/'. The following property + * can be used to specifiy a different root path. If set to false, this is + * disabled. + */ + //httpNodeRoot: '/red-nodes', + + /** The following property can be used to configure cross-origin resource sharing + * in the HTTP nodes. + * See https://github.com/troygoode/node-cors#configuration-options for + * details on its contents. The following is a basic permissive set of options: + */ + //httpNodeCors: { + // origin: "*", + // methods: "GET,PUT,POST,DELETE" + //}, + + /** If you need to set an http proxy please set an environment variable + * called http_proxy (or HTTP_PROXY) outside of Node-RED in the operating system. + * For example - http_proxy=http://myproxy.com:8080 + * (Setting it here will have no effect) + * You may also specify no_proxy (or NO_PROXY) to supply a comma separated + * list of domains to not proxy, eg - no_proxy=.acme.co,.acme.co.uk + */ + + /** The following property can be used to add a custom middleware function + * in front of all http in nodes. This allows custom authentication to be + * applied to all http in nodes, or any other sort of common request processing. + * It can be a single function or an array of middleware functions. + */ + //httpNodeMiddleware: function(req,res,next) { + // // Handle/reject the request, or pass it on to the http in node by calling next(); + // // Optionally skip our rawBodyParser by setting this to true; + // //req.skipRawBodyParser = true; + // next(); + //}, + + /** When httpAdminRoot is used to move the UI to a different root path, the + * following property can be used to identify a directory of static content + * that should be served at http://localhost:1880/. + */ + //httpStatic: '/home/nol/node-red-static/', + +/******************************************************************************* + * Runtime Settings + * - lang + * - logging + * - contextStorage + * - exportGlobalContextKeys + * - externalModules + ******************************************************************************/ + + /** Uncomment the following to run node-red in your preferred language. + * Available languages include: en-US (default), ja, de, zh-CN, zh-TW, ru, ko + * Some languages are more complete than others. + */ + // lang: "de", + + /** Configure the logging output */ + logging: { + /** Only console logging is currently supported */ + console: { + /** Level of logging to be recorded. Options are: + * fatal - only those errors which make the application unusable should be recorded + * error - record errors which are deemed fatal for a particular request + fatal errors + * warn - record problems which are non fatal + errors + fatal errors + * info - record information about the general running of the application + warn + error + fatal errors + * debug - record information which is more verbose than info + info + warn + error + fatal errors + * trace - record very detailed logging + debug + info + warn + error + fatal errors + * off - turn off all logging (doesn't affect metrics or audit) + */ + level: "info", + /** Whether or not to include metric events in the log output */ + metrics: false, + /** Whether or not to include audit events in the log output */ + audit: false + } + }, + + /** Context Storage + * The following property can be used to enable context storage. The configuration + * provided here will enable file-based context that flushes to disk every 30 seconds. + * Refer to the documentation for further options: https://nodered.org/docs/api/context/ + */ + //contextStorage: { + // default: { + // module:"localfilesystem" + // }, + //}, + + /** `global.keys()` returns a list of all properties set in global context. + * This allows them to be displayed in the Context Sidebar within the editor. + * In some circumstances it is not desirable to expose them to the editor. The + * following property can be used to hide any property set in `functionGlobalContext` + * from being list by `global.keys()`. + * By default, the property is set to false to avoid accidental exposure of + * their values. Setting this to true will cause the keys to be listed. + */ + exportGlobalContextKeys: false, + + /** Configure how the runtime will handle external npm modules. + * This covers: + * - whether the editor will allow new node modules to be installed + * - whether nodes, such as the Function node are allowed to have their + * own dynamically configured dependencies. + * The allow/denyList options can be used to limit what modules the runtime + * will install/load. It can use '*' as a wildcard that matches anything. + */ + externalModules: { + // autoInstall: false, /** Whether the runtime will attempt to automatically install missing modules */ + // autoInstallRetry: 30, /** Interval, in seconds, between reinstall attempts */ + // palette: { /** Configuration for the Palette Manager */ + // allowInstall: true, /** Enable the Palette Manager in the editor */ + // allowUpdate: true, /** Allow modules to be updated in the Palette Manager */ + // allowUpload: true, /** Allow module tgz files to be uploaded and installed */ + // allowList: ['*'], + // denyList: [], + // allowUpdateList: ['*'], + // denyUpdateList: [] + // }, + // modules: { /** Configuration for node-specified modules */ + // allowInstall: true, + // allowList: [], + // denyList: [] + // } + }, + + +/******************************************************************************* + * Editor Settings + * - disableEditor + * - editorTheme + ******************************************************************************/ + + /** The following property can be used to disable the editor. The admin API + * is not affected by this option. To disable both the editor and the admin + * API, use either the httpRoot or httpAdminRoot properties + */ + //disableEditor: false, + + /** Customising the editor + * See https://nodered.org/docs/user-guide/runtime/configuration#editor-themes + * for all available options. + */ + editorTheme: { + /** The following property can be used to set a custom theme for the editor. + * See https://github.com/node-red-contrib-themes/theme-collection for + * a collection of themes to chose from. + */ + //theme: "", + + /** To disable the 'Welcome to Node-RED' tour that is displayed the first + * time you access the editor for each release of Node-RED, set this to false + */ + //tours: false, + + palette: { + /** The following property can be used to order the categories in the editor + * palette. If a node's category is not in the list, the category will get + * added to the end of the palette. + * If not set, the following default order is used: + */ + //categories: ['subflows', 'common', 'function', 'network', 'sequence', 'parser', 'storage'], + }, + + projects: { + /** To enable the Projects feature, set this value to true */ + enabled: false, + workflow: { + /** Set the default projects workflow mode. + * - manual - you must manually commit changes + * - auto - changes are automatically committed + * This can be overridden per-user from the 'Git config' + * section of 'User Settings' within the editor + */ + mode: "manual" + } + }, + + codeEditor: { + /** Select the text editor component used by the editor. + * Defaults to "ace", but can be set to "ace" or "monaco" + */ + lib: "ace", + options: { + /** The follow options only apply if the editor is set to "monaco" + * + * theme - must match the file name of a theme in + * packages/node_modules/@node-red/editor-client/src/vendor/monaco/dist/theme + * e.g. "tomorrow-night", "upstream-sunburst", "github", "my-theme" + */ + theme: "vs", + /** other overrides can be set e.g. fontSize, fontFamily, fontLigatures etc. + * for the full list, see https://microsoft.github.io/monaco-editor/api/interfaces/monaco.editor.istandaloneeditorconstructionoptions.html + */ + //fontSize: 14, + //fontFamily: "Cascadia Code, Fira Code, Consolas, 'Courier New', monospace", + //fontLigatures: true, + } + } + }, + +/******************************************************************************* + * Node Settings + * - fileWorkingDirectory + * - functionGlobalContext + * - functionExternalModules + * - nodeMessageBufferMaxLength + * - ui (for use with Node-RED Dashboard) + * - debugUseColors + * - debugMaxLength + * - execMaxBufferSize + * - httpRequestTimeout + * - mqttReconnectTime + * - serialReconnectTime + * - socketReconnectTime + * - socketTimeout + * - tcpMsgQueueSize + * - inboundWebSocketTimeout + * - tlsConfigDisableLocalFiles + * - webSocketNodeVerifyClient + ******************************************************************************/ + + /** The working directory to handle relative file paths from within the File nodes + * defaults to the working directory of the Node-RED process. + */ + //fileWorkingDirectory: "", + + /** Allow the Function node to load additional npm modules directly */ + functionExternalModules: true, + + /** The following property can be used to set predefined values in Global Context. + * This allows extra node modules to be made available with in Function node. + * For example, the following: + * functionGlobalContext: { os:require('os') } + * will allow the `os` module to be accessed in a Function node using: + * global.get("os") + */ + functionGlobalContext: { + // os:require('os'), + }, + + /** The maximum number of messages nodes will buffer internally as part of their + * operation. This applies across a range of nodes that operate on message sequences. + * defaults to no limit. A value of 0 also means no limit is applied. + */ + //nodeMessageBufferMaxLength: 0, + + /** If you installed the optional node-red-dashboard you can set it's path + * relative to httpNodeRoot + * Other optional properties include + * readOnly:{boolean}, + * middleware:{function or array}, (req,res,next) - http middleware + * ioMiddleware:{function or array}, (socket,next) - socket.io middleware + */ + //ui: { path: "ui" }, + + /** Colourise the console output of the debug node */ + //debugUseColors: true, + + /** The maximum length, in characters, of any message sent to the debug sidebar tab */ + debugMaxLength: 1000, + + /** Maximum buffer size for the exec node. Defaults to 10Mb */ + //execMaxBufferSize: 10000000, + + /** Timeout in milliseconds for HTTP request connections. Defaults to 120s */ + //httpRequestTimeout: 120000, + + /** Retry time in milliseconds for MQTT connections */ + mqttReconnectTime: 15000, + + /** Retry time in milliseconds for Serial port connections */ + serialReconnectTime: 15000, + + /** Retry time in milliseconds for TCP socket connections */ + //socketReconnectTime: 10000, + + /** Timeout in milliseconds for TCP server socket connections. Defaults to no timeout */ + //socketTimeout: 120000, + + /** Maximum number of messages to wait in queue while attempting to connect to TCP socket + * defaults to 1000 + */ + //tcpMsgQueueSize: 2000, + + /** Timeout in milliseconds for inbound WebSocket connections that do not + * match any configured node. Defaults to 5000 + */ + //inboundWebSocketTimeout: 5000, + + /** To disable the option for using local files for storing keys and + * certificates in the TLS configuration node, set this to true. + */ + //tlsConfigDisableLocalFiles: true, + + /** The following property can be used to verify websocket connection attempts. + * This allows, for example, the HTTP request headers to be checked to ensure + * they include valid authentication information. + */ + //webSocketNodeVerifyClient: function(info) { + // /** 'info' has three properties: + // * - origin : the value in the Origin header + // * - req : the HTTP request + // * - secure : true if req.connection.authorized or req.connection.encrypted is set + // * + // * The function should return true if the connection should be accepted, false otherwise. + // * + // * Alternatively, if this function is defined to accept a second argument, callback, + // * it can be used to verify the client asynchronously. + // * The callback takes three arguments: + // * - result : boolean, whether to accept the connection or not + // * - code : if result is false, the HTTP error status to return + // * - reason: if result is false, the HTTP reason string to return + // */ + //}, +} diff --git a/kubeflow/examples/Dockerfile b/kubeflow/examples/Dockerfile new file mode 100644 index 000000000..f195a77fd --- /dev/null +++ b/kubeflow/examples/Dockerfile @@ -0,0 +1,20 @@ +FROM nodered/node-red:2.2.3-12 + +ARG PREFIX + +ENV NODE_OPTIONS=--max_old_space_size=128 + +USER root + +RUN apk update && \ + apk add py3-pip + +USER node-red +# Copy package.json to the WORKDIR so npm builds all +# of your added nodes modules for Node-RED +COPY package.json . +RUN npm install --unsafe-perm --no-update-notifier --no-fund --only=production + +ADD scripts/entrypoint.sh . + +ENTRYPOINT ["./entrypoint.sh"] diff --git a/kubeflow/examples/README.md b/kubeflow/examples/README.md new file mode 100644 index 000000000..c56f4dd28 --- /dev/null +++ b/kubeflow/examples/README.md @@ -0,0 +1,40 @@ +#### Examples + +This example folder contains multiple node-red flow setups, each is isolated with a folder that allows you to run the different examples with the same settings. + +##### Add customized npm package + +You can add customized npm package via `npm install` or directly modify `packages.json`. Then rebuild your container image. + +##### Build + +Simply to run the following command to build node-red image + +``` +./build.sh +``` + +##### Run the container image + +To run the container image, use + +``` +KUBEFLOW_HOST= \ +KUBEFLOW_USERNAME= \ +KUBEFLOW_PASSWORD= \ +./run.sh +``` + +which would mount the current folder (i.e. ./example) onto the containers. We took this as a convenient step as you could change codes with node-red UI and the mounting volume allows the changes to be reflected onto your local file system. + +The example-args allows you to specify which example you want to run, for example + +``` +./run.sh 0.helloworld +``` + +would run the `0.helloworld` example. + +##### Visit vis UI + +then you can go to UI, check it out: http://127.0.0.1:1880/ diff --git a/kubeflow/examples/build.sh b/kubeflow/examples/build.sh new file mode 100644 index 000000000..37ad5136d --- /dev/null +++ b/kubeflow/examples/build.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash + +docker compose build diff --git a/kubeflow/examples/docker-compose.yaml b/kubeflow/examples/docker-compose.yaml new file mode 100644 index 000000000..2a9619477 --- /dev/null +++ b/kubeflow/examples/docker-compose.yaml @@ -0,0 +1,33 @@ +################################################################################ +# Node-RED Stack or Compose +################################################################################ +# docker stack deploy node-red --compose-file docker-compose-node-red.yml +# docker-compose -f docker-compose-node-red.yml -p myNoderedProject up +################################################################################ +version: "3.7" + +services: + node-red: + image: reg.footprint-ai.com/public/kube-nodered:latest + build: + context: . + dockerfile: Dockerfile + labels: + # workaround around a docker-compose bug when image is also used by docker run (https://github.com/docker/compose/issues/10162) + com.docker.compose.container-number: "1" + environment: + - TZ=Asia/Taipei + - USERDIR=$USERDIR + - NODE_RED_CREDENTIAL_SECRET=noderedtutorial + - KUBEFLOW_HOST=$KUBEFLOW_HOST + - KUBEFLOW_USERNAME=$KUBEFLOW_USERNAME + - KUBEFLOW_PASSWORD=$KUBEFLOW_PASSWORD + ports: + - "1880:1880" + networks: + - node-red-net + volumes: + - ./:/data + +networks: + node-red-net: diff --git 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"engines": { + "node": ">=12" + } +} diff --git a/kubeflow/examples/run.sh b/kubeflow/examples/run.sh new file mode 100644 index 000000000..daca187e0 --- /dev/null +++ b/kubeflow/examples/run.sh @@ -0,0 +1,15 @@ +#!/usr/bin/env bash + +target=$1 + +if [ "$#" -eq 1 ] +then + USERDIR=/data/$target +else + USERDIR=/data/0.helloworld +fi + +echo "run with userdir=$USERDIR" + +USERDIR=$USERDIR docker compose up +#USERDIR=$USERDIR docker compose convert diff --git a/kubeflow/examples/scripts/entrypoint.sh b/kubeflow/examples/scripts/entrypoint.sh new file mode 100644 index 000000000..705054c0a --- /dev/null +++ b/kubeflow/examples/scripts/entrypoint.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +trap stop SIGINT SIGTERM + +function stop() { + kill $CHILD_PID + wait $CHILD_PID +} + +/usr/local/bin/node $NODE_OPTIONS node_modules/node-red/red.js --userDir $USERDIR & + +CHILD_PID="$!" + +wait "${CHILD_PID}" From 0870f9157ab5e6f9d592dd9ddc24fa6acee340cb Mon Sep 17 00:00:00 2001 From: NootNoot0 <161794667+NootNoot0@users.noreply.github.com> Date: Mon, 24 Jun 2024 16:17:51 +0800 Subject: [PATCH 2/3] Delete kubeflow directory --- kubeflow/LICENSE | 201 - kubeflow/README.md | 354 - kubeflow/examples/0.helloworld/flows.json | 101 - .../examples/0.helloworld/flows_cred.json | 3 - kubeflow/examples/0.helloworld/helloworld.py | 6 - kubeflow/examples/0.helloworld/package.json | 6 - kubeflow/examples/0.helloworld/settings.js | 498 - .../examples/1.connect-kubeflow/flows.json | 368 - .../1.connect-kubeflow/flows_cred.json | 3 - .../node_modules/nodepipe/LSTM.html | 149 - .../node_modules/nodepipe/LSTM.js | 66 - .../node_modules/nodepipe/LSTM0.js | 147 - .../node_modules/nodepipe/decisionTree0.js | 147 - .../node_modules/nodepipe/decisiontree.html | 149 - .../node_modules/nodepipe/decisiontree.js | 66 - .../node_modules/nodepipe/fl.html | 149 - .../node_modules/nodepipe/fl.js | 66 - .../node_modules/nodepipe/fl0.js | 147 - .../node_modules/nodepipe/mnist.html | 149 - 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kubeflow/examples/package.json delete mode 100644 kubeflow/examples/run.sh delete mode 100644 kubeflow/examples/scripts/entrypoint.sh diff --git a/kubeflow/LICENSE b/kubeflow/LICENSE deleted file mode 100644 index 261eeb9e9..000000000 --- a/kubeflow/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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Please refer the following example to launch an individual example: - -1. In terminal (If you on Windows system, please use WSL) -``` -$ git clone https://github.com/NightLightTw/kube-nodered.git -``` -2. Enter target folder -``` -cd kube-nodered/examples -``` -3. Enter account information and start -``` -KUBEFLOW_HOST= \ -KUBEFLOW_USERNAME= \ -KUBEFLOW_PASSWORD= \ -./run.sh -``` -> **Info:** Here please use 1.connect-kubeflow - -## Install dependencies - -1. Then you can go to UI, check it out: http://127.0.0.1:1880/ -![](https://hackmd.io/_uploads/HJ8Rbmdya.png) - -2. Click the “install dependency” button to install dependency items such as specific python libraries and wait for its completion -![](https://hackmd.io/_uploads/rygsMmuy6.png) - -3. Click the “list experiments” button to test the environment work! -![](https://hackmd.io/_uploads/HyXxfX_kp.png) - -## Using our nodes -Switch to the "three-pipeline" flow and press the button to trigger the pipeline process - -![](https://hackmd.io/_uploads/Hkr4mXdk6.png) - -On kubeflow: -![](https://hackmd.io/_uploads/H1_hEX_JT.png) - - - -> **Info:** If the environment variable does not work, please fill in the account password directly in the python file - - -## Test python files to interact with kubeflow -``` -# Open another terminal and check docker status -docker ps -#enter container -docker exec -it bash -#enter document folder -cd /data/1.connect-kubeflow/py/api_examples -#execute function -python3 -``` - -You can test the file in api_example -> **Info:** Some of these files require a custom name, description, or assigned id in - -## Possible problems and solution -Q1: MissingSchema Invalid URL '' - -A1: This problem means that the login information is not accessed correctly, which may be caused by the environment variable not being read. -You can directly override the login information of the specified file - -ex:![](https://hackmd.io/_uploads/ryx59rejBh.jpg) - -Change to your own login information -``` -host = "https://example@test.com" -username = "test01" -password = "123456" -``` - -# Modify your own custom nodes/pipeline -![implementation architecture](https://hackmd.io/_uploads/H1ZLgUsH2.png) - -## Kubeflow part -### Custom make pipeline’s yaml file -Please refer to [Kubeflow implementation：add Random Forest algorithm](https://hackmd.io/@Nhi7So-lTz2m5R6pHyCLcA/Sk1eZFTbh) - -### Take changing randomForest.py as an example - -Modify using your own yaml file path -> **Info:** Line 66: uploadfile='pipelines/only_randomforest.yaml' - -> **Info:** Line 122~129 use json parser for filtering different outputs from get_run() - -```python= -from __future__ import print_function - -import time -import kfp_server_api -import os -import requests -import string -import random -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -def random_suffix() -> string: - return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the Experiment API class - experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) - name="experiment-" + random_suffix() - description="This is a experiment for only_randomforest." - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey( - type=kfp_server_api.models.ApiResourceType.NAMESPACE, - id=resource_reference_key_id - ), - relationship=kfp_server_api.models.ApiRelationship.OWNER - )] - body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. - try: - # Creates a new experiment. - experiment_api_response = experiment_api_instance.create_experiment(body) - experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) - - # Create an instance of the pipeline API class - api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) - uploadfile='pipelines/only_randomforest.yaml' - name='pipeline-' + random_suffix() - description="This is a only_randomForest pipline." - try: - pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) - pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) - - # Create an instance of the run API class - run_api_instance = kfp_server_api.RunServiceApi(api_client) - display_name = 'run_only_randomForest' + random_suffix() - description = "This is a only_randomForest run." - pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), - relationship=kfp_server_api.models.ApiRelationship.OWNER )] - body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | - try: - # Creates a new run. - run_api_response = run_api_instance.create_run(body) - run_id = run_api_response.run.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling RunServiceApi->create_run: %s\n" % e) - - Completed_flag = False - polling_interval = 10 # Time in seconds between polls - - while not Completed_flag: - try: - time.sleep(1) - # Finds a specific run by ID. - api_instance = run_api_instance.get_run(run_id) - output = api_instance.pipeline_runtime.workflow_manifest - output = json.loads(output) - - try: - nodes = output['status']['nodes'] - conditions = output['status']['conditions'] # Comfirm completion. - - except KeyError: - nodes = {} - conditions = [] - - output_value = None - Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False - - except ApiException as e: - print("Exception when calling RunServiceApi->get_run: %s\n" % e) - break - - if not Completed_flag: - print("Pipeline is still running. Waiting...") - time.sleep(polling_interval-1) - - for node_id, node in nodes.items(): - if 'inputs' in node and 'parameters' in node['inputs']: - for parameter in node['inputs']['parameters']: - if parameter['name'] == 'random-forest-classifier-Accuracy': #change parameter - output_value = parameter['value'] - - if output_value is not None: - print(f"Random Forest Classifier Accuracy: {output_value}") - else: - print("Parameter not found.") - print(nodes) -``` - -## Node-red part -**Package nodered pyshell node** - -**A node mainly consists of two files** -* **Javascript file(.js)** -define what the node does -* **HTML file(.html)** -Define the properties of the node and the windows and help messages in the Node-RED editor - -**When finally package into npm module, will need package.json** - - -### **package.json** -A standard file for describing the content of node.js modules - -A standard package.json can be generated using npm init. This command will ask a series of questions to find a reasonable default value. When asked for the name of the module name: enter the example name node-red-contrib- - -When it is established, you need to manually add the node-red attribute -*p.s. Where the example files need to be changed * - - - -```json= -{ - "name": "node-red-contrib-pythonshell-custom", - ... - "node-red": { - "nodes": { - "decisionTree": "decisiontree.js", - "randomForest": "randomforest.js", - "logisticRegression": "logisticregression.js" - "":"" - } - }, - ... -} - -``` -### **HTML** -```javascript= - - - - - - - \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js deleted file mode 100644 index d16c5fe18..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Copyright 2014 Sense Tecnic Systems, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - **/ - -var util = require("util"); -var httpclient; -var PythonshellNode = require('./LSTM0'); - -module.exports = function(RED) { - "use strict"; - - function PythonshellInNode(n) { - RED.nodes.createNode(this,n); - - var node = this; - node.config = n; // copy config to the backend so that down bellow we can have a reference - - var pyNode = new PythonshellNode(n); - - pyNode.setStatusCallback(node.status.bind(node)) - - node.on("input",function(msg) { - pyNode.onInput(msg, function(result){ - node.send(result); - }, function(err){ - node.error(err); - }); - }); - - node.on('close', ()=>pyNode.onClose()); - } - - RED.nodes.registerType("LSTM", PythonshellInNode); - - RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { - var node = RED.nodes.getNode(req.params.id); - if (node != null) { - try { - if (node.config.continuous){// see above comment - node.receive({payload: 'pythonshell@close'}) - } else { - node.receive(); - } - res.sendStatus(200); - } catch(err) { - res.sendStatus(500); - node.error(RED._("pythonshell.failed",{error:err.toString()})); - } - } else { - res.sendStatus(404); - } - }); - -} diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js deleted file mode 100644 index 86c0742f4..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js +++ /dev/null @@ -1,147 +0,0 @@ -var fs = require("fs"); - -function PythonshellInNode(config) { - if (!config.pyfile){ - throw 'pyfile not present'; - } - this.pythonExec = config.python3 ? "python3" : "python"; - this.pyfile = '/data/1.connect-kubeflow/py/LSTM.py'; - this.virtualenv = config.virtualenv; - - if (!fs.existsSync(this.pyfile)) { - throw 'pyfile not exist'; - } - - if (this.virtualenv && !fs.existsSync(this.virtualenv)){ - throw 'configured virtualenv not exist, consider remove or change'; - } - - this.stdInData = config.stdInData; - this.continuous = this.stdInData ? true : config.continuous; - this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); - this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); - this.spawn = require('child_process').spawn; - this.onStatus = ()=>{} -} - -PythonshellInNode.prototype.onInput = function(msg, out, err) { - payload = msg.payload || ''; - if (typeof payload === 'object'){ - payload = JSON.stringify(payload); - } else if (typeof payload !== 'string'){ - payload = payload.toString(); - } - - if (payload === 'pythonshell@close'){ - if (this.py != null){ - this.onClose() - return - } else { - // trigger new execution - payload = '' - } - } - - if (this.continuous && !this.stdInData && this.py != null){ - this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) - return - } - - var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec - - if (this.stdInData){ - if (!this.py){ - this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { - cwd: this.pydir, - detached: true - }); - this.firstExecution = true - } else { - this.firstExecution = false - } - } else { - this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { - cwd: this.pydir - }); - } - - this.onStatus({fill:"green",shape:"dot",text:"Standby"}) - - // subsequence message, no need to setup callbacks - if (this.stdInData && !this.firstExecution){ - this.py.stdin.write(payload + '\n') - return - } - - var py = this.py; - var dataString = ''; - var errString = ''; - - py.stdout.on('data', data => { - clearTimeout(this.standbyTimer) - - this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) - - let dataStr = data.toString(); - - dataString += dataStr; - - if (dataString.endsWith("\n")){ - if (this.continuous){ - msg.payload = dataString; - out(msg); - dataString = '' - } - } - - this.standbyTimer = setTimeout(()=>{ - this.onStatus({fill:"green",shape:"dot",text:"Standby"}) - }, 2000) - - }); - - py.stderr.on('data', data => { - errString += String(data);// just a different way to do it - this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) - }); - - py.stderr.on('error', console.log) - py.stdout.on('error', console.log) - py.stdin.on('error', console.log) - py.on('error', console.log) - - py.on('close', code =>{ - if (code){ - err('exit code: ' + code + ', ' + errString); - this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) - } else if (!this.continuous){ - msg.payload = dataString.trim(); - out(msg); - this.onStatus({fill:"green",shape:"dot",text:"Done"}) - } else { - this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) - } - this.py = null - setTimeout(()=>{ - this.onStatus({}) - }, 2000) - }); - - if (this.stdInData){ - py.stdin.write(payload + '\n') - } -}; - -PythonshellInNode.prototype.onClose = function() { - if (this.py){ - this.py.kill() - this.py = null - } -}; - -PythonshellInNode.prototype.setStatusCallback = function(callback) { - this.onStatus = callback -}; - - -module.exports = PythonshellInNode \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js deleted file mode 100644 index 7a8f3a690..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js +++ /dev/null @@ -1,147 +0,0 @@ -var fs = require("fs"); - -function PythonshellInNode(config) { - if (!config.pyfile){ - throw 'pyfile not present'; - } - this.pythonExec = config.python3 ? "python3" : "python"; - this.pyfile = "/data/1.connect-kubeflow/py/decisionTree.py"; - this.virtualenv = config.virtualenv; - - if (!fs.existsSync(this.pyfile)) { - throw 'pyfile not exist'; - } - - if (this.virtualenv && !fs.existsSync(this.virtualenv)){ - throw 'configured virtualenv not exist, consider remove or change'; - } - - this.stdInData = config.stdInData; - this.continuous = this.stdInData ? true : config.continuous; - this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); - this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); - this.spawn = require('child_process').spawn; - this.onStatus = ()=>{} -} - -PythonshellInNode.prototype.onInput = function(msg, out, err) { - payload = msg.payload || ''; - if (typeof payload === 'object'){ - payload = JSON.stringify(payload); - } else if (typeof payload !== 'string'){ - payload = payload.toString(); - } - - if (payload === 'pythonshell@close'){ - if (this.py != null){ - this.onClose() - return - } else { - // trigger new execution - payload = '' - } - } - - if (this.continuous && !this.stdInData && this.py != null){ - this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) - return - } - - var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec - - if (this.stdInData){ - if (!this.py){ - this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { - cwd: this.pydir, - detached: true - }); - this.firstExecution = true - } else { - this.firstExecution = false - } - } else { - this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { - cwd: this.pydir - }); - } - - this.onStatus({fill:"green",shape:"dot",text:"Standby"}) - - // subsequence message, no need to setup callbacks - if (this.stdInData && !this.firstExecution){ - this.py.stdin.write(payload + '\n') - return - } - - var py = this.py; - var dataString = ''; - var errString = ''; - - py.stdout.on('data', data => { - clearTimeout(this.standbyTimer) - - this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) - - let dataStr = data.toString(); - - dataString += dataStr; - - if (dataString.endsWith("\n")){ - if (this.continuous){ - msg.payload = dataString; - out(msg); - dataString = '' - } - } - - this.standbyTimer = setTimeout(()=>{ - this.onStatus({fill:"green",shape:"dot",text:"Standby"}) - }, 2000) - - }); - - py.stderr.on('data', data => { - errString += String(data);// just a different way to do it - this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) - }); - - py.stderr.on('error', console.log) - py.stdout.on('error', console.log) - py.stdin.on('error', console.log) - py.on('error', console.log) - - py.on('close', code =>{ - if (code){ - err('exit code: ' + code + ', ' + errString); - this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) - } else if (!this.continuous){ - msg.payload = dataString.trim(); - out(msg); - this.onStatus({fill:"green",shape:"dot",text:"Done"}) - } else { - this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) - } - this.py = null - setTimeout(()=>{ - this.onStatus({}) - }, 2000) - }); - - if (this.stdInData){ - py.stdin.write(payload + '\n') - } -}; - -PythonshellInNode.prototype.onClose = function() { - if (this.py){ - this.py.kill() - this.py = null - } -}; - -PythonshellInNode.prototype.setStatusCallback = function(callback) { - this.onStatus = callback -}; - - -module.exports = PythonshellInNode \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html deleted file mode 100644 index 2045d7745..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html +++ /dev/null @@ -1,149 +0,0 @@ -f - - - - - - - \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js deleted file mode 100644 index 1727299e0..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Copyright 2014 Sense Tecnic Systems, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - **/ - -var util = require("util"); -var httpclient; -var PythonshellNode = require('./decisionTree0'); - -module.exports = function(RED) { - "use strict"; - - function PythonshellInNode(n) { - RED.nodes.createNode(this,n); - - var node = this; - node.config = n; // copy config to the backend so that down bellow we can have a reference - - var pyNode = new PythonshellNode(n); - - pyNode.setStatusCallback(node.status.bind(node)) - - node.on("input",function(msg) { - pyNode.onInput(msg, function(result){ - node.send(result); - }, function(err){ - node.error(err); - }); - }); - - node.on('close', ()=>pyNode.onClose()); - } - - RED.nodes.registerType("decisionTree", PythonshellInNode); - - RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { - var node = RED.nodes.getNode(req.params.id); - if (node != null) { - try { - if (node.config.continuous){// see above comment - node.receive({payload: 'pythonshell@close'}) - } else { - node.receive(); - } - res.sendStatus(200); - } catch(err) { - res.sendStatus(500); - node.error(RED._("pythonshell.failed",{error:err.toString()})); - } - } else { - res.sendStatus(404); - } - }); - -} diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html deleted file mode 100644 index 35f46a295..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html +++ /dev/null @@ -1,149 +0,0 @@ -f - - - - - - - \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js deleted file mode 100644 index edabfa19e..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Copyright 2014 Sense Tecnic Systems, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - **/ - -var util = require("util"); -var httpclient; -var PythonshellNode = require('./fl0'); - -module.exports = function(RED) { - "use strict"; - - function PythonshellInNode(n) { - RED.nodes.createNode(this,n); - - var node = this; - node.config = n; // copy config to the backend so that down bellow we can have a reference - - var pyNode = new PythonshellNode(n); - - pyNode.setStatusCallback(node.status.bind(node)) - - node.on("input",function(msg) { - pyNode.onInput(msg, function(result){ - node.send(result); - }, function(err){ - node.error(err); - }); - }); - - node.on('close', ()=>pyNode.onClose()); - } - - RED.nodes.registerType("fl", PythonshellInNode); - - RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { - var node = RED.nodes.getNode(req.params.id); - if (node != null) { - try { - if (node.config.continuous){// see above comment - node.receive({payload: 'pythonshell@close'}) - } else { - node.receive(); - } - res.sendStatus(200); - } catch(err) { - res.sendStatus(500); - node.error(RED._("pythonshell.failed",{error:err.toString()})); - } - } else { - res.sendStatus(404); - } - }); - -} diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js deleted file mode 100644 index ee8b3f222..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js +++ /dev/null @@ -1,147 +0,0 @@ -var fs = require("fs"); - -function PythonshellInNode(config) { - if (!config.pyfile){ - throw 'pyfile not present'; - } - this.pythonExec = config.python3 ? "python3" : "python"; - this.pyfile = "/data/1.connect-kubeflow/py/fl.py"; - this.virtualenv = config.virtualenv; - - if (!fs.existsSync(this.pyfile)) { - throw 'pyfile not exist'; - } - - if (this.virtualenv && !fs.existsSync(this.virtualenv)){ - throw 'configured virtualenv not exist, consider remove or change'; - } - - this.stdInData = config.stdInData; - this.continuous = this.stdInData ? true : config.continuous; - this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); - this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); - this.spawn = require('child_process').spawn; - this.onStatus = ()=>{} -} - -PythonshellInNode.prototype.onInput = function(msg, out, err) { - payload = msg.payload || ''; - if (typeof payload === 'object'){ - payload = JSON.stringify(payload); - } else if (typeof payload !== 'string'){ - payload = payload.toString(); - } - - if (payload === 'pythonshell@close'){ - if (this.py != null){ - this.onClose() - return - } else { - // trigger new execution - payload = '' - } - } - - if (this.continuous && !this.stdInData && this.py != null){ - this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) - return - } - - var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec - - if (this.stdInData){ - if (!this.py){ - this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { - cwd: this.pydir, - detached: true - }); - this.firstExecution = true - } else { - this.firstExecution = false - } - } else { - this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { - cwd: this.pydir - }); - } - - this.onStatus({fill:"green",shape:"dot",text:"Standby"}) - - // subsequence message, no need to setup callbacks - if (this.stdInData && !this.firstExecution){ - this.py.stdin.write(payload + '\n') - return - } - - var py = this.py; - var dataString = ''; - var errString = ''; - - py.stdout.on('data', data => { - clearTimeout(this.standbyTimer) - - this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) - - let dataStr = data.toString(); - - dataString += dataStr; - - if (dataString.endsWith("\n")){ - if (this.continuous){ - msg.payload = dataString; - out(msg); - dataString = '' - } - } - - this.standbyTimer = setTimeout(()=>{ - this.onStatus({fill:"green",shape:"dot",text:"Standby"}) - }, 2000) - - }); - - py.stderr.on('data', data => { - errString += String(data);// just a different way to do it - this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) - }); - - py.stderr.on('error', console.log) - py.stdout.on('error', console.log) - py.stdin.on('error', console.log) - py.on('error', console.log) - - py.on('close', code =>{ - if (code){ - err('exit code: ' + code + ', ' + errString); - this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) - } else if (!this.continuous){ - msg.payload = dataString.trim(); - out(msg); - this.onStatus({fill:"green",shape:"dot",text:"Done"}) - } else { - this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) - } - this.py = null - setTimeout(()=>{ - this.onStatus({}) - }, 2000) - }); - - if (this.stdInData){ - py.stdin.write(payload + '\n') - } -}; - -PythonshellInNode.prototype.onClose = function() { - if (this.py){ - this.py.kill() - this.py = null - } -}; - -PythonshellInNode.prototype.setStatusCallback = function(callback) { - this.onStatus = callback -}; - - -module.exports = PythonshellInNode \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html deleted file mode 100644 index 322e0e99d..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html +++ /dev/null @@ -1,149 +0,0 @@ -f - - - - - - - \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js deleted file mode 100644 index 3c3e04c13..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Copyright 2014 Sense Tecnic Systems, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - **/ - -var util = require("util"); -var httpclient; -var PythonshellNode = require('./mnist0'); - -module.exports = function(RED) { - "use strict"; - - function PythonshellInNode(n) { - RED.nodes.createNode(this,n); - - var node = this; - node.config = n; // copy config to the backend so that down bellow we can have a reference - - var pyNode = new PythonshellNode(n); - - pyNode.setStatusCallback(node.status.bind(node)) - - node.on("input",function(msg) { - pyNode.onInput(msg, function(result){ - node.send(result); - }, function(err){ - node.error(err); - }); - }); - - node.on('close', ()=>pyNode.onClose()); - } - - RED.nodes.registerType("mnist", PythonshellInNode); - - RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { - var node = RED.nodes.getNode(req.params.id); - if (node != null) { - try { - if (node.config.continuous){// see above comment - node.receive({payload: 'pythonshell@close'}) - } else { - node.receive(); - } - res.sendStatus(200); - } catch(err) { - res.sendStatus(500); - node.error(RED._("pythonshell.failed",{error:err.toString()})); - } - } else { - res.sendStatus(404); - } - }); - -} diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js deleted file mode 100644 index 58ebc34a3..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js +++ /dev/null @@ -1,147 +0,0 @@ -var fs = require("fs"); - -function PythonshellInNode(config) { - if (!config.pyfile){ - throw 'pyfile not present'; - } - this.pythonExec = config.python3 ? "python3" : "python"; - this.pyfile = "/data/1.connect-kubeflow/py/mnist.py"; - this.virtualenv = config.virtualenv; - - if (!fs.existsSync(this.pyfile)) { - throw 'pyfile not exist'; - } - - if (this.virtualenv && !fs.existsSync(this.virtualenv)){ - throw 'configured virtualenv not exist, consider remove or change'; - } - - this.stdInData = config.stdInData; - this.continuous = this.stdInData ? true : config.continuous; - this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); - this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); - this.spawn = require('child_process').spawn; - this.onStatus = ()=>{} -} - -PythonshellInNode.prototype.onInput = function(msg, out, err) { - payload = msg.payload || ''; - if (typeof payload === 'object'){ - payload = JSON.stringify(payload); - } else if (typeof payload !== 'string'){ - payload = payload.toString(); - } - - if (payload === 'pythonshell@close'){ - if (this.py != null){ - this.onClose() - return - } else { - // trigger new execution - payload = '' - } - } - - if (this.continuous && !this.stdInData && this.py != null){ - this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) - return - } - - var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec - - if (this.stdInData){ - if (!this.py){ - this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { - cwd: this.pydir, - detached: true - }); - this.firstExecution = true - } else { - this.firstExecution = false - } - } else { - this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { - cwd: this.pydir - }); - } - - this.onStatus({fill:"green",shape:"dot",text:"Standby"}) - - // subsequence message, no need to setup callbacks - if (this.stdInData && !this.firstExecution){ - this.py.stdin.write(payload + '\n') - return - } - - var py = this.py; - var dataString = ''; - var errString = ''; - - py.stdout.on('data', data => { - clearTimeout(this.standbyTimer) - - this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) - - let dataStr = data.toString(); - - dataString += dataStr; - - if (dataString.endsWith("\n")){ - if (this.continuous){ - msg.payload = dataString; - out(msg); - dataString = '' - } - } - - this.standbyTimer = setTimeout(()=>{ - this.onStatus({fill:"green",shape:"dot",text:"Standby"}) - }, 2000) - - }); - - py.stderr.on('data', data => { - errString += String(data);// just a different way to do it - this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) - }); - - py.stderr.on('error', console.log) - py.stdout.on('error', console.log) - py.stdin.on('error', console.log) - py.on('error', console.log) - - py.on('close', code =>{ - if (code){ - err('exit code: ' + code + ', ' + errString); - this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) - } else if (!this.continuous){ - msg.payload = dataString.trim(); - out(msg); - this.onStatus({fill:"green",shape:"dot",text:"Done"}) - } else { - this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) - } - this.py = null - setTimeout(()=>{ - this.onStatus({}) - }, 2000) - }); - - if (this.stdInData){ - py.stdin.write(payload + '\n') - } -}; - -PythonshellInNode.prototype.onClose = function() { - if (this.py){ - this.py.kill() - this.py = null - } -}; - -PythonshellInNode.prototype.setStatusCallback = function(callback) { - this.onStatus = callback -}; - - -module.exports = PythonshellInNode \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json b/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json deleted file mode 100644 index fcf46cd21..000000000 --- a/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "name": "node-red-contrib-pythonshell-custom", - "version": "1.0.0", - "description": "modified version of node-red-contrib-pythonshell with additional features", - "scripts": { - "test": "./node_modules/mocha/bin/mocha" - }, - "keywords": [ - "distributed", - "python", - "node", - "node-red" - ], - "author": "WuChunYen", - "license": "ISC", - "dependencies": {}, - "node-red": { - "nodes": { - "decisionTree": "decisiontree.js", - "randomForest": "randomforest.js", - "logisticRegression": "logisticregression.js", - "fl": "fl.js", - "mnist": "mnist.js", - "LSTM": "LSTM.js" - } - }, - "devDependencies": { - "mocha": "^5.0.4" - } -} diff --git a/kubeflow/examples/1.connect-kubeflow/package.json b/kubeflow/examples/1.connect-kubeflow/package.json deleted file mode 100644 index b788fd491..000000000 --- a/kubeflow/examples/1.connect-kubeflow/package.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "name": "node-red-project", - "description": "A Node-RED Project", - "version": "0.0.1", - "private": true -} \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/LSTM.py b/kubeflow/examples/1.connect-kubeflow/py/LSTM.py deleted file mode 100644 index 0eb2cb76a..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/LSTM.py +++ /dev/null @@ -1,132 +0,0 @@ -from __future__ import print_function - -import time -import kfp_server_api -import os -import requests -import string -import random -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = "https://ai4edu.thu01.footprint-ai.com" -username = "thu02" -password = "M*$3sNF7" - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -def random_suffix() -> string: - return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the Experiment API class - experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) - name="experiment-" + random_suffix() - description="This is a experiment for LSTM." - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey( - type=kfp_server_api.models.ApiResourceType.NAMESPACE, - id=resource_reference_key_id - ), - relationship=kfp_server_api.models.ApiRelationship.OWNER - )] - body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. - try: - # Creates a new experiment. - experiment_api_response = experiment_api_instance.create_experiment(body) - experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) - - # Create an instance of the pipeline API class - api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) - uploadfile='pipelines/LSTM_pipeline.yaml' - name='pipeline-' + random_suffix() - description="This is a LSTM pipline." - try: - pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) - pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) - - # Create an instance of the run API class - run_api_instance = kfp_server_api.RunServiceApi(api_client) - display_name = 'LSTM' + random_suffix() - description = "This is a LSTM run." - pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), - relationship=kfp_server_api.models.ApiRelationship.OWNER )] - body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | - try: - # Creates a new run. - run_api_response = run_api_instance.create_run(body) - run_id = run_api_response.run.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling RunServiceApi->create_run: %s\n" % e) - - Completed_flag = False - polling_interval = 10 # Time in seconds between polls - - while not Completed_flag: - try: - time.sleep(1) - # Finds a specific run by ID. - api_instance = run_api_instance.get_run(run_id) - output = api_instance.pipeline_runtime.workflow_manifest - output = json.loads(output) - - try: - nodes = output['status']['nodes'] - conditions = output['status']['conditions'] # Comfirm completion. - - except KeyError: - nodes = {} - conditions = [] - - output_value = None - Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False - - except ApiException as e: - print("Exception when calling RunServiceApi->get_run: %s\n" % e) - break - - if not Completed_flag: - print("Pipeline is still running. Waiting...") - time.sleep(polling_interval-1) - - for node_id, node in nodes.items(): - if 'inputs' in node and 'parameters' in node['inputs']: - for parameter in node['inputs']['parameters']: - if parameter['name'] == 'random-forest-classifier-Accuracy': #change parameter - output_value = parameter['value'] - - if output_value is not None: - print(f"Random Forest Classifier Accuracy: {output_value}") - else: - print("Parameter not found.") - print(nodes) diff --git a/kubeflow/examples/1.connect-kubeflow/py/README.md b/kubeflow/examples/1.connect-kubeflow/py/README.md deleted file mode 100644 index 06bb167e4..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/README.md +++ /dev/null @@ -1,18 +0,0 @@ -## How to use -``` -KUBEFLOW_HOST= \ -KUBEFLOW_USERNAME= \ -KUBEFLOW_PASSWORD= \ -python3 -``` -## three pipelines -流程架構: -create_experiment -> upload_pipeline -> create_run -> get_run -> filter -> result -### decisionTree.py -功能：以乳癌資料集訓練decisionTree模型並回傳準確率 - -### logisticRegression.py -功能：以乳癌資料集訓練logisticRegression模型並回傳準確率 - -### randomForest.py -功能：以乳癌資料集訓練randomForest模型並回傳準確率 diff --git a/kubeflow/examples/1.connect-kubeflow/py/__pycache__/kfp_login.cpython-38.pyc b/kubeflow/examples/1.connect-kubeflow/py/__pycache__/kfp_login.cpython-38.pyc deleted file mode 100644 index 193a91af9ab988978f2373b5b3ad1d940277f916..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1992 zcmah}&2JM&6rb5IZ=AIg0;F(h=YRx`iqlH%#dQ(1Q2G(2Aqh$qR5|ONak9>OXPKEL z#Bwf$V^QguR7%uS@BJfs?0K*4g@1txrEg|!6rfUPwe#`jz2Cg=yR}*s!MJn%9{aJ5 z&|ms+dbn`74!`^eCWQUXVas24K=*yAc>Y6 zxbSn4$SjFf!zpao13@y8h67gkSt#Wm7qsvMQ@KbZTT6{c1y-%*=kUu17@-j;GXAVX z_rO-@2p^-T_@~taXXNxPxVNeE#5!+9^oG_P~4GC z{Weniw(x`-a(15ZsIRcSEt`qRgj z_#M^&C^Z_;nxWaW{$#>6Gv@kp(I0iDjaQTmzD*-+lp`zaRwYnmIbH?#)~K$y{J z7tsihEPCe90;IVo_JMoaESfq@3N+Qm8W7agl8#h*$zgoWCB0Nn>$t|fu6-O)M43S#~YisG|jTPAD z@OTQ>zye62U<95OKmuu*XBiiYQG=}2Hsi`3Bb4>RJW;P!P%D>lx=r4G4GipZwPUbZ 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-from __future__ import print_function -import string -import random -import time -import kfp_server_api -import os -import requests -import kfp -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.ExperimentServiceApi(api_client) - name="" # str | The ID of the name to be create. - description='' # str | The description experiment. - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey( - type=kfp_server_api.models.ApiResourceType.NAMESPACE, - id=resource_reference_key_id - ), - relationship=kfp_server_api.models.ApiRelationship.OWNER - )] - body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. - try: - # Creates a new experiment. - api_response = api_instance.create_experiment(body) - pprint(api_response) - except ApiException as e: - print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py deleted file mode 100644 index b02691de7..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py +++ /dev/null @@ -1,58 +0,0 @@ -from __future__ import print_function -import string -import random -import time -import kfp_server_api -import os -import requests -import kfp -import json -from pprint import pprint -from kfp_server_api.rest import ApiException -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.RunServiceApi(api_client) - pipeline_id = '' # str | The ID of the pipeline. - experiment_id = '' # str | The ID of the experiment. - display_name = '' # str | The name of the run to be create. - description = '' # str | The description of run. - pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), - relationship=kfp_server_api.models.ApiRelationship.OWNER )] - body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | - - try: - # Creates a new run. - api_response = api_instance.create_run(body) - pprint(api_response) - except ApiException as e: - print("Exception when calling RunServiceApi->create_run: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py deleted file mode 100644 index 31d9205c7..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py +++ /dev/null @@ -1,51 +0,0 @@ -from __future__ import print_function -import string -import random -import time -import kfp_server_api -import os -import requests -import kfp -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.ExperimentServiceApi(api_client) - experiment_id = '' # str | The ID of the experiment to be deleted. - - try: - # Deletes an experiment without deleting the experiment's runs and recurring runs. - # To avoid unexpected behaviors, delete an experiment's runs and recurring runs before deleting the experiment. - api_response = api_instance.delete_experiment(experiment_id) - pprint(api_response) - except ApiException as e: - print("Exception when calling ExperimentServiceApi->delete_experiment: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py deleted file mode 100644 index c04d595ef..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import print_function -import string -import random -import time -import kfp_server_api -import os -import requests -import kfp -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.PipelineServiceApi(api_client) - id = '' # str | The ID of the pipeline to be deleted. - - try: - # Deletes a pipeline and its pipeline versions. - api_response = api_instance.delete_pipeline(id) - pprint(api_response) - except ApiException as e: - print("Exception when calling PipelineServiceApi->delete_pipeline: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py deleted file mode 100644 index aefc7dca3..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import print_function -import string -import random -import time -import kfp_server_api -import os -import requests -import kfp -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.RunServiceApi(api_client) - id = '' # str | The ID of the run to be deleted. - - try: - # Deletes a run. - api_response = api_instance.delete_run(id) - pprint(api_response) - except ApiException as e: - print("Exception when calling RunServiceApi->delete_run: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py deleted file mode 100644 index f62c56bdc..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py +++ /dev/null @@ -1,64 +0,0 @@ -from __future__ import print_function - -import time -import kfp_server_api -import os -import requests -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces -import json - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.RunServiceApi(api_client) - run_id = '' # str | The ID of the run to be retrieved. - - try: - # Finds a specific run by ID. - api_response = api_instance.get_run(run_id) - output = api_response.pipeline_runtime.workflow_manifest - output = json.loads(output) - nodes = output['status']['nodes'] - conditions = output['status']['conditions'] # Comfirm completion. - output_value = None - - for node_id, node in nodes.items(): - if 'inputs' in node and 'parameters' in node['inputs']: - for parameter in node['inputs']['parameters']: - if parameter['name'] == 'decision-tree-classifier-Accuracy': - output_value = parameter['value'] - break - - if output_value is not None: - print(f"Decision Tree Classifier Accuracy: {output_value}") - else: - print("Parameter not found.") - except ApiException as e: - print("Exception when calling RunServiceApi->get_run: %s\n" % e) diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py deleted file mode 100644 index 7c95abcf0..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py +++ /dev/null @@ -1,93 +0,0 @@ -import re -from urllib.parse import urlsplit - -import requests - -# NOTE: the following code is referred from https://github.com/kubeflow/website/issues/2916 -def get_istio_auth_session(url: str, username: str, password: str) -> dict: - """ - Determine if the specified URL is secured by Dex and try to obtain a session cookie. - WARNING: only Dex `staticPasswords` and `LDAP` authentication are currently supported - (we default default to using `staticPasswords` if both are enabled) - - :param url: Kubeflow server URL, including protocol - :param username: Dex `staticPasswords` or `LDAP` username - :param password: Dex `staticPasswords` or `LDAP` password - :return: auth session information - """ - # define the default return object - auth_session = { - "endpoint_url": url, # KF endpoint URL - "redirect_url": None, # KF redirect URL, if applicable - "dex_login_url": None, # Dex login URL (for POST of credentials) - "is_secured": None, # True if KF endpoint is secured - "session_cookie": None # Resulting session cookies in the form "key1=value1; key2=value2" - } - - # use a persistent session (for cookies) - with requests.Session() as s: - - ################ - # Determine if Endpoint is Secured - ################ - resp = s.get(url, allow_redirects=True) - if resp.status_code != 200: - raise RuntimeError( - f"HTTP status code '{resp.status_code}' for GET against: {url}" - ) - - auth_session["redirect_url"] = resp.url - - # if we were NOT redirected, then the endpoint is UNSECURED - if len(resp.history) == 0: - auth_session["is_secured"] = False - return auth_session - else: - auth_session["is_secured"] = True - - ################ - # Get Dex Login URL - ################ - redirect_url_obj = urlsplit(auth_session["redirect_url"]) - - # if we are at `/auth?=xxxx` path, we need to select an auth type - if re.search(r"/auth$", redirect_url_obj.path): - # default to "staticPasswords" auth type - redirect_url_obj = redirect_url_obj._replace( - path=re.sub(r"/auth$", "/auth/local", redirect_url_obj.path) - ) - - # if we are at `/auth/xxxx/login` path, then no further action is needed (we can use it for login POST) - if re.search(r"/auth/.*/login$", redirect_url_obj.path): - auth_session["dex_login_url"] = redirect_url_obj.geturl() - - # else, we need to be redirected to the actual login page - else: - # this GET should redirect us to the `/auth/xxxx/login` path - resp = s.get(redirect_url_obj.geturl(), allow_redirects=True) - if resp.status_code != 200: - raise RuntimeError( - f"HTTP status code '{resp.status_code}' for GET against: {redirect_url_obj.geturl()}" - ) - - # set the login url - auth_session["dex_login_url"] = resp.url - - ################ - # Attempt Dex Login - ################ - resp = s.post( - auth_session["dex_login_url"], - data={"login": username, "password": password}, - allow_redirects=True - ) - if len(resp.history) == 0: - raise RuntimeError( - f"Login credentials were probably invalid - " - f"No redirect after POST to: {auth_session['dex_login_url']}" - ) - - # store the session cookies in a "key1=value1; key2=value2" string - auth_session["session_cookie"] = "; ".join([f"{c.name}={c.value}" for c in s.cookies]) - - return auth_session \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py deleted file mode 100644 index 3c7c0dfdc..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py +++ /dev/null @@ -1,19 +0,0 @@ -import os -import requests - -from typing import TypedDict - -def retrieve_namespaces(host: str, auth_session: TypedDict) -> str: - workgroup_endpoint = os.path.join(host, "api/workgroup/env-info") - - cookies = {} - cookie_tokens = auth_session["session_cookie"].split("=") - print(cookie_tokens[0]) - cookies[cookie_tokens[0]]=cookie_tokens[1] - resp = requests.get(workgroup_endpoint, cookies=cookies) - if resp.status_code != 200: - raise RuntimeError( - f"HTTP status code '{resp.status_code}' for GET against: {workgroup_endpoint}" - ) - return [ns["namespace"] for ns in resp.json()["namespaces"] if - ns["role"]=="owner"] \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py deleted file mode 100644 index 2e4729bea..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py +++ /dev/null @@ -1,44 +0,0 @@ -from __future__ import print_function - -import time -import kfp_server_api -import os -import requests -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -print("available namespace: {}".format(namespaces)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.ExperimentServiceApi(api_client) - resource_reference_key_type = "NAMESPACE" - resource_reference_key_id = namespaces[0] - list_experiment_response = api_instance.list_experiment(resource_reference_key_type=resource_reference_key_type, resource_reference_key_id=resource_reference_key_id) - for experiment in list_experiment_response.experiments: - pprint(experiment) diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py deleted file mode 100644 index 6e2be4b5e..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py +++ /dev/null @@ -1,43 +0,0 @@ -from __future__ import print_function -import string -import time -import kfp_server_api -import os -import requests -import kfp -import json -from pprint import pprint -from kfp_server_api.rest import ApiException -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - api_instance = kfp_server_api.PipelineServiceApi(api_client) - list_pipeline_response = api_instance.list_pipelines() - for pipelines in list_pipeline_response.pipelines: - print(pipelines) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py b/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py deleted file mode 100644 index 410cf50aa..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py +++ /dev/null @@ -1,52 +0,0 @@ -from __future__ import print_function -import string -import random -import time -import kfp_server_api -import os -import requests -from kfp_server_api.rest import ApiException - -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - - -host = os.getenv("KUBEFLOW_HOST") -username = os.getenv("KUBEFLOW_USERNAME") -password = os.getenv("KUBEFLOW_PASSWORD") - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -print("available namespace: {}".format(namespaces)) - -def random_suffix() -> string: - return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) - uploadfile='' # The yaml file in your local path. - name='pipeline-' + random_suffix() - description='' # str | The description of pipeline. - try: - api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) - print(api_response) - except ApiException as e: - print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py b/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py deleted file mode 100644 index 16293f1d6..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py +++ /dev/null @@ -1,132 +0,0 @@ -from __future__ import print_function - -import time -import kfp_server_api -import os -import requests -import string -import random -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = "http://ai4edu.thu01.footprint-ai.com" -username = "thu02" -password = "M*$3sNF7" - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -def random_suffix() -> string: - return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the Experiment API class - experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) - name="experiment-" + random_suffix() - description="This is a experiment for only_decision_tree." - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey( - type=kfp_server_api.models.ApiResourceType.NAMESPACE, - id=resource_reference_key_id - ), - relationship=kfp_server_api.models.ApiRelationship.OWNER - )] - body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. - try: - # Creates a new experiment. - experiment_api_response = experiment_api_instance.create_experiment(body) - experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) - - # Create an instance of the pipeline API class - api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) - uploadfile='pipelines/only_decision_tree.yaml' - name='pipeline-' + random_suffix() - description="This is a only_decision_tree pipline." - try: - pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) - pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) - - # Create an instance of the run API class - run_api_instance = kfp_server_api.RunServiceApi(api_client) - display_name = 'run_only_decision_tree' + random_suffix() - description = "This is a only_decision_tree run." - pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), - relationship=kfp_server_api.models.ApiRelationship.OWNER )] - body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | - try: - # Creates a new run. - run_api_response = run_api_instance.create_run(body) - run_id = run_api_response.run.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling RunServiceApi->create_run: %s\n" % e) - - Completed_flag = False - polling_interval = 10 # Time in seconds between polls - - while not Completed_flag: - try: - time.sleep(1) - # Finds a specific run by ID. - api_instance = run_api_instance.get_run(run_id) - output = api_instance.pipeline_runtime.workflow_manifest - output = json.loads(output) - - try: - nodes = output['status']['nodes'] - conditions = output['status']['conditions'] # Comfirm completion. - - except KeyError: - nodes = {} - conditions = [] - - output_value = None - Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False - - except ApiException as e: - print("Exception when calling RunServiceApi->get_run: %s\n" % e) - break - - if not Completed_flag: - print("Pipeline is still running. Waiting...") - time.sleep(polling_interval-1) - - for node_id, node in nodes.items(): - if 'inputs' in node and 'parameters' in node['inputs']: - for parameter in node['inputs']['parameters']: - if parameter['name'] == 'decision-tree-classifier-Accuracy': - output_value = parameter['value'] - - if output_value is not None: - print(f"Decision Tree Classifier Accuracy: {output_value}") - else: - print("Parameter not found.") - print(nodes) diff --git a/kubeflow/examples/1.connect-kubeflow/py/fl.py b/kubeflow/examples/1.connect-kubeflow/py/fl.py deleted file mode 100644 index 6aeb83f9d..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/fl.py +++ /dev/null @@ -1,132 +0,0 @@ -from __future__ import print_function - -import time -import kfp_server_api -import os -import requests -import string -import random -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = "https://ai4edu.thu01.footprint-ai.com" -username = "thu02" -password = "M*$3sNF7" - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -def random_suffix() -> string: - return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the Experiment API class - experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) - name="experiment-" + random_suffix() - description="This is a experiment for fl." - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey( - type=kfp_server_api.models.ApiResourceType.NAMESPACE, - id=resource_reference_key_id - ), - relationship=kfp_server_api.models.ApiRelationship.OWNER - )] - body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. - try: - # Creates a new experiment. - experiment_api_response = experiment_api_instance.create_experiment(body) - experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) - - # Create an instance of the pipeline API class - api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) - uploadfile='pipelines/fl_pipeline.yaml' - name='pipeline-' + random_suffix() - description="This is a fl pipline." - try: - pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) - pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) - - # Create an instance of the run API class - run_api_instance = kfp_server_api.RunServiceApi(api_client) - display_name = 'fl' + random_suffix() - description = "This is a fl run." - pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), - relationship=kfp_server_api.models.ApiRelationship.OWNER )] - body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | - try: - # Creates a new run. - run_api_response = run_api_instance.create_run(body) - run_id = run_api_response.run.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling RunServiceApi->create_run: %s\n" % e) - - Completed_flag = False - polling_interval = 10 # Time in seconds between polls - - while not Completed_flag: - try: - time.sleep(1) - # Finds a specific run by ID. - api_instance = run_api_instance.get_run(run_id) - output = api_instance.pipeline_runtime.workflow_manifest - output = json.loads(output) - - try: - nodes = output['status']['nodes'] - conditions = output['status']['conditions'] # Comfirm completion. - - except KeyError: - nodes = {} - conditions = [] - - output_value = None - Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False - - except ApiException as e: - print("Exception when calling RunServiceApi->get_run: %s\n" % e) - break - - if not Completed_flag: - print("Pipeline is still running. Waiting...") - time.sleep(polling_interval-1) - - for node_id, node in nodes.items(): - if 'inputs' in node and 'parameters' in node['inputs']: - for parameter in node['inputs']['parameters']: - if parameter['name'] == 'client-last_accuracy': - output_value = parameter['value'] - - if output_value is not None: - print(f"fl Accuracy: {output_value}") - else: - print("Parameter not found.") - print(nodes) diff --git a/kubeflow/examples/1.connect-kubeflow/py/helloworld.py b/kubeflow/examples/1.connect-kubeflow/py/helloworld.py deleted file mode 100644 index 97e823fb5..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/helloworld.py +++ /dev/null @@ -1,6 +0,0 @@ -import sys - -while True: - line = sys.stdin.readline() - print('this is send from python') - print(line) diff --git a/kubeflow/examples/1.connect-kubeflow/py/install.py b/kubeflow/examples/1.connect-kubeflow/py/install.py deleted file mode 100644 index 470d38321..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/install.py +++ /dev/null @@ -1,23 +0,0 @@ -import os -import subprocess - -def python3_version(): - return subprocess.check_call(["python3", "--version"]) - -def which(command): - return subprocess.check_call(["which", command]) - -def pip3_install_requirements(): - return subprocess.check_call(["pip3", "install", "-r", "requirements.txt", - "--user"]) - -def pip3_install_kfp(): - return subprocess.check_call(["pip3", "install", - "git+https://github.com/kubeflow/pipelines.git@1.8.19#subdirectory=backend/api/python_http_client", - "--user"]) - -python3_version() -pip3_install_requirements() -pip3_install_kfp() - -print("done") diff --git a/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py b/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py deleted file mode 100644 index da7ef822f..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py +++ /dev/null @@ -1,44 +0,0 @@ -from __future__ import print_function - -import time -import kfp_server_api -import os -import requests -from kfp_server_api.rest import ApiException - -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = "http://ai4edu.thu01.footprint-ai.com" -username = "thu02" -password = "M*$3sNF7" - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -print("available namespace: {}".format(namespaces)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the API class - api_instance = kfp_server_api.ExperimentServiceApi(api_client) - resource_reference_key_type = "NAMESPACE" - resource_reference_key_id = namespaces[0] - list_experiment_response = api_instance.list_experiment(resource_reference_key_type=resource_reference_key_type, resource_reference_key_id=resource_reference_key_id) - for experiment in list_experiment_response.experiments: - print(experiment) diff --git a/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py b/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py deleted file mode 100644 index 7c95abcf0..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py +++ /dev/null @@ -1,93 +0,0 @@ -import re -from urllib.parse import urlsplit - -import requests - -# NOTE: the following code is referred from https://github.com/kubeflow/website/issues/2916 -def get_istio_auth_session(url: str, username: str, password: str) -> dict: - """ - Determine if the specified URL is secured by Dex and try to obtain a session cookie. - WARNING: only Dex `staticPasswords` and `LDAP` authentication are currently supported - (we default default to using `staticPasswords` if both are enabled) - - :param url: Kubeflow server URL, including protocol - :param username: Dex `staticPasswords` or `LDAP` username - :param password: Dex `staticPasswords` or `LDAP` password - :return: auth session information - """ - # define the default return object - auth_session = { - "endpoint_url": url, # KF endpoint URL - "redirect_url": None, # KF redirect URL, if applicable - "dex_login_url": None, # Dex login URL (for POST of credentials) - "is_secured": None, # True if KF endpoint is secured - "session_cookie": None # Resulting session cookies in the form "key1=value1; key2=value2" - } - - # use a persistent session (for cookies) - with requests.Session() as s: - - ################ - # Determine if Endpoint is Secured - ################ - resp = s.get(url, allow_redirects=True) - if resp.status_code != 200: - raise RuntimeError( - f"HTTP status code '{resp.status_code}' for GET against: {url}" - ) - - auth_session["redirect_url"] = resp.url - - # if we were NOT redirected, then the endpoint is UNSECURED - if len(resp.history) == 0: - auth_session["is_secured"] = False - return auth_session - else: - auth_session["is_secured"] = True - - ################ - # Get Dex Login URL - ################ - redirect_url_obj = urlsplit(auth_session["redirect_url"]) - - # if we are at `/auth?=xxxx` path, we need to select an auth type - if re.search(r"/auth$", redirect_url_obj.path): - # default to "staticPasswords" auth type - redirect_url_obj = redirect_url_obj._replace( - path=re.sub(r"/auth$", "/auth/local", redirect_url_obj.path) - ) - - # if we are at `/auth/xxxx/login` path, then no further action is needed (we can use it for login POST) - if re.search(r"/auth/.*/login$", redirect_url_obj.path): - auth_session["dex_login_url"] = redirect_url_obj.geturl() - - # else, we need to be redirected to the actual login page - else: - # this GET should redirect us to the `/auth/xxxx/login` path - resp = s.get(redirect_url_obj.geturl(), allow_redirects=True) - if resp.status_code != 200: - raise RuntimeError( - f"HTTP status code '{resp.status_code}' for GET against: {redirect_url_obj.geturl()}" - ) - - # set the login url - auth_session["dex_login_url"] = resp.url - - ################ - # Attempt Dex Login - ################ - resp = s.post( - auth_session["dex_login_url"], - data={"login": username, "password": password}, - allow_redirects=True - ) - if len(resp.history) == 0: - raise RuntimeError( - f"Login credentials were probably invalid - " - f"No redirect after POST to: {auth_session['dex_login_url']}" - ) - - # store the session cookies in a "key1=value1; key2=value2" string - auth_session["session_cookie"] = "; ".join([f"{c.name}={c.value}" for c in s.cookies]) - - return auth_session \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py b/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py deleted file mode 100644 index 3c7c0dfdc..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py +++ /dev/null @@ -1,19 +0,0 @@ -import os -import requests - -from typing import TypedDict - -def retrieve_namespaces(host: str, auth_session: TypedDict) -> str: - workgroup_endpoint = os.path.join(host, "api/workgroup/env-info") - - cookies = {} - cookie_tokens = auth_session["session_cookie"].split("=") - print(cookie_tokens[0]) - cookies[cookie_tokens[0]]=cookie_tokens[1] - resp = requests.get(workgroup_endpoint, cookies=cookies) - if resp.status_code != 200: - raise RuntimeError( - f"HTTP status code '{resp.status_code}' for GET against: {workgroup_endpoint}" - ) - return [ns["namespace"] for ns in resp.json()["namespaces"] if - ns["role"]=="owner"] \ No newline at end of file diff --git a/kubeflow/examples/1.connect-kubeflow/py/mnist.py b/kubeflow/examples/1.connect-kubeflow/py/mnist.py deleted file mode 100644 index fc2911a05..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/mnist.py +++ /dev/null @@ -1,132 +0,0 @@ -from __future__ import print_function - -import time -import kfp_server_api -import os -import requests -import string -import random -import json -from kfp_server_api.rest import ApiException -from pprint import pprint -from kfp_login import get_istio_auth_session -from kfp_namespace import retrieve_namespaces - -host = "https://ai4edu.thu01.footprint-ai.com" -username = "thu02" -password = "M*$3sNF7" - -auth_session = get_istio_auth_session( - url=host, - username=username, - password=password - ) - -# The client must configure the authentication and authorization parameters -# in accordance with the API server security policy. -# Examples for each auth method are provided below, use the example that -# satisfies your auth use case. - -# Configure API key authorization: Bearer -configuration = kfp_server_api.Configuration( - host = os.path.join(host, "pipeline"), -) -configuration.debug = True - -namespaces = retrieve_namespaces(host, auth_session) -#print("available namespace: {}".format(namespaces)) - -def random_suffix() -> string: - return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) - -# Enter a context with an instance of the API client -with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: - # Create an instance of the Experiment API class - experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) - name="experiment-" + random_suffix() - description="This is a experiment for mnist." - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey( - type=kfp_server_api.models.ApiResourceType.NAMESPACE, - id=resource_reference_key_id - ), - relationship=kfp_server_api.models.ApiRelationship.OWNER - )] - body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. - try: - # Creates a new experiment. - experiment_api_response = experiment_api_instance.create_experiment(body) - experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) - - # Create an instance of the pipeline API class - api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) - uploadfile='pipelines/mnist_pipeline.yaml' - name='pipeline-' + random_suffix() - description="This is a mnist pipline." - try: - pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) - pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) - - # Create an instance of the run API class - run_api_instance = kfp_server_api.RunServiceApi(api_client) - display_name = 'mnist' + random_suffix() - description = "This is a mnist run." - pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) - resource_reference_key_id = namespaces[0] - resource_references=[kfp_server_api.models.ApiResourceReference( - key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), - relationship=kfp_server_api.models.ApiRelationship.OWNER )] - body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | - try: - # Creates a new run. - run_api_response = run_api_instance.create_run(body) - run_id = run_api_response.run.id # str | The ID of the run to be retrieved. - except ApiException as e: - print("Exception when calling RunServiceApi->create_run: %s\n" % e) - - Completed_flag = False - polling_interval = 10 # Time in seconds between polls - - while not Completed_flag: - try: - time.sleep(1) - # Finds a specific run by ID. - api_instance = run_api_instance.get_run(run_id) - output = api_instance.pipeline_runtime.workflow_manifest - output = json.loads(output) - - try: - nodes = output['status']['nodes'] - conditions = output['status']['conditions'] # Comfirm completion. - - except KeyError: - nodes = {} - conditions = [] - - output_value = None - Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False - - except ApiException as e: - print("Exception when calling RunServiceApi->get_run: %s\n" % e) - break - - if not Completed_flag: - print("Pipeline is still running. Waiting...") - time.sleep(polling_interval-1) - - for node_id, node in nodes.items(): - if 'inputs' in node and 'parameters' in node['inputs']: - for parameter in node['inputs']['parameters']: - if parameter['name'] == 'client-last_accuracy': - output_value = parameter['value'] - - if output_value is not None: - print(f"fl Accuracy: {output_value}") - else: - print("Parameter not found.") - print(nodes) diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml deleted file mode 100644 index 11f53462f..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml +++ /dev/null @@ -1,651 +0,0 @@ -apiVersion: argoproj.io/v1alpha1 -kind: Workflow -metadata: - generateName: fl-test- - annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2024-01-02T05:53:41.650368', - pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "kubeflow-user-thu01", - "name": "namespace", "optional": true}], "name": "FL test"}'} - labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} -spec: - entrypoint: fl-test - templates: - - name: client - container: - args: [--batch, '1', '----output-paths', /tmp/outputs/last_accuracy/data] - command: - - sh - - -c - - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install - --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - "def client(batch):\n import json\n import requests\n import time\n\ - \ import pandas as pd\n import numpy as np\n import tensorflow as\ - \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ - \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ - \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ - \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ - \ tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import\ - \ Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras\ - \ import backend as K\n\n normal_url='https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'\ - \ \n abnormal_url='https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download'\n\ - \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ - \ num_features = len(normal_data.columns)\n print(num_features)\n \ - \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ - \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ - \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ - \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ - \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ - \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ - \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ - \n input_shape = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n\ - \ @staticmethod\n def build(shape, classes):\n model\ - \ = Sequential()\n model.add(LSTM(units=64, input_shape=input_shape,\ - \ return_sequences=True))\n model.add(Dropout(0.2))\n \ - \ model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n \ - \ model.add(Dense(units=num_classes, activation='softmax'))\n\n \ - \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ - \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ - \ #The client should have its own data, not like this. It's a lazy method.\n\ - \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ - \n #seperate shard into data and labels lists\n data, label\ - \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ - \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ - \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ - \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ - \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ - \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ - \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ - \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ - \ \n optimizer=optimizer, \n metrics=metrics)\n\ - \n if(comm_round == 0):\n history = client_model.fit(dataset,\ - \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ - \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ - \ test_loss, test_accuracy = client_model.evaluate(dataset)\n \ - \ print(f'Test accuracy: {test_accuracy}')\n\n local_weight = client_model.get_weights()\n\ - \ local_weight = [np.array(w).tolist() for w in local_weight]\n\n \ - \ client_data = {\"local_count\": local_count,'bs': bs, 'local_weight':\ - \ json.dumps(local_weight)}\n\n while True:\n try:\n \ - \ weight = (requests.post(server_url,data=client_data))\n\n \ - \ if weight.status_code == 200:\n print(f\"\ - exist\")\n\n break\n else:\n \ - \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ - \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ - \ data = weight.json()\n avg_weight = data.get('result')\n \ - \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ - \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ - \ \n try:\n response = requests.get(shutdown_url)\n except\ - \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ - \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ - \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ - \ str:\n if isinstance(float_value, str):\n return float_value\n\ - \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ - \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ - \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ - _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ - --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ - _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ - \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ - _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ - \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ - \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ - \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" - image: tensorflow/tensorflow - resources: - limits: {cpu: '0.2'} - requests: {cpu: '0.2'} - outputs: - parameters: - - name: client-last_accuracy - valueFrom: {path: /tmp/outputs/last_accuracy/data} - artifacts: - - {name: client-last_accuracy, path: /tmp/outputs/last_accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": - "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 - python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' - || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def client(batch):\n import json\n import requests\n import time\n import - pandas as pd\n import numpy as np\n import tensorflow as tf\n from - tensorflow.keras.models import Sequential\n from tensorflow.keras.layers - import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from - tensorflow.keras.layers import Activation\n from tensorflow.keras.layers - import Flatten\n from tensorflow.keras.layers import Dense\n from - tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import - Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras - import backend as K\n\n normal_url=''https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'' - \n abnormal_url=''https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download''\n normal_data - = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features - = len(normal_data.columns)\n print(num_features)\n normal_label = - np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, - 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label - = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data - = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), - num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data - = list(zip(data, data_label))\n data_length=len(full_data)\n\n input_shape - = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n @staticmethod\n def - build(shape, classes):\n model = Sequential()\n model.add(LSTM(units=64, - input_shape=input_shape, return_sequences=True))\n model.add(Dropout(0.2))\n model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n model.add(Dense(units=num_classes, - activation=''softmax''))\n\n return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] - #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] - #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data - len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate - shard into data and labels lists\n data, label = zip(*data_shard)\n dataset - = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return - dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs - = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics - = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for - comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model - = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, - \n metrics=metrics)\n\n if(comm_round == 0):\n history - = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history - = client_model.fit(dataset, epochs=5, verbose=1)\n\n test_loss, test_accuracy - = client_model.evaluate(dataset)\n print(f''Test accuracy: {test_accuracy}'')\n\n local_weight - = client_model.get_weights()\n local_weight = [np.array(w).tolist() - for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': - bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight - = (requests.post(server_url,data=client_data))\n\n if weight.status_code - == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server - error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not - exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight - = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight - = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response - = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already - shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef - _serialize_float(float_value: float) -> str:\n if isinstance(float_value, - str):\n return float_value\n if not isinstance(float_value, (float, - int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of - float.''.format(\n str(float_value), str(type(float_value))))\n return - str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', - description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, - required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", - dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files - = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers - = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except - OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], - "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": - "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": - "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": - "1"}'} - - name: client-2 - container: - args: [--batch, '2', '----output-paths', /tmp/outputs/last_accuracy/data] - command: - - sh - - -c - - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install - --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - "def client(batch):\n import json\n import requests\n import time\n\ - \ import pandas as pd\n import numpy as np\n import tensorflow as\ - \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ - \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ - \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ - \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ - \ tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import\ - \ Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras\ - \ import backend as K\n\n normal_url='https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'\ - \ \n abnormal_url='https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download'\n\ - \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ - \ num_features = len(normal_data.columns)\n print(num_features)\n \ - \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ - \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ - \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ - \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ - \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ - \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ - \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ - \n input_shape = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n\ - \ @staticmethod\n def build(shape, classes):\n model\ - \ = Sequential()\n model.add(LSTM(units=64, input_shape=input_shape,\ - \ return_sequences=True))\n model.add(Dropout(0.2))\n \ - \ model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n \ - \ model.add(Dense(units=num_classes, activation='softmax'))\n\n \ - \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ - \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ - \ #The client should have its own data, not like this. It's a lazy method.\n\ - \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ - \n #seperate shard into data and labels lists\n data, label\ - \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ - \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ - \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ - \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ - \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ - \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ - \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ - \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ - \ \n optimizer=optimizer, \n metrics=metrics)\n\ - \n if(comm_round == 0):\n history = client_model.fit(dataset,\ - \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ - \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ - \ test_loss, test_accuracy = client_model.evaluate(dataset)\n \ - \ print(f'Test accuracy: {test_accuracy}')\n\n local_weight = client_model.get_weights()\n\ - \ local_weight = [np.array(w).tolist() for w in local_weight]\n\n \ - \ client_data = {\"local_count\": local_count,'bs': bs, 'local_weight':\ - \ json.dumps(local_weight)}\n\n while True:\n try:\n \ - \ weight = (requests.post(server_url,data=client_data))\n\n \ - \ if weight.status_code == 200:\n print(f\"\ - exist\")\n\n break\n else:\n \ - \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ - \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ - \ data = weight.json()\n avg_weight = data.get('result')\n \ - \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ - \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ - \ \n try:\n response = requests.get(shutdown_url)\n except\ - \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ - \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ - \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ - \ str:\n if isinstance(float_value, str):\n return float_value\n\ - \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ - \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ - \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ - _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ - --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ - _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ - \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ - _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ - \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ - \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ - \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" - image: tensorflow/tensorflow - resources: - limits: {cpu: '0.2'} - requests: {cpu: '0.2'} - outputs: - artifacts: - - {name: client-2-last_accuracy, path: /tmp/outputs/last_accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": - "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 - python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' - || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def client(batch):\n import json\n import requests\n import time\n import - pandas as pd\n import numpy as np\n import tensorflow as tf\n from - tensorflow.keras.models import Sequential\n from tensorflow.keras.layers - import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from - tensorflow.keras.layers import Activation\n from tensorflow.keras.layers - import Flatten\n from tensorflow.keras.layers import Dense\n from - tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import - Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras - import backend as K\n\n normal_url=''https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'' - \n abnormal_url=''https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download''\n normal_data - = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features - = len(normal_data.columns)\n print(num_features)\n normal_label = - np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, - 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label - = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data - = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), - num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data - = list(zip(data, data_label))\n data_length=len(full_data)\n\n input_shape - = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n @staticmethod\n def - build(shape, classes):\n model = Sequential()\n model.add(LSTM(units=64, - input_shape=input_shape, return_sequences=True))\n model.add(Dropout(0.2))\n model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n model.add(Dense(units=num_classes, - activation=''softmax''))\n\n return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] - #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] - #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data - len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate - shard into data and labels lists\n data, label = zip(*data_shard)\n dataset - = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return - dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs - = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics - = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for - comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model - = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, - \n metrics=metrics)\n\n if(comm_round == 0):\n history - = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history - = client_model.fit(dataset, epochs=5, verbose=1)\n\n test_loss, test_accuracy - = client_model.evaluate(dataset)\n print(f''Test accuracy: {test_accuracy}'')\n\n local_weight - = client_model.get_weights()\n local_weight = [np.array(w).tolist() - for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': - bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight - = (requests.post(server_url,data=client_data))\n\n if weight.status_code - == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server - error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not - exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight - = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight - = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response - = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already - shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef - _serialize_float(float_value: float) -> str:\n if isinstance(float_value, - str):\n return float_value\n if not isinstance(float_value, (float, - int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of - float.''.format(\n str(float_value), str(type(float_value))))\n return - str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', - description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, - required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", - dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files - = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers - = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except - OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], - "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": - "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": - "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": - "2"}'} - - name: delete-service - resource: - action: delete - flags: [--wait=false] - manifest: | - apiVersion: v1 - kind: Service - metadata: - name: http-service - spec: - ports: - - port: 80 - protocol: TCP - targetPort: 8080 - selector: - app: http-service - type: NodePort - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - - name: fl-test - dag: - tasks: - - {name: client, template: client} - - {name: client-2, template: client-2} - - name: delete-service - template: delete-service - dependencies: [server] - - {name: http-service, template: http-service} - - name: server - template: server - dependencies: [http-service] - - name: show-results - template: show-results - dependencies: [client] - arguments: - parameters: - - {name: client-last_accuracy, value: '{{tasks.client.outputs.parameters.client-last_accuracy}}'} - - name: http-service - resource: - action: create - manifest: | - apiVersion: v1 - kind: Service - metadata: - name: http-service - spec: - ports: - - port: 5000 - protocol: TCP - targetPort: 8080 - selector: - app: http-service - outputs: - parameters: - - name: http-service-manifest - valueFrom: {jsonPath: '{}'} - - name: http-service-name - valueFrom: {jsonPath: '{.metadata.name}'} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - - name: server - container: - args: [] - command: - - sh - - -c - - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - 'flask' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install - --quiet --no-warn-script-location 'flask' 'pandas' --user) && "$0" "$@" - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - "def server():\n import json\n import pandas as pd\n import numpy\ - \ as np\n import pickle\n import threading\n import time\n import\ - \ tensorflow as tf\n from flask import Flask, jsonify,request\n import\ - \ os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list\ - \ = []\n global_value = { #Share variable\n 'last_run_statue'\ - \ : False, #last run finish or not\n 'data_statue' : None,\ - \ #global_count finish or not\n 'global_count' : None,\n\ - \ 'scale_statue' : None,\n 'weight_statue'\ - \ : None,\n 'average_weights' : None,\n \ - \ 'shutdown' : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n \ - \ init_lock = threading.Lock()\n clients_local_count_lock = threading.Lock()\n\ - \ scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock\ - \ = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n\ - \ def before_request():\n print('get request')\n\n @app.route('/data',\ - \ methods=['POST'])\n def flask_server():\n with init_lock: #check\ - \ last run is finish and init varible\n\n while True:\n\n \ - \ if(len(clients_local_count)==0 and global_value['last_run_statue']\ - \ == False):#init the variable by first client enter\n \ - \ global_value['last_run_statue'] = True\n global_value['data_statue']\ - \ = False\n global_value['scale_statue'] = False\n \ - \ global_value['weight_statue'] = False\n \ - \ break\n\n elif(global_value['last_run_statue'] == True):\n\ - \ break\n time.sleep(3)\n\n local_count\ - \ = int(request.form.get('local_count')) #get data\n bs =\ - \ int(request.form.get('bs'))\n local_weight = json.loads(request.form.get('local_weight'))\n\ - \ local_weight = [np.array(lst) for lst in local_weight]\n\n \ - \ def scale_model_weights(weight, scalar):\n weight_final = []\n\ - \ steps = len(weight)\n for i in range(steps):\n \ - \ weight_final.append(scalar * weight[i])\n return\ - \ weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n \ - \ avg_grad = list()\n #get the average grad accross all\ - \ client gradients\n for grad_list_tuple in zip(*scaled_weight_list):\n\ - \ layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)\n\ - \ avg_grad.append(layer_mean)\n\n return avg_grad\n\ - \n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\ - \n with scaled_local_weight_list_lock:\n while True:\n\n\ - \ if (len(clients_local_count) == NUM_OF_CLIENTS and global_value['data_statue']\ - \ != True):\n global_value['last_run_statue'] = False\n\ - \ sum_of_local_count=sum(clients_local_count)\n\n \ - \ global_value['global_count'] = sum_of_local_count \n\n\ - \ scaling_factor=local_count/global_value['global_count']\n\ - \ scaled_weights = scale_model_weights(local_weight, scaling_factor)\n\ - \ scaled_local_weight_list.append(scaled_weights)\n\n \ - \ global_value['scale_statue'] = True \n \ - \ global_value['data_statue'] = True\n break\n \ - \ elif (global_value['data_statue'] == True and global_value['scale_statue']\ - \ == True):\n scaling_factor=local_count/global_value['global_count']\n\ - \ scaled_weights =scale_model_weights(local_weight, scaling_factor)\n\ - \ scaled_local_weight_list.append(scaled_weights)\n\n \ - \ break\n time.sleep(1)\n\n with cal_weight_lock:\n\ - \n while True:\n if(len(scaled_local_weight_list)\ - \ == NUM_OF_CLIENTS and global_value['weight_statue'] != True):\n\n \ - \ global_value['average_weights'] = sum_scaled_weights(scaled_local_weight_list)\n\ - \ global_value['weight_statue'] = True\n \ - \ global_value['average_weights'] = json.dumps([np.array(w).tolist()\ - \ for w in global_value['average_weights']])\n\n break\n\ - \n elif(global_value['weight_statue'] == True):\n\n \ - \ break\n\n time.sleep(1)\n\n clients_local_count.clear()\n\ - \ scaled_local_weight_list.clear()\n\n return jsonify({'result':\ - \ (global_value['average_weights'])})\n\n @app.route('/shutdown', methods=['GET'])\n\ - \ def shutdown_server():\n global_value['shutdown'] +=1 \n \ - \ with shutdown_lock:\n while True:\n if(global_value['shutdown']\ - \ == NUM_OF_CLIENTS):\n os._exit(0)\n \ - \ return 'Server shutting down...'\n time.sleep(1)\n\n \ - \ app.run(host=\"0.0.0.0\", port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Server',\ - \ description='')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs =\ - \ server(**_parsed_args)\n" - image: tensorflow/tensorflow - ports: - - {containerPort: 8080, name: my-port} - resources: - limits: {cpu: '0.2'} - requests: {cpu: '0.2'} - metadata: - labels: - app: http-service - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": [], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 - -m pip install --quiet --no-warn-script-location ''flask'' ''pandas'' || - PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - ''flask'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def server():\n import json\n import pandas as pd\n import numpy - as np\n import pickle\n import threading\n import time\n import - tensorflow as tf\n from flask import Flask, jsonify,request\n import - os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list - = []\n global_value = { #Share variable\n ''last_run_statue'' - : False, #last run finish or not\n ''data_statue'' : - None, #global_count finish or not\n ''global_count'' - : None,\n ''scale_statue'' : None,\n ''weight_statue'' - : None,\n ''average_weights'' : None,\n ''shutdown'' - : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n init_lock = threading.Lock()\n clients_local_count_lock - = threading.Lock()\n scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock - = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n def - before_request():\n print(''get request'')\n\n @app.route(''/data'', - methods=[''POST''])\n def flask_server():\n with init_lock: #check - last run is finish and init varible\n\n while True:\n\n if(len(clients_local_count)==0 - and global_value[''last_run_statue''] == False):#init the variable by first - client enter\n global_value[''last_run_statue''] = True\n global_value[''data_statue''] - = False\n global_value[''scale_statue''] = False\n global_value[''weight_statue''] - = False\n break\n\n elif(global_value[''last_run_statue''] - == True):\n break\n time.sleep(3)\n\n local_count - = int(request.form.get(''local_count'')) #get data\n bs - = int(request.form.get(''bs''))\n local_weight = json.loads(request.form.get(''local_weight''))\n local_weight - = [np.array(lst) for lst in local_weight]\n\n def scale_model_weights(weight, - scalar):\n weight_final = []\n steps = len(weight)\n for - i in range(steps):\n weight_final.append(scalar * weight[i])\n return - weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n avg_grad - = list()\n #get the average grad accross all client gradients\n for - grad_list_tuple in zip(*scaled_weight_list):\n layer_mean - = tf.math.reduce_sum(grad_list_tuple, axis=0)\n avg_grad.append(layer_mean)\n\n return - avg_grad\n\n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\n with - scaled_local_weight_list_lock:\n while True:\n\n if - (len(clients_local_count) == NUM_OF_CLIENTS and global_value[''data_statue''] - != True):\n global_value[''last_run_statue''] = False\n sum_of_local_count=sum(clients_local_count)\n\n global_value[''global_count''] - = sum_of_local_count \n\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights - = scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n global_value[''scale_statue''] - = True \n global_value[''data_statue''] = True\n break\n elif - (global_value[''data_statue''] == True and global_value[''scale_statue''] - == True):\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights - =scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n break\n time.sleep(1)\n\n with - cal_weight_lock:\n\n while True:\n if(len(scaled_local_weight_list) - == NUM_OF_CLIENTS and global_value[''weight_statue''] != True):\n\n global_value[''average_weights''] - = sum_scaled_weights(scaled_local_weight_list)\n global_value[''weight_statue''] - = True\n global_value[''average_weights''] = json.dumps([np.array(w).tolist() - for w in global_value[''average_weights'']])\n\n break\n\n elif(global_value[''weight_statue''] - == True):\n\n break\n\n time.sleep(1)\n\n clients_local_count.clear()\n scaled_local_weight_list.clear()\n\n return - jsonify({''result'': (global_value[''average_weights''])})\n\n @app.route(''/shutdown'', - methods=[''GET''])\n def shutdown_server():\n global_value[''shutdown''] - +=1 \n with shutdown_lock:\n while True:\n if(global_value[''shutdown''] - == NUM_OF_CLIENTS):\n os._exit(0)\n return - ''Server shutting down...''\n time.sleep(1)\n\n app.run(host=\"0.0.0.0\", - port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Server'', - description='''')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs - = server(**_parsed_args)\n"], "image": "tensorflow/tensorflow"}}, "name": - "Server"}', pipelines.kubeflow.org/component_ref: '{}'} - - name: show-results - container: - args: [--test-acc, '{{inputs.parameters.client-last_accuracy}}', '----output-paths', - /tmp/outputs/test_accuracy/data] - command: - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - | - def show_results(test_acc): - return([test_acc]) - - def _serialize_float(float_value: float) -> str: - if isinstance(float_value, str): - return float_value - if not isinstance(float_value, (float, int)): - raise TypeError('Value "{}" has type "{}" instead of float.'.format( - str(float_value), str(type(float_value)))) - return str(float_value) - - import argparse - _parser = argparse.ArgumentParser(prog='Show results', description='') - _parser.add_argument("--test-acc", dest="test_acc", type=float, required=True, default=argparse.SUPPRESS) - _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1) - _parsed_args = vars(_parser.parse_args()) - _output_files = _parsed_args.pop("_output_paths", []) - - _outputs = show_results(**_parsed_args) - - _output_serializers = [ - _serialize_float, - - ] - - import os - for idx, output_file in enumerate(_output_files): - try: - os.makedirs(os.path.dirname(output_file)) - except OSError: - pass - with open(output_file, 'w') as f: - f.write(_output_serializers[idx](_outputs[idx])) - image: python:3.7 - resources: - limits: {cpu: '0.2'} - requests: {cpu: '0.2'} - inputs: - parameters: - - {name: client-last_accuracy} - outputs: - artifacts: - - {name: show-results-test_accuracy, path: /tmp/outputs/test_accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--test-acc", {"inputValue": "test_acc"}, "----output-paths", - {"outputPath": "test_accuracy"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def show_results(test_acc):\n return([test_acc])\n\ndef _serialize_float(float_value: - float) -> str:\n if isinstance(float_value, str):\n return float_value\n if - not isinstance(float_value, (float, int)):\n raise TypeError(''Value - \"{}\" has type \"{}\" instead of float.''.format(\n str(float_value), - str(type(float_value))))\n return str(float_value)\n\nimport argparse\n_parser - = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--test-acc\", - dest=\"test_acc\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", - dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files - = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = show_results(**_parsed_args)\n\n_output_serializers - = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except - OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], - "image": "python:3.7"}}, "inputs": [{"name": "test_acc", "type": "Float"}], - "name": "Show results", "outputs": [{"name": "test_accuracy", "type": "Float"}]}', - pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"test_acc": - "{{inputs.parameters.client-last_accuracy}}"}'} - arguments: - parameters: - - {name: namespace, value: kubeflow-user-thu01} - serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml deleted file mode 100644 index 8381600f3..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml +++ /dev/null @@ -1,639 +0,0 @@ -apiVersion: argoproj.io/v1alpha1 -kind: Workflow -metadata: - generateName: fl-test- - annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-12-13T03:33:40.267552', - pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "kubeflow-user-thu01", - "name": "namespace", "optional": true}], "name": "FL test"}'} - labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} -spec: - entrypoint: fl-test - templates: - - name: client - container: - args: [--batch, '1', '----output-paths', /tmp/outputs/last_accuracy/data] - command: - - sh - - -c - - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install - --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - "def client(batch):\n import json\n import requests\n import time\n\ - \ import pandas as pd\n import numpy as np\n import tensorflow as\ - \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ - \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ - \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ - \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ - \ tensorflow.keras.optimizers import SGD\n from tensorflow.keras import\ - \ backend as K\n\n normal_url='https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'\ - \ \n abnormal_url='https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy'\n\ - \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ - \ num_features = len(normal_data.columns)\n print(num_features)\n \ - \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ - \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ - \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ - \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ - \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ - \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ - \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ - \n class SimpleMLP:\n @staticmethod\n def build(shape, classes):\n\ - \ model = Sequential()\n model.add(Conv1D(filters=4,\ - \ kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n\ - \ model.add(Flatten())\n model.add(Dense(8, activation=\"\ - relu\"))\n model.add(Dense(2, activation = 'softmax'))\n\n \ - \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ - \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ - \ #The client should have its own data, not like this. It's a lazy method.\n\ - \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ - \n #seperate shard into data and labels lists\n data, label\ - \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ - \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ - \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ - \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ - \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ - \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ - \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ - \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ - \ \n optimizer=optimizer, \n metrics=metrics)\n\ - \n if(comm_round == 0):\n history = client_model.fit(dataset,\ - \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ - \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ - \ local_weight = client_model.get_weights()\n local_weight =\ - \ [np.array(w).tolist() for w in local_weight]\n\n client_data = {\"\ - local_count\": local_count,'bs': bs, 'local_weight': json.dumps(local_weight)}\n\ - \n while True:\n try:\n weight = (requests.post(server_url,data=client_data))\n\ - \n if weight.status_code == 200:\n print(f\"\ - exist\")\n\n break\n else:\n \ - \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ - \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ - \ data = weight.json()\n avg_weight = data.get('result')\n \ - \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ - \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ - \ \n try:\n response = requests.get(shutdown_url)\n except\ - \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ - \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ - \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ - \ str:\n if isinstance(float_value, str):\n return float_value\n\ - \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ - \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ - \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ - _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ - --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ - _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ - \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ - _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ - \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ - \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ - \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" - image: tensorflow/tensorflow - resources: - limits: {cpu: '0.2'} - requests: {cpu: '0.2'} - outputs: - parameters: - - name: client-last_accuracy - valueFrom: {path: /tmp/outputs/last_accuracy/data} - artifacts: - - {name: client-last_accuracy, path: /tmp/outputs/last_accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": - "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 - python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' - || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def client(batch):\n import json\n import requests\n import time\n import - pandas as pd\n import numpy as np\n import tensorflow as tf\n from - tensorflow.keras.models import Sequential\n from tensorflow.keras.layers - import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from - tensorflow.keras.layers import Activation\n from tensorflow.keras.layers - import Flatten\n from tensorflow.keras.layers import Dense\n from - tensorflow.keras.optimizers import SGD\n from tensorflow.keras import - backend as K\n\n normal_url=''https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'' - \n abnormal_url=''https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy''\n normal_data - = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features - = len(normal_data.columns)\n print(num_features)\n normal_label = - np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, - 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label - = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data - = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), - num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data - = list(zip(data, data_label))\n data_length=len(full_data)\n\n class - SimpleMLP:\n @staticmethod\n def build(shape, classes):\n model - = Sequential()\n model.add(Conv1D(filters=4, kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n model.add(Flatten())\n model.add(Dense(8, - activation=\"relu\"))\n model.add(Dense(2, activation = ''softmax''))\n\n return - model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] - #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] - #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data - len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate - shard into data and labels lists\n data, label = zip(*data_shard)\n dataset - = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return - dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs - = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics - = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for - comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model - = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, - \n metrics=metrics)\n\n if(comm_round == 0):\n history - = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history - = client_model.fit(dataset, epochs=5, verbose=1)\n\n local_weight - = client_model.get_weights()\n local_weight = [np.array(w).tolist() - for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': - bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight - = (requests.post(server_url,data=client_data))\n\n if weight.status_code - == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server - error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not - exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight - = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight - = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response - = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already - shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef - _serialize_float(float_value: float) -> str:\n if isinstance(float_value, - str):\n return float_value\n if not isinstance(float_value, (float, - int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of - float.''.format(\n str(float_value), str(type(float_value))))\n return - str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', - description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, - required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", - dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files - = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers - = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except - OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], - "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": - "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": - "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": - "1"}'} - - name: client-2 - container: - args: [--batch, '2', '----output-paths', /tmp/outputs/last_accuracy/data] - command: - - sh - - -c - - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install - --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - "def client(batch):\n import json\n import requests\n import time\n\ - \ import pandas as pd\n import numpy as np\n import tensorflow as\ - \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ - \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ - \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ - \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ - \ tensorflow.keras.optimizers import SGD\n from tensorflow.keras import\ - \ backend as K\n\n normal_url='https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'\ - \ \n abnormal_url='https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy'\n\ - \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ - \ num_features = len(normal_data.columns)\n print(num_features)\n \ - \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ - \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ - \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ - \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ - \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ - \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ - \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ - \n class SimpleMLP:\n @staticmethod\n def build(shape, classes):\n\ - \ model = Sequential()\n model.add(Conv1D(filters=4,\ - \ kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n\ - \ model.add(Flatten())\n model.add(Dense(8, activation=\"\ - relu\"))\n model.add(Dense(2, activation = 'softmax'))\n\n \ - \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ - \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ - \ #The client should have its own data, not like this. It's a lazy method.\n\ - \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ - \n #seperate shard into data and labels lists\n data, label\ - \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ - \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ - \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ - \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ - \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ - \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ - \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ - \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ - \ \n optimizer=optimizer, \n metrics=metrics)\n\ - \n if(comm_round == 0):\n history = client_model.fit(dataset,\ - \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ - \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ - \ local_weight = client_model.get_weights()\n local_weight =\ - \ [np.array(w).tolist() for w in local_weight]\n\n client_data = {\"\ - local_count\": local_count,'bs': bs, 'local_weight': json.dumps(local_weight)}\n\ - \n while True:\n try:\n weight = (requests.post(server_url,data=client_data))\n\ - \n if weight.status_code == 200:\n print(f\"\ - exist\")\n\n break\n else:\n \ - \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ - \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ - \ data = weight.json()\n avg_weight = data.get('result')\n \ - \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ - \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ - \ \n try:\n response = requests.get(shutdown_url)\n except\ - \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ - \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ - \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ - \ str:\n if isinstance(float_value, str):\n return float_value\n\ - \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ - \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ - \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ - _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ - --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ - _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ - \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ - _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ - \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ - \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ - \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" - image: tensorflow/tensorflow - resources: - limits: {cpu: '0.2'} - requests: {cpu: '0.2'} - outputs: - artifacts: - - {name: client-2-last_accuracy, path: /tmp/outputs/last_accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": - "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 - python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' - || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def client(batch):\n import json\n import requests\n import time\n import - pandas as pd\n import numpy as np\n import tensorflow as tf\n from - tensorflow.keras.models import Sequential\n from tensorflow.keras.layers - import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from - tensorflow.keras.layers import Activation\n from tensorflow.keras.layers - import Flatten\n from tensorflow.keras.layers import Dense\n from - tensorflow.keras.optimizers import SGD\n from tensorflow.keras import - backend as K\n\n normal_url=''https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'' - \n abnormal_url=''https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy''\n normal_data - = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features - = len(normal_data.columns)\n print(num_features)\n normal_label = - np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, - 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label - = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data - = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), - num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data - = list(zip(data, data_label))\n data_length=len(full_data)\n\n class - SimpleMLP:\n @staticmethod\n def build(shape, classes):\n model - = Sequential()\n model.add(Conv1D(filters=4, kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n model.add(Flatten())\n model.add(Dense(8, - activation=\"relu\"))\n model.add(Dense(2, activation = ''softmax''))\n\n return - model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] - #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] - #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data - len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate - shard into data and labels lists\n data, label = zip(*data_shard)\n dataset - = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return - dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs - = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics - = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for - comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model - = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, - \n metrics=metrics)\n\n if(comm_round == 0):\n history - = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history - = client_model.fit(dataset, epochs=5, verbose=1)\n\n local_weight - = client_model.get_weights()\n local_weight = [np.array(w).tolist() - for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': - bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight - = (requests.post(server_url,data=client_data))\n\n if weight.status_code - == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server - error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not - exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight - = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight - = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response - = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already - shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef - _serialize_float(float_value: float) -> str:\n if isinstance(float_value, - str):\n return float_value\n if not isinstance(float_value, (float, - int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of - float.''.format(\n str(float_value), str(type(float_value))))\n return - str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', - description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, - required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", - dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files - = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers - = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except - OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], - "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": - "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": - "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": - "2"}'} - - name: delete-service - resource: - action: delete - flags: [--wait=false] - manifest: | - apiVersion: v1 - kind: Service - metadata: - name: http-service - spec: - ports: - - port: 80 - protocol: TCP - targetPort: 8080 - selector: - app: http-service - type: NodePort - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - - name: fl-test - dag: - tasks: - - {name: client, template: client} - - {name: client-2, template: client-2} - - name: delete-service - template: delete-service - dependencies: [server] - - {name: http-service, template: http-service} - - name: server - template: server - dependencies: [http-service] - - name: show-results - template: show-results - dependencies: [client] - arguments: - parameters: - - {name: client-last_accuracy, value: '{{tasks.client.outputs.parameters.client-last_accuracy}}'} - - name: http-service - resource: - action: create - manifest: | - apiVersion: v1 - kind: Service - metadata: - name: http-service - spec: - ports: - - port: 5000 - protocol: TCP - targetPort: 8080 - selector: - app: http-service - outputs: - parameters: - - name: http-service-manifest - valueFrom: {jsonPath: '{}'} - - name: http-service-name - valueFrom: {jsonPath: '{.metadata.name}'} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - - name: server - container: - args: [] - command: - - sh - - -c - - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - 'flask' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install - --quiet --no-warn-script-location 'flask' 'pandas' --user) && "$0" "$@" - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - "def server():\n import json\n import pandas as pd\n import numpy\ - \ as np\n import pickle\n import threading\n import time\n import\ - \ tensorflow as tf\n from flask import Flask, jsonify,request\n import\ - \ os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list\ - \ = []\n global_value = { #Share variable\n 'last_run_statue'\ - \ : False, #last run finish or not\n 'data_statue' : None,\ - \ #global_count finish or not\n 'global_count' : None,\n\ - \ 'scale_statue' : None,\n 'weight_statue'\ - \ : None,\n 'average_weights' : None,\n \ - \ 'shutdown' : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n \ - \ init_lock = threading.Lock()\n clients_local_count_lock = threading.Lock()\n\ - \ scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock\ - \ = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n\ - \ def before_request():\n print('get request')\n\n @app.route('/data',\ - \ methods=['POST'])\n def flask_server():\n with init_lock: #check\ - \ last run is finish and init varible\n\n while True:\n\n \ - \ if(len(clients_local_count)==0 and global_value['last_run_statue']\ - \ == False):#init the variable by first client enter\n \ - \ global_value['last_run_statue'] = True\n global_value['data_statue']\ - \ = False\n global_value['scale_statue'] = False\n \ - \ global_value['weight_statue'] = False\n \ - \ break\n\n elif(global_value['last_run_statue'] == True):\n\ - \ break\n time.sleep(3)\n\n local_count\ - \ = int(request.form.get('local_count')) #get data\n bs =\ - \ int(request.form.get('bs'))\n local_weight = json.loads(request.form.get('local_weight'))\n\ - \ local_weight = [np.array(lst) for lst in local_weight]\n\n \ - \ def scale_model_weights(weight, scalar):\n weight_final = []\n\ - \ steps = len(weight)\n for i in range(steps):\n \ - \ weight_final.append(scalar * weight[i])\n return\ - \ weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n \ - \ avg_grad = list()\n #get the average grad accross all\ - \ client gradients\n for grad_list_tuple in zip(*scaled_weight_list):\n\ - \ layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)\n\ - \ avg_grad.append(layer_mean)\n\n return avg_grad\n\ - \n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\ - \n with scaled_local_weight_list_lock:\n while True:\n\n\ - \ if (len(clients_local_count) == NUM_OF_CLIENTS and global_value['data_statue']\ - \ != True):\n global_value['last_run_statue'] = False\n\ - \ sum_of_local_count=sum(clients_local_count)\n\n \ - \ global_value['global_count'] = sum_of_local_count \n\n\ - \ scaling_factor=local_count/global_value['global_count']\n\ - \ scaled_weights = scale_model_weights(local_weight, scaling_factor)\n\ - \ scaled_local_weight_list.append(scaled_weights)\n\n \ - \ global_value['scale_statue'] = True \n \ - \ global_value['data_statue'] = True\n break\n \ - \ elif (global_value['data_statue'] == True and global_value['scale_statue']\ - \ == True):\n scaling_factor=local_count/global_value['global_count']\n\ - \ scaled_weights =scale_model_weights(local_weight, scaling_factor)\n\ - \ scaled_local_weight_list.append(scaled_weights)\n\n \ - \ break\n time.sleep(1)\n\n with cal_weight_lock:\n\ - \n while True:\n if(len(scaled_local_weight_list)\ - \ == NUM_OF_CLIENTS and global_value['weight_statue'] != True):\n\n \ - \ global_value['average_weights'] = sum_scaled_weights(scaled_local_weight_list)\n\ - \ global_value['weight_statue'] = True\n \ - \ global_value['average_weights'] = json.dumps([np.array(w).tolist()\ - \ for w in global_value['average_weights']])\n\n break\n\ - \n elif(global_value['weight_statue'] == True):\n\n \ - \ break\n\n time.sleep(1)\n\n clients_local_count.clear()\n\ - \ scaled_local_weight_list.clear()\n\n return jsonify({'result':\ - \ (global_value['average_weights'])})\n\n @app.route('/shutdown', methods=['GET'])\n\ - \ def shutdown_server():\n global_value['shutdown'] +=1 \n \ - \ with shutdown_lock:\n while True:\n if(global_value['shutdown']\ - \ == NUM_OF_CLIENTS):\n os._exit(0)\n \ - \ return 'Server shutting down...'\n time.sleep(1)\n\n \ - \ app.run(host=\"0.0.0.0\", port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Server',\ - \ description='')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs =\ - \ server(**_parsed_args)\n" - image: tensorflow/tensorflow - ports: - - {containerPort: 8080, name: my-port} - resources: - limits: {cpu: '0.2'} - requests: {cpu: '0.2'} - metadata: - labels: - app: http-service - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": [], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 - -m pip install --quiet --no-warn-script-location ''flask'' ''pandas'' || - PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location - ''flask'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def server():\n import json\n import pandas as pd\n import numpy - as np\n import pickle\n import threading\n import time\n import - tensorflow as tf\n from flask import Flask, jsonify,request\n import - os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list - = []\n global_value = { #Share variable\n ''last_run_statue'' - : False, #last run finish or not\n ''data_statue'' : - None, #global_count finish or not\n ''global_count'' - : None,\n ''scale_statue'' : None,\n ''weight_statue'' - : None,\n ''average_weights'' : None,\n ''shutdown'' - : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n init_lock = threading.Lock()\n clients_local_count_lock - = threading.Lock()\n scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock - = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n def - before_request():\n print(''get request'')\n\n @app.route(''/data'', - methods=[''POST''])\n def flask_server():\n with init_lock: #check - last run is finish and init varible\n\n while True:\n\n if(len(clients_local_count)==0 - and global_value[''last_run_statue''] == False):#init the variable by first - client enter\n global_value[''last_run_statue''] = True\n global_value[''data_statue''] - = False\n global_value[''scale_statue''] = False\n global_value[''weight_statue''] - = False\n break\n\n elif(global_value[''last_run_statue''] - == True):\n break\n time.sleep(3)\n\n local_count - = int(request.form.get(''local_count'')) #get data\n bs - = int(request.form.get(''bs''))\n local_weight = json.loads(request.form.get(''local_weight''))\n local_weight - = [np.array(lst) for lst in local_weight]\n\n def scale_model_weights(weight, - scalar):\n weight_final = []\n steps = len(weight)\n for - i in range(steps):\n weight_final.append(scalar * weight[i])\n return - weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n avg_grad - = list()\n #get the average grad accross all client gradients\n for - grad_list_tuple in zip(*scaled_weight_list):\n layer_mean - = tf.math.reduce_sum(grad_list_tuple, axis=0)\n avg_grad.append(layer_mean)\n\n return - avg_grad\n\n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\n with - scaled_local_weight_list_lock:\n while True:\n\n if - (len(clients_local_count) == NUM_OF_CLIENTS and global_value[''data_statue''] - != True):\n global_value[''last_run_statue''] = False\n sum_of_local_count=sum(clients_local_count)\n\n global_value[''global_count''] - = sum_of_local_count \n\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights - = scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n global_value[''scale_statue''] - = True \n global_value[''data_statue''] = True\n break\n elif - (global_value[''data_statue''] == True and global_value[''scale_statue''] - == True):\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights - =scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n break\n time.sleep(1)\n\n with - cal_weight_lock:\n\n while True:\n if(len(scaled_local_weight_list) - == NUM_OF_CLIENTS and global_value[''weight_statue''] != True):\n\n global_value[''average_weights''] - = sum_scaled_weights(scaled_local_weight_list)\n global_value[''weight_statue''] - = True\n global_value[''average_weights''] = json.dumps([np.array(w).tolist() - for w in global_value[''average_weights'']])\n\n break\n\n elif(global_value[''weight_statue''] - == True):\n\n break\n\n time.sleep(1)\n\n clients_local_count.clear()\n scaled_local_weight_list.clear()\n\n return - jsonify({''result'': (global_value[''average_weights''])})\n\n @app.route(''/shutdown'', - methods=[''GET''])\n def shutdown_server():\n global_value[''shutdown''] - +=1 \n with shutdown_lock:\n while True:\n if(global_value[''shutdown''] - == NUM_OF_CLIENTS):\n os._exit(0)\n return - ''Server shutting down...''\n time.sleep(1)\n\n app.run(host=\"0.0.0.0\", - port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Server'', - description='''')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs - = server(**_parsed_args)\n"], "image": "tensorflow/tensorflow"}}, "name": - "Server"}', pipelines.kubeflow.org/component_ref: '{}'} - - name: show-results - container: - args: [--test-acc, '{{inputs.parameters.client-last_accuracy}}', '----output-paths', - /tmp/outputs/test_accuracy/data] - command: - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - | - def show_results(test_acc): - return([test_acc]) - - def _serialize_float(float_value: float) -> str: - if isinstance(float_value, str): - return float_value - if not isinstance(float_value, (float, int)): - raise TypeError('Value "{}" has type "{}" instead of float.'.format( - str(float_value), str(type(float_value)))) - return str(float_value) - - import argparse - _parser = argparse.ArgumentParser(prog='Show results', description='') - _parser.add_argument("--test-acc", dest="test_acc", type=float, required=True, default=argparse.SUPPRESS) - _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1) - _parsed_args = vars(_parser.parse_args()) - _output_files = _parsed_args.pop("_output_paths", []) - - _outputs = show_results(**_parsed_args) - - _output_serializers = [ - _serialize_float, - - ] - - import os - for idx, output_file in enumerate(_output_files): - try: - os.makedirs(os.path.dirname(output_file)) - except OSError: - pass - with open(output_file, 'w') as f: - f.write(_output_serializers[idx](_outputs[idx])) - image: python:3.7 - resources: - limits: {cpu: '0.2'} - requests: {cpu: '0.2'} - inputs: - parameters: - - {name: client-last_accuracy} - outputs: - artifacts: - - {name: show-results-test_accuracy, path: /tmp/outputs/test_accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--test-acc", {"inputValue": "test_acc"}, "----output-paths", - {"outputPath": "test_accuracy"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def show_results(test_acc):\n return([test_acc])\n\ndef _serialize_float(float_value: - float) -> str:\n if isinstance(float_value, str):\n return float_value\n if - not isinstance(float_value, (float, int)):\n raise TypeError(''Value - \"{}\" has type \"{}\" instead of float.''.format(\n str(float_value), - str(type(float_value))))\n return str(float_value)\n\nimport argparse\n_parser - = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--test-acc\", - dest=\"test_acc\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", - dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files - = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = show_results(**_parsed_args)\n\n_output_serializers - = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except - OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], - "image": "python:3.7"}}, "inputs": [{"name": "test_acc", "type": "Float"}], - "name": "Show results", "outputs": [{"name": "test_accuracy", "type": "Float"}]}', - pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"test_acc": - "{{inputs.parameters.client-last_accuracy}}"}'} - arguments: - parameters: - - {name: namespace, value: kubeflow-user-thu01} - serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml deleted file mode 100644 index b8f353c80..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml +++ /dev/null @@ -1,211 +0,0 @@ -apiVersion: argoproj.io/v1alpha1 -kind: Workflow -metadata: - generateName: mnist-pipeline- - annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-12-26T13:06:02.352865', - pipelines.kubeflow.org/pipeline_spec: '{"description": "A pipeline to train a - model on mnist dataset and start a tensorboard.", "name": "mnist pipeline"}'} - labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} -spec: - entrypoint: mnist-pipeline - templates: - - name: create-tensorboard-visualization - container: - args: [] - command: - - sh - - -ex - - -c - - | - log_dir="$0" - output_metadata_path="$1" - pod_template_spec="$2" - image="$3" - - mkdir -p "$(dirname "$output_metadata_path")" - - echo ' - { - "outputs" : [{ - "type": "tensorboard", - "source": "'"$log_dir"'", - "image": "'"$image"'", - "pod_template_spec": '"$pod_template_spec"' - }] - } - ' >"$output_metadata_path" - - volume://mypvc/logs - - /tmp/outputs/mlpipeline-ui-metadata/data - - '{"spec": {"containers": [{"volumeMounts": [{"mountPath": "/data", "name": - "mypvc"}], "resources": {"requests": {"cpu": "250m"}, "limits": {"cpu": "500m"}}}], - "serviceAccountName": "default-editor", "volumes": [{"name": "mypvc", "persistentVolumeClaim": - {"claimName": "my-awesome-kf-workshop-1703595962"}}]}}' - - footprintai/tensorboard:2.7.0 - image: alpine - resources: - limits: {cpu: '1'} - requests: {cpu: '1'} - outputs: - artifacts: - - {name: mlpipeline-ui-metadata, path: /tmp/outputs/mlpipeline-ui-metadata/data} - metadata: - annotations: {author: Alexey Volkov , canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/tensorboard/prepare_tensorboard/component.yaml', - pipelines.kubeflow.org/component_spec: '{"description": "Pre-creates Tensorboard - visualization for a given Log dir URI.\nThis way the Tensorboard can be - viewed before the training completes.\nThe output Log dir URI should be - passed to a trainer component that will write Tensorboard logs to that directory.\n", - "implementation": {"container": {"command": ["sh", "-ex", "-c", "log_dir=\"$0\"\noutput_metadata_path=\"$1\"\npod_template_spec=\"$2\"\nimage=\"$3\"\n\nmkdir - -p \"$(dirname \"$output_metadata_path\")\"\n\necho ''\n {\n \"outputs\" - : [{\n \"type\": \"tensorboard\",\n \"source\": \"''\"$log_dir\"''\",\n \"image\": - \"''\"$image\"''\",\n \"pod_template_spec\": ''\"$pod_template_spec\"''\n }]\n }\n'' - >\"$output_metadata_path\"\n", {"inputValue": "Log dir URI"}, {"outputPath": - "mlpipeline-ui-metadata"}, {"inputValue": "Pod Template Spec"}, {"inputValue": - "Image"}], "image": "alpine"}}, "inputs": [{"name": "Log dir URI", "type": - "String"}, {"default": "", "name": "Image", "type": "String"}, {"default": - "null", "name": "Pod Template Spec", "type": "String"}], "metadata": {"annotations": - {"author": "Alexey Volkov ", "canonical_location": - "https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/tensorboard/prepare_tensorboard/component.yaml"}}, - "name": "Create Tensorboard visualization", "outputs": [{"name": "mlpipeline-ui-metadata", - "type": "kfp.v1.ui-metadata"}]}', pipelines.kubeflow.org/component_ref: '{"digest": - "cc3c37c54619129e4f57e4564bc5df0ba9719a305e6145238f2ae7e54d87f2ef", "url": - "https://raw.githubusercontent.com/kubeflow/pipelines/1b107eb4bb2510ecb99fd5f4fb438cbf7c96a87a/components/contrib/tensorflow/tensorboard/prepare_tensorboard/component.yaml"}', - pipelines.kubeflow.org/arguments.parameters: '{"Image": "footprintai/tensorboard:2.7.0", - "Log dir URI": "volume://mypvc/logs", "Pod Template Spec": "{\"spec\": {\"containers\": - [{\"volumeMounts\": [{\"mountPath\": \"/data\", \"name\": \"mypvc\"}], \"resources\": - {\"requests\": {\"cpu\": \"250m\"}, \"limits\": {\"cpu\": \"500m\"}}}], - \"serviceAccountName\": \"default-editor\", \"volumes\": [{\"name\": \"mypvc\", - \"persistentVolumeClaim\": {\"claimName\": \"my-awesome-kf-workshop-1703595962\"}}]}}"}'} - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - - name: mnist-func - container: - args: [--log-folder, /data, '----output-paths', /tmp/outputs/logdir/data] - command: - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - "def mnist_func(log_folder):\n\n print('mnist_func:', log_folder)\n \ - \ import tensorflow as tf\n import json\n mnist = tf.keras.datasets.mnist\n\ - \ (x_train,y_train), (x_test, y_test) = mnist.load_data()\n x_train,\ - \ x_test = x_train/255.0, x_test/255.0\n\n def create_model():\n \ - \ return tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape\ - \ = (28,28)),\n tf.keras.layers.Dense(512, activation = 'relu'),\n\ - \ tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10,\ - \ activation = 'softmax')\n ])\n model = create_model()\n model.compile(optimizer='adam',\n\ - \ loss='sparse_categorical_crossentropy',\n \ - \ metrics=['accuracy'])\n import datetime\n import os\n\n ###\ - \ add tensorboard logout callback\n log_dir = os.path.join(log_folder,\ - \ \"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n tensorboard_callback\ - \ = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n \ - \ ######\n\n model.fit(x=x_train, \n y=y_train, \n \ - \ epochs=5, \n validation_data=(x_test, y_test), \n \ - \ callbacks=[tensorboard_callback])\n\n print('At least tensorboard\ - \ callbacks are correct')\n print('logdir:', log_dir)\n return ([log_dir])\n\ - \ndef _serialize_str(str_value: str) -> str:\n if not isinstance(str_value,\ - \ str):\n raise TypeError('Value \"{}\" has type \"{}\" instead of\ - \ str.'.format(\n str(str_value), str(type(str_value))))\n return\ - \ str_value\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Mnist\ - \ func', description='')\n_parser.add_argument(\"--log-folder\", dest=\"log_folder\"\ - , type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"\ - ----output-paths\", dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args\ - \ = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"_output_paths\"\ - , [])\n\n_outputs = mnist_func(**_parsed_args)\n\n_output_serializers = [\n\ - \ _serialize_str,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ - \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ - \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" - image: tensorflow/tensorflow:2.0.0-py3 - resources: - limits: {cpu: '1'} - requests: {cpu: '1'} - volumeMounts: - - {mountPath: /data, name: mypvc} - inputs: - parameters: - - {name: mypvc-name} - outputs: - artifacts: - - {name: mnist-func-logdir, path: /tmp/outputs/logdir/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--log-folder", {"inputValue": "log_folder"}, "----output-paths", - {"outputPath": "logdir"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def mnist_func(log_folder):\n\n print(''mnist_func:'', log_folder)\n import - tensorflow as tf\n import json\n mnist = tf.keras.datasets.mnist\n (x_train,y_train), - (x_test, y_test) = mnist.load_data()\n x_train, x_test = x_train/255.0, - x_test/255.0\n\n def create_model():\n return tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape - = (28,28)),\n tf.keras.layers.Dense(512, activation = ''relu''),\n tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10, - activation = ''softmax'')\n ])\n model = create_model()\n model.compile(optimizer=''adam'',\n loss=''sparse_categorical_crossentropy'',\n metrics=[''accuracy''])\n import - datetime\n import os\n\n ### add tensorboard logout callback\n log_dir - = os.path.join(log_folder, \"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n tensorboard_callback - = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n ######\n\n model.fit(x=x_train, - \n y=y_train, \n epochs=5, \n validation_data=(x_test, - y_test), \n callbacks=[tensorboard_callback])\n\n print(''At - least tensorboard callbacks are correct'')\n print(''logdir:'', log_dir)\n return - ([log_dir])\n\ndef _serialize_str(str_value: str) -> str:\n if not isinstance(str_value, - str):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of - str.''.format(\n str(str_value), str(type(str_value))))\n return - str_value\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Mnist - func'', description='''')\n_parser.add_argument(\"--log-folder\", dest=\"log_folder\", - type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", - dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files - = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = mnist_func(**_parsed_args)\n\n_output_serializers - = [\n _serialize_str,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except - OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], - "image": "tensorflow/tensorflow:2.0.0-py3"}}, "inputs": [{"name": "log_folder", - "type": "String"}], "name": "Mnist func", "outputs": [{"name": "logdir", - "type": "String"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"log_folder": - "/data"}'} - volumes: - - name: mypvc - persistentVolumeClaim: {claimName: '{{inputs.parameters.mypvc-name}}'} - - name: mnist-pipeline - dag: - tasks: - - {name: create-tensorboard-visualization, template: create-tensorboard-visualization} - - name: mnist-func - template: mnist-func - dependencies: [create-tensorboard-visualization, mypvc] - arguments: - parameters: - - {name: mypvc-name, value: '{{tasks.mypvc.outputs.parameters.mypvc-name}}'} - - {name: mypvc, template: mypvc} - - name: mypvc - resource: - action: create - manifest: | - apiVersion: v1 - kind: PersistentVolumeClaim - metadata: - name: '{{workflow.name}}-my-awesome-kf-workshop-1703595962' - spec: - accessModes: - - ReadWriteOnce - resources: - requests: - storage: 1Gi - outputs: - parameters: - - name: mypvc-manifest - valueFrom: {jsonPath: '{}'} - - name: mypvc-name - valueFrom: {jsonPath: '{.metadata.name}'} - - name: mypvc-size - valueFrom: {jsonPath: '{.status.capacity.storage}'} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - arguments: - parameters: [] - serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml deleted file mode 100644 index b1a4a1e25..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml +++ /dev/null @@ -1,131 +0,0 @@ -apiVersion: argoproj.io/v1alpha1 -kind: Workflow -metadata: - generateName: only-decision-tree- - annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.20, pipelines.kubeflow.org/pipeline_compilation_time: '2023-04-28T21:42:00.682134', - pipelines.kubeflow.org/pipeline_spec: '{"description": "Applies Decision Tree - for classification problem.", "name": "only_decision_tree"}'} - labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.20} -spec: - entrypoint: only-decision-tree - templates: - - name: decision-tree-classifier - container: - args: [] - command: [python, decision_tree.py, --data, /tmp/inputs/Data/data, --accuracy, - /tmp/outputs/Accuracy/data] - image: lightnighttw/kubeflow:decision_tree_v2 - resources: - limits: - cpu: 2 - inputs: - artifacts: - - {name: download-data-function-Data, path: /tmp/inputs/Data/data} - outputs: - parameters: - - name: decision-tree-classifier-Accuracy - valueFrom: {path: /tmp/outputs/Accuracy/data} - artifacts: - - {name: decision-tree-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains - a decision tree classifier", "implementation": {"container": {"command": - ["python", "decision_tree.py", "--data", {"inputPath": "Data"}, "--accuracy", - {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:decision_tree_v2"}}, - "inputs": [{"description": "Path where data is stored.", "name": "Data", - "type": "LocalPath"}], "name": "Decision Tree classifier", "outputs": [{"description": - "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": - "c5c232a9654213b3b222693949b71a5d561d04ed09543c20b6f8f2eab651ae0b", "url": - "decision_tree/decision_tree.yaml"}'} - - name: download-data-function - container: - args: [] - command: [python, download_data.py, --data, /tmp/outputs/Data/data] - image: lightnighttw/kubeflow:download_data - resources: - limits: - cpu: 2 - outputs: - artifacts: - - {name: download-data-function-Data, path: /tmp/outputs/Data/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Download - toy data from sklearn datasets", "implementation": {"container": {"command": - ["python", "download_data.py", "--data", {"outputPath": "Data"}], "image": - "lightnighttw/kubeflow:download_data"}}, "name": "Download Data Function", - "outputs": [{"description": "Path where data will be stored.", "name": "Data", - "type": "LocalPath"}]}', pipelines.kubeflow.org/component_ref: '{"digest": - "467750defdccfec51c3af2a7eb853f74235f5f97329006d72bf33ff6e15ed02d", "url": - "download_data/download_data.yaml"}'} - - name: only-decision-tree - dag: - tasks: - - name: decision-tree-classifier - template: decision-tree-classifier - dependencies: [download-data-function] - arguments: - artifacts: - - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} - - {name: download-data-function, template: download-data-function} - - name: show-results - template: show-results - dependencies: [decision-tree-classifier] - arguments: - parameters: - - {name: decision-tree-classifier-Accuracy, value: '{{tasks.decision-tree-classifier.outputs.parameters.decision-tree-classifier-Accuracy}}'} - - name: show-results - container: - args: [--decision-tree, '{{inputs.parameters.decision-tree-classifier-Accuracy}}'] - command: - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - | - def show_results(decision_tree): - # the results are shown. - - print(f"Decision tree (accuracy): {decision_tree}") - - import argparse - _parser = argparse.ArgumentParser(prog='Show results', description='') - _parser.add_argument("--decision-tree", dest="decision_tree", type=float, required=True, default=argparse.SUPPRESS) - _parsed_args = vars(_parser.parse_args()) - - _outputs = show_results(**_parsed_args) - image: python:3.7 - resources: - limits: - cpu: 2 - inputs: - parameters: - - {name: decision-tree-classifier-Accuracy} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--decision-tree", {"inputValue": "decision_tree"}], "command": - ["sh", "-ec", "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 - -u \"$program_path\" \"$@\"\n", "def show_results(decision_tree):\n # - the results are shown.\n\n print(f\"Decision tree (accuracy): {decision_tree}\")\n\nimport - argparse\n_parser = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--decision-tree\", - dest=\"decision_tree\", type=float, required=True, default=argparse.SUPPRESS)\n_parsed_args - = vars(_parser.parse_args())\n\n_outputs = show_results(**_parsed_args)\n"], - "image": "python:3.7"}}, "inputs": [{"name": "decision_tree", "type": "Float"}], - "name": "Show results"}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"decision_tree": - "{{inputs.parameters.decision-tree-classifier-Accuracy}}"}'} - arguments: - parameters: [] - serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml b/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml deleted file mode 100644 index 2e10bcefa..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml +++ /dev/null @@ -1,229 +0,0 @@ -apiVersion: argoproj.io/v1alpha1 -kind: Workflow -metadata: - generateName: three-pipeline- - annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-04-28T12:05:44.365082', - pipelines.kubeflow.org/pipeline_spec: '{"description": "Applies Decision Tree, - random forest and Logistic Regression for classification problem.", "name": - "Three Pipeline"}'} - labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} -spec: - entrypoint: three-pipeline - templates: - - name: decision-tree-classifier - container: - args: [] - command: [python, decision_tree.py, --data, /tmp/inputs/Data/data, --accuracy, - /tmp/outputs/Accuracy/data] - image: lightnighttw/kubeflow:decision_tree_v2 - resources: - limits: - cpu: 2 - inputs: - artifacts: - - {name: download-data-function-Data, path: /tmp/inputs/Data/data} - outputs: - parameters: - - name: decision-tree-classifier-Accuracy - valueFrom: {path: /tmp/outputs/Accuracy/data} - artifacts: - - {name: decision-tree-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains - a decision tree classifier", "implementation": {"container": {"command": - ["python", "decision_tree.py", "--data", {"inputPath": "Data"}, "--accuracy", - {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:decision_tree_v2"}}, - "inputs": [{"description": "Path where data is stored.", "name": "Data", - "type": "LocalPath"}], "name": "Decision Tree classifier", "outputs": [{"description": - "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": - "c5c232a9654213b3b222693949b71a5d561d04ed09543c20b6f8f2eab651ae0b", "url": - "decision_tree/decision_tree.yaml"}'} - - name: download-data-function - container: - args: [] - command: [python, download_data.py, --data, /tmp/outputs/Data/data] - image: lightnighttw/kubeflow:download_data - resources: - limits: - cpu: 2 - outputs: - artifacts: - - {name: download-data-function-Data, path: /tmp/outputs/Data/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Download - toy data from sklearn datasets", "implementation": {"container": {"command": - ["python", "download_data.py", "--data", {"outputPath": "Data"}], "image": - "lightnighttw/kubeflow:download_data"}}, "name": "Download Data Function", - "outputs": [{"description": "Path where data will be stored.", "name": "Data", - "type": "LocalPath"}]}', pipelines.kubeflow.org/component_ref: '{"digest": - "467750defdccfec51c3af2a7eb853f74235f5f97329006d72bf33ff6e15ed02d", "url": - "download_data/download_data.yaml"}'} - - name: logistic-regression-classifier - container: - args: [] - command: [python, logistic_regression.py, --data, /tmp/inputs/Data/data, --accuracy, - /tmp/outputs/Accuracy/data] - image: lightnighttw/kubeflow:logistic_regression - resources: - limits: - cpu: 2 - inputs: - artifacts: - - {name: download-data-function-Data, path: /tmp/inputs/Data/data} - outputs: - parameters: - - name: logistic-regression-classifier-Accuracy - valueFrom: {path: /tmp/outputs/Accuracy/data} - artifacts: - - {name: logistic-regression-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains - a Logistic Regression Classifier", "implementation": {"container": {"command": - ["python", "logistic_regression.py", "--data", {"inputPath": "Data"}, "--accuracy", - {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:logistic_regression"}}, - "inputs": [{"description": "Path where data is stored.", "name": "Data", - "type": "LocalPath"}], "name": "Logistic Regression Classifier", "outputs": - [{"description": "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', - pipelines.kubeflow.org/component_ref: '{"digest": "a8d1e77d07d18a75bef200aee96f35136833fc4bb535f33fd949a307beb094c2", - "url": "logistic_regression/logistic_regression.yaml"}'} - - name: random-forest-classifier - container: - args: [] - command: [python, randomforest.py, --data, /tmp/inputs/Data/data, --accuracy, - /tmp/outputs/Accuracy/data] - image: lightnighttw/kubeflow:random_forest_v4 - resources: - limits: - cpu: 2 - inputs: - artifacts: - - {name: download-data-function-Data, path: /tmp/inputs/Data/data} - outputs: - parameters: - - name: random-forest-classifier-Accuracy - valueFrom: {path: /tmp/outputs/Accuracy/data} - artifacts: - - {name: random-forest-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Train - a random forest classifier", "implementation": {"container": {"command": - ["python", "randomforest.py", "--data", {"inputPath": "Data"}, "--accuracy", - {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:random_forest_v4"}}, - "inputs": [{"description": "Path where data is stored.", "name": "Data", - "type": "LocalPath"}], "name": "Random Forest classifier", "outputs": [{"description": - "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": - "b49b12da3371976eddf41d662685bb49d71b419d516de65efdd90938d2c706bc", "url": - "randomForest/random_forest.yaml"}'} - - name: show-results - container: - args: [--decision-tree, '{{inputs.parameters.decision-tree-classifier-Accuracy}}', - --logistic-regression, '{{inputs.parameters.logistic-regression-classifier-Accuracy}}', - --random-forest, '{{inputs.parameters.random-forest-classifier-Accuracy}}'] - command: - - sh - - -ec - - | - program_path=$(mktemp) - printf "%s" "$0" > "$program_path" - python3 -u "$program_path" "$@" - - | - def show_results(decision_tree, logistic_regression, random_forest): - # Given the outputs from decision_tree and logistic regression components - # the results are shown. - - print(f"Decision tree (accuracy): {decision_tree}") - print(f"Logistic regression (accuracy): {logistic_regression}") - print(f"Random forest (accuracy): {random_forest}") - - import argparse - _parser = argparse.ArgumentParser(prog='Show results', description='') - _parser.add_argument("--decision-tree", dest="decision_tree", type=float, required=True, default=argparse.SUPPRESS) - _parser.add_argument("--logistic-regression", dest="logistic_regression", type=float, required=True, default=argparse.SUPPRESS) - _parser.add_argument("--random-forest", dest="random_forest", type=float, required=True, default=argparse.SUPPRESS) - _parsed_args = vars(_parser.parse_args()) - - _outputs = show_results(**_parsed_args) - image: python:3.7 - resources: - limits: - cpu: 2 - inputs: - parameters: - - {name: decision-tree-classifier-Accuracy} - - {name: logistic-regression-classifier-Accuracy} - - {name: random-forest-classifier-Accuracy} - metadata: - labels: - pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 - pipelines.kubeflow.org/pipeline-sdk-type: kfp - pipelines.kubeflow.org/enable_caching: "true" - annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": - {"args": ["--decision-tree", {"inputValue": "decision_tree"}, "--logistic-regression", - {"inputValue": "logistic_regression"}, "--random-forest", {"inputValue": - "random_forest"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf - \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", - "def show_results(decision_tree, logistic_regression, random_forest):\n # - Given the outputs from decision_tree and logistic regression components\n # - the results are shown.\n\n print(f\"Decision tree (accuracy): {decision_tree}\")\n print(f\"Logistic - regression (accuracy): {logistic_regression}\")\n print(f\"Random forest - (accuracy): {random_forest}\")\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Show - results'', description='''')\n_parser.add_argument(\"--decision-tree\", - dest=\"decision_tree\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--logistic-regression\", - dest=\"logistic_regression\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--random-forest\", - dest=\"random_forest\", type=float, required=True, default=argparse.SUPPRESS)\n_parsed_args - = vars(_parser.parse_args())\n\n_outputs = show_results(**_parsed_args)\n"], - "image": "python:3.7"}}, "inputs": [{"name": "decision_tree", "type": "Float"}, - {"name": "logistic_regression", "type": "Float"}, {"name": "random_forest", - "type": "Float"}], "name": "Show results"}', pipelines.kubeflow.org/component_ref: '{}', - pipelines.kubeflow.org/arguments.parameters: '{"decision_tree": "{{inputs.parameters.decision-tree-classifier-Accuracy}}", - "logistic_regression": "{{inputs.parameters.logistic-regression-classifier-Accuracy}}", - "random_forest": "{{inputs.parameters.random-forest-classifier-Accuracy}}"}'} - - name: three-pipeline - dag: - tasks: - - name: decision-tree-classifier - template: decision-tree-classifier - dependencies: [download-data-function] - arguments: - artifacts: - - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} - - {name: download-data-function, template: download-data-function} - - name: logistic-regression-classifier - template: logistic-regression-classifier - dependencies: [download-data-function] - arguments: - artifacts: - - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} - - name: random-forest-classifier - template: random-forest-classifier - dependencies: [download-data-function] - arguments: - artifacts: - - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} - - name: show-results - template: show-results - dependencies: [decision-tree-classifier, logistic-regression-classifier, random-forest-classifier] - arguments: - parameters: - - {name: decision-tree-classifier-Accuracy, value: '{{tasks.decision-tree-classifier.outputs.parameters.decision-tree-classifier-Accuracy}}'} - - {name: logistic-regression-classifier-Accuracy, value: '{{tasks.logistic-regression-classifier.outputs.parameters.logistic-regression-classifier-Accuracy}}'} - - {name: random-forest-classifier-Accuracy, value: '{{tasks.random-forest-classifier.outputs.parameters.random-forest-classifier-Accuracy}}'} - arguments: - parameters: [] - serviceAccountName: pipeline-runner diff --git a/kubeflow/examples/1.connect-kubeflow/py/requirements.txt b/kubeflow/examples/1.connect-kubeflow/py/requirements.txt deleted file mode 100644 index 0a8547bcc..000000000 --- a/kubeflow/examples/1.connect-kubeflow/py/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -setuptools -wheel diff --git a/kubeflow/examples/1.connect-kubeflow/settings.js b/kubeflow/examples/1.connect-kubeflow/settings.js deleted file mode 100644 index 332013ee1..000000000 --- a/kubeflow/examples/1.connect-kubeflow/settings.js +++ /dev/null @@ -1,498 +0,0 @@ -/** - * This is the default settings file provided by Node-RED. - * - * It can contain any valid JavaScript code that will get run when Node-RED - * is started. - * - * Lines that start with // are commented out. - * Each entry should be separated from the entries above and below by a comma ',' - * - * For more information about individual settings, refer to the documentation: - * https://nodered.org/docs/user-guide/runtime/configuration - * - * The settings are split into the following sections: - * - Flow File and User Directory Settings - * - Security - * - Server Settings - * - Runtime Settings - * - Editor Settings - * - Node Settings - * - **/ - -module.exports = { - -/******************************************************************************* - * Flow File and User Directory Settings - * - flowFile - * - credentialSecret - * - flowFilePretty - * - userDir - * - nodesDir - ******************************************************************************/ - - /** The file containing the flows. If not set, defaults to flows_.json **/ - flowFile: 'flows.json', - - /** By default, credentials are encrypted in storage using a generated key. To - * specify your own secret, set the following property. - * If you want to disable encryption of credentials, set this property to false. - * Note: once you set this property, do not change it - doing so will prevent - * node-red from being able to decrypt your existing credentials and they will be - * lost. - */ - //credentialSecret: "a-secret-key", - credentialSecret: process.env.NODE_RED_CREDENTIAL_SECRET, - - /** By default, the flow JSON will be formatted over multiple lines making - * it easier to compare changes when using version control. - * To disable pretty-printing of the JSON set the following property to false. - */ - flowFilePretty: true, - - /** By default, all user data is stored in a directory called `.node-red` under - * the user's home directory. To use a different location, the following - * property can be used - */ - //userDir: '/home/nol/.node-red/', - - /** Node-RED scans the `nodes` directory in the userDir to find local node files. - * The following property can be used to specify an additional directory to scan. - */ - //nodesDir: '/home/nol/.node-red/nodes', - -/******************************************************************************* - * Security - * - adminAuth - * - https - * - httpsRefreshInterval - * - requireHttps - * - httpNodeAuth - * - httpStaticAuth - ******************************************************************************/ - - /** To password protect the Node-RED editor and admin API, the following - * property can be used. See http://nodered.org/docs/security.html for details. - */ - //adminAuth: { - // type: "credentials", - // users: [{ - // username: "admin", - // password: "$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN.", - // permissions: "*" - // }] - //}, - - /** The following property can be used to enable HTTPS - * This property can be either an object, containing both a (private) key - * and a (public) certificate, or a function that returns such an object. - * See http://nodejs.org/api/https.html#https_https_createserver_options_requestlistener - * for details of its contents. - */ - - /** Option 1: static object */ - //https: { - // key: require("fs").readFileSync('privkey.pem'), - // cert: require("fs").readFileSync('cert.pem') - //}, - - /** Option 2: function that returns the HTTP configuration object */ - // https: function() { - // // This function should return the options object, or a Promise - // // that resolves to the options object - // return { - // key: require("fs").readFileSync('privkey.pem'), - // cert: require("fs").readFileSync('cert.pem') - // } - // }, - - /** If the `https` setting is a function, the following setting can be used - * to set how often, in hours, the function will be called. That can be used - * to refresh any certificates. - */ - //httpsRefreshInterval : 12, - - /** The following property can be used to cause insecure HTTP connections to - * be redirected to HTTPS. - */ - //requireHttps: true, - - /** To password protect the node-defined HTTP endpoints (httpNodeRoot), - * including node-red-dashboard, or the static content (httpStatic), the - * following properties can be used. - * The `pass` field is a bcrypt hash of the password. - * See http://nodered.org/docs/security.html#generating-the-password-hash - */ - //httpNodeAuth: {user:"user",pass:"$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN."}, - //httpStaticAuth: {user:"user",pass:"$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN."}, - -/******************************************************************************* - * Server Settings - * - uiPort - * - uiHost - * - apiMaxLength - * - httpServerOptions - * - httpAdminRoot - * - httpAdminMiddleware - * - httpNodeRoot - * - httpNodeCors - * - httpNodeMiddleware - * - httpStatic - ******************************************************************************/ - - /** the tcp port that the Node-RED web server is listening on */ - uiPort: process.env.PORT || 1880, - - /** By default, the Node-RED UI accepts connections on all IPv4 interfaces. - * To listen on all IPv6 addresses, set uiHost to "::", - * The following property can be used to listen on a specific interface. For - * example, the following would only allow connections from the local machine. - */ - //uiHost: "127.0.0.1", - - /** The maximum size of HTTP request that will be accepted by the runtime api. - * Default: 5mb - */ - //apiMaxLength: '5mb', - - /** The following property can be used to pass custom options to the Express.js - * server used by Node-RED. For a full list of available options, refer - * to http://expressjs.com/en/api.html#app.settings.table - */ - //httpServerOptions: { }, - - /** By default, the Node-RED UI is available at http://localhost:1880/ - * The following property can be used to specify a different root path. - * If set to false, this is disabled. - */ - //httpAdminRoot: '/admin', - - /** The following property can be used to add a custom middleware function - * in front of all admin http routes. For example, to set custom http - * headers. It can be a single function or an array of middleware functions. - */ - // httpAdminMiddleware: function(req,res,next) { - // // Set the X-Frame-Options header to limit where the editor - // // can be embedded - // //res.set('X-Frame-Options', 'sameorigin'); - // next(); - // }, - - - /** Some nodes, such as HTTP In, can be used to listen for incoming http requests. - * By default, these are served relative to '/'. The following property - * can be used to specifiy a different root path. If set to false, this is - * disabled. - */ - //httpNodeRoot: '/red-nodes', - - /** The following property can be used to configure cross-origin resource sharing - * in the HTTP nodes. - * See https://github.com/troygoode/node-cors#configuration-options for - * details on its contents. The following is a basic permissive set of options: - */ - //httpNodeCors: { - // origin: "*", - // methods: "GET,PUT,POST,DELETE" - //}, - - /** If you need to set an http proxy please set an environment variable - * called http_proxy (or HTTP_PROXY) outside of Node-RED in the operating system. - * For example - http_proxy=http://myproxy.com:8080 - * (Setting it here will have no effect) - * You may also specify no_proxy (or NO_PROXY) to supply a comma separated - * list of domains to not proxy, eg - no_proxy=.acme.co,.acme.co.uk - */ - - /** The following property can be used to add a custom middleware function - * in front of all http in nodes. This allows custom authentication to be - * applied to all http in nodes, or any other sort of common request processing. - * It can be a single function or an array of middleware functions. - */ - //httpNodeMiddleware: function(req,res,next) { - // // Handle/reject the request, or pass it on to the http in node by calling next(); - // // Optionally skip our rawBodyParser by setting this to true; - // //req.skipRawBodyParser = true; - // next(); - //}, - - /** When httpAdminRoot is used to move the UI to a different root path, the - * following property can be used to identify a directory of static content - * that should be served at http://localhost:1880/. - */ - //httpStatic: '/home/nol/node-red-static/', - -/******************************************************************************* - * Runtime Settings - * - lang - * - logging - * - contextStorage - * - exportGlobalContextKeys - * - externalModules - ******************************************************************************/ - - /** Uncomment the following to run node-red in your preferred language. - * Available languages include: en-US (default), ja, de, zh-CN, zh-TW, ru, ko - * Some languages are more complete than others. - */ - // lang: "de", - - /** Configure the logging output */ - logging: { - /** Only console logging is currently supported */ - console: { - /** Level of logging to be recorded. Options are: - * fatal - only those errors which make the application unusable should be recorded - * error - record errors which are deemed fatal for a particular request + fatal errors - * warn - record problems which are non fatal + errors + fatal errors - * info - record information about the general running of the application + warn + error + fatal errors - * debug - record information which is more verbose than info + info + warn + error + fatal errors - * trace - record very detailed logging + debug + info + warn + error + fatal errors - * off - turn off all logging (doesn't affect metrics or audit) - */ - level: "info", - /** Whether or not to include metric events in the log output */ - metrics: false, - /** Whether or not to include audit events in the log output */ - audit: false - } - }, - - /** Context Storage - * The following property can be used to enable context storage. The configuration - * provided here will enable file-based context that flushes to disk every 30 seconds. - * Refer to the documentation for further options: https://nodered.org/docs/api/context/ - */ - //contextStorage: { - // default: { - // module:"localfilesystem" - // }, - //}, - - /** `global.keys()` returns a list of all properties set in global context. - * This allows them to be displayed in the Context Sidebar within the editor. - * In some circumstances it is not desirable to expose them to the editor. The - * following property can be used to hide any property set in `functionGlobalContext` - * from being list by `global.keys()`. - * By default, the property is set to false to avoid accidental exposure of - * their values. Setting this to true will cause the keys to be listed. - */ - exportGlobalContextKeys: false, - - /** Configure how the runtime will handle external npm modules. - * This covers: - * - whether the editor will allow new node modules to be installed - * - whether nodes, such as the Function node are allowed to have their - * own dynamically configured dependencies. - * The allow/denyList options can be used to limit what modules the runtime - * will install/load. It can use '*' as a wildcard that matches anything. - */ - externalModules: { - // autoInstall: false, /** Whether the runtime will attempt to automatically install missing modules */ - // autoInstallRetry: 30, /** Interval, in seconds, between reinstall attempts */ - // palette: { /** Configuration for the Palette Manager */ - // allowInstall: true, /** Enable the Palette Manager in the editor */ - // allowUpdate: true, /** Allow modules to be updated in the Palette Manager */ - // allowUpload: true, /** Allow module tgz files to be uploaded and installed */ - // allowList: ['*'], - // denyList: [], - // allowUpdateList: ['*'], - // denyUpdateList: [] - // }, - // modules: { /** Configuration for node-specified modules */ - // allowInstall: true, - // allowList: [], - // denyList: [] - // } - }, - - -/******************************************************************************* - * Editor Settings - * - disableEditor - * - editorTheme - ******************************************************************************/ - - /** The following property can be used to disable the editor. The admin API - * is not affected by this option. To disable both the editor and the admin - * API, use either the httpRoot or httpAdminRoot properties - */ - //disableEditor: false, - - /** Customising the editor - * See https://nodered.org/docs/user-guide/runtime/configuration#editor-themes - * for all available options. - */ - editorTheme: { - /** The following property can be used to set a custom theme for the editor. - * See https://github.com/node-red-contrib-themes/theme-collection for - * a collection of themes to chose from. - */ - //theme: "", - - /** To disable the 'Welcome to Node-RED' tour that is displayed the first - * time you access the editor for each release of Node-RED, set this to false - */ - //tours: false, - - palette: { - /** The following property can be used to order the categories in the editor - * palette. If a node's category is not in the list, the category will get - * added to the end of the palette. - * If not set, the following default order is used: - */ - //categories: ['subflows', 'common', 'function', 'network', 'sequence', 'parser', 'storage'], - }, - - projects: { - /** To enable the Projects feature, set this value to true */ - enabled: false, - workflow: { - /** Set the default projects workflow mode. - * - manual - you must manually commit changes - * - auto - changes are automatically committed - * This can be overridden per-user from the 'Git config' - * section of 'User Settings' within the editor - */ - mode: "manual" - } - }, - - codeEditor: { - /** Select the text editor component used by the editor. - * Defaults to "ace", but can be set to "ace" or "monaco" - */ - lib: "ace", - options: { - /** The follow options only apply if the editor is set to "monaco" - * - * theme - must match the file name of a theme in - * packages/node_modules/@node-red/editor-client/src/vendor/monaco/dist/theme - * e.g. "tomorrow-night", "upstream-sunburst", "github", "my-theme" - */ - theme: "vs", - /** other overrides can be set e.g. fontSize, fontFamily, fontLigatures etc. - * for the full list, see https://microsoft.github.io/monaco-editor/api/interfaces/monaco.editor.istandaloneeditorconstructionoptions.html - */ - //fontSize: 14, - //fontFamily: "Cascadia Code, Fira Code, Consolas, 'Courier New', monospace", - //fontLigatures: true, - } - } - }, - -/******************************************************************************* - * Node Settings - * - fileWorkingDirectory - * - functionGlobalContext - * - functionExternalModules - * - nodeMessageBufferMaxLength - * - ui (for use with Node-RED Dashboard) - * - debugUseColors - * - debugMaxLength - * - execMaxBufferSize - * - httpRequestTimeout - * - mqttReconnectTime - * - serialReconnectTime - * - socketReconnectTime - * - socketTimeout - * - tcpMsgQueueSize - * - inboundWebSocketTimeout - * - tlsConfigDisableLocalFiles - * - webSocketNodeVerifyClient - ******************************************************************************/ - - /** The working directory to handle relative file paths from within the File nodes - * defaults to the working directory of the Node-RED process. - */ - //fileWorkingDirectory: "", - - /** Allow the Function node to load additional npm modules directly */ - functionExternalModules: true, - - /** The following property can be used to set predefined values in Global Context. - * This allows extra node modules to be made available with in Function node. - * For example, the following: - * functionGlobalContext: { os:require('os') } - * will allow the `os` module to be accessed in a Function node using: - * global.get("os") - */ - functionGlobalContext: { - // os:require('os'), - }, - - /** The maximum number of messages nodes will buffer internally as part of their - * operation. This applies across a range of nodes that operate on message sequences. - * defaults to no limit. A value of 0 also means no limit is applied. - */ - //nodeMessageBufferMaxLength: 0, - - /** If you installed the optional node-red-dashboard you can set it's path - * relative to httpNodeRoot - * Other optional properties include - * readOnly:{boolean}, - * middleware:{function or array}, (req,res,next) - http middleware - * ioMiddleware:{function or array}, (socket,next) - socket.io middleware - */ - //ui: { path: "ui" }, - - /** Colourise the console output of the debug node */ - //debugUseColors: true, - - /** The maximum length, in characters, of any message sent to the debug sidebar tab */ - debugMaxLength: 1000, - - /** Maximum buffer size for the exec node. Defaults to 10Mb */ - //execMaxBufferSize: 10000000, - - /** Timeout in milliseconds for HTTP request connections. Defaults to 120s */ - //httpRequestTimeout: 120000, - - /** Retry time in milliseconds for MQTT connections */ - mqttReconnectTime: 15000, - - /** Retry time in milliseconds for Serial port connections */ - serialReconnectTime: 15000, - - /** Retry time in milliseconds for TCP socket connections */ - //socketReconnectTime: 10000, - - /** Timeout in milliseconds for TCP server socket connections. Defaults to no timeout */ - //socketTimeout: 120000, - - /** Maximum number of messages to wait in queue while attempting to connect to TCP socket - * defaults to 1000 - */ - //tcpMsgQueueSize: 2000, - - /** Timeout in milliseconds for inbound WebSocket connections that do not - * match any configured node. Defaults to 5000 - */ - //inboundWebSocketTimeout: 5000, - - /** To disable the option for using local files for storing keys and - * certificates in the TLS configuration node, set this to true. - */ - //tlsConfigDisableLocalFiles: true, - - /** The following property can be used to verify websocket connection attempts. - * This allows, for example, the HTTP request headers to be checked to ensure - * they include valid authentication information. - */ - //webSocketNodeVerifyClient: function(info) { - // /** 'info' has three properties: - // * - origin : the value in the Origin header - // * - req : the HTTP request - // * - secure : true if req.connection.authorized or req.connection.encrypted is set - // * - // * The function should return true if the connection should be accepted, false otherwise. - // * - // * Alternatively, if this function is defined to accept a second argument, callback, - // * it can be used to verify the client asynchronously. - // * The callback takes three arguments: - // * - result : boolean, whether to accept the connection or not - // * - code : if result is false, the HTTP error status to return - // * - reason: if result is false, the HTTP reason string to return - // */ - //}, -} diff --git a/kubeflow/examples/Dockerfile b/kubeflow/examples/Dockerfile deleted file mode 100644 index f195a77fd..000000000 --- a/kubeflow/examples/Dockerfile +++ /dev/null @@ -1,20 +0,0 @@ -FROM nodered/node-red:2.2.3-12 - -ARG PREFIX - -ENV NODE_OPTIONS=--max_old_space_size=128 - -USER root - -RUN apk update && \ - apk add py3-pip - -USER node-red -# Copy package.json to the WORKDIR so npm builds all -# of your added nodes modules for Node-RED -COPY package.json . -RUN npm install --unsafe-perm --no-update-notifier --no-fund --only=production - -ADD scripts/entrypoint.sh . - -ENTRYPOINT ["./entrypoint.sh"] diff --git a/kubeflow/examples/README.md b/kubeflow/examples/README.md deleted file mode 100644 index c56f4dd28..000000000 --- a/kubeflow/examples/README.md +++ /dev/null @@ -1,40 +0,0 @@ -#### Examples - -This example folder contains multiple node-red flow setups, each is isolated with a folder that allows you to run the different examples with the same settings. - -##### Add customized npm package - -You can add customized npm package via `npm install` or directly modify `packages.json`. Then rebuild your container image. - -##### Build - -Simply to run the following command to build node-red image - -``` -./build.sh -``` - -##### Run the container image - -To run the container image, use - -``` -KUBEFLOW_HOST= \ -KUBEFLOW_USERNAME= \ -KUBEFLOW_PASSWORD= \ -./run.sh -``` - -which would mount the current folder (i.e. ./example) onto the containers. We took this as a convenient step as you could change codes with node-red UI and the mounting volume allows the changes to be reflected onto your local file system. - -The example-args allows you to specify which example you want to run, for example - -``` -./run.sh 0.helloworld -``` - -would run the `0.helloworld` example. - -##### Visit vis UI - -then you can go to UI, check it out: http://127.0.0.1:1880/ diff --git a/kubeflow/examples/build.sh b/kubeflow/examples/build.sh deleted file mode 100644 index 37ad5136d..000000000 --- a/kubeflow/examples/build.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/env bash - -docker compose build diff --git a/kubeflow/examples/docker-compose.yaml b/kubeflow/examples/docker-compose.yaml deleted file mode 100644 index 2a9619477..000000000 --- a/kubeflow/examples/docker-compose.yaml +++ /dev/null @@ -1,33 +0,0 @@ -################################################################################ -# Node-RED Stack or Compose -################################################################################ -# docker stack deploy node-red --compose-file docker-compose-node-red.yml -# docker-compose -f docker-compose-node-red.yml -p myNoderedProject up -################################################################################ -version: "3.7" - -services: - node-red: - image: reg.footprint-ai.com/public/kube-nodered:latest - build: - context: . - dockerfile: Dockerfile - labels: - # workaround around a docker-compose bug when image is also used by docker run (https://github.com/docker/compose/issues/10162) - com.docker.compose.container-number: "1" - environment: - - TZ=Asia/Taipei - - USERDIR=$USERDIR - - NODE_RED_CREDENTIAL_SECRET=noderedtutorial - - KUBEFLOW_HOST=$KUBEFLOW_HOST - - KUBEFLOW_USERNAME=$KUBEFLOW_USERNAME - - KUBEFLOW_PASSWORD=$KUBEFLOW_PASSWORD - ports: - - "1880:1880" - networks: - - node-red-net - volumes: - - ./:/data - -networks: - node-red-net: diff --git a/kubeflow/examples/package-lock.json b/kubeflow/examples/package-lock.json deleted file mode 100644 index a333a1615..000000000 --- a/kubeflow/examples/package-lock.json +++ /dev/null @@ -1,10782 +0,0 @@ -{ - "name": "node-red", - "version": "2.2.3", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "node-red", - "version": "2.2.3", - 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"engines": { - "node": ">=12" - } -} diff --git a/kubeflow/examples/run.sh b/kubeflow/examples/run.sh deleted file mode 100644 index daca187e0..000000000 --- a/kubeflow/examples/run.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env bash - -target=$1 - -if [ "$#" -eq 1 ] -then - USERDIR=/data/$target -else - USERDIR=/data/0.helloworld -fi - -echo "run with userdir=$USERDIR" - -USERDIR=$USERDIR docker compose up -#USERDIR=$USERDIR docker compose convert diff --git a/kubeflow/examples/scripts/entrypoint.sh b/kubeflow/examples/scripts/entrypoint.sh deleted file mode 100644 index 705054c0a..000000000 --- a/kubeflow/examples/scripts/entrypoint.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash - -trap stop SIGINT SIGTERM - -function stop() { - kill $CHILD_PID - wait $CHILD_PID -} - -/usr/local/bin/node $NODE_OPTIONS node_modules/node-red/red.js --userDir $USERDIR & - -CHILD_PID="$!" - -wait "${CHILD_PID}" From 76e882ed0b4f70a44d4aae029ef6dc7d286e5db1 Mon Sep 17 00:00:00 2001 From: NootNoot0 <161794667+NootNoot0@users.noreply.github.com> Date: Tue, 25 Jun 2024 10:01:01 +0800 Subject: [PATCH 3/3] Add files via upload --- Tunghai-CS-project-main/README.md | 61 + Tunghai-CS-project-main/kubeflow/LICENSE | 201 + Tunghai-CS-project-main/kubeflow/README.md | 354 + .../kubeflow/examples/0.helloworld/flows.json | 101 + .../examples/0.helloworld/flows_cred.json | 3 + .../examples/0.helloworld/helloworld.py | 6 + .../examples/0.helloworld/package.json | 6 + .../examples/0.helloworld/settings.js | 498 + .../examples/1.connect-kubeflow/flows.json | 368 + .../1.connect-kubeflow/flows_cred.json | 3 + .../node_modules/nodepipe/LSTM.html | 149 + .../node_modules/nodepipe/LSTM.js | 66 + .../node_modules/nodepipe/LSTM0.js | 147 + .../node_modules/nodepipe/decisionTree0.js | 147 + .../node_modules/nodepipe/decisiontree.html | 149 + .../node_modules/nodepipe/decisiontree.js | 66 + .../node_modules/nodepipe/fl.html | 149 + .../node_modules/nodepipe/fl.js | 66 + .../node_modules/nodepipe/fl0.js 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Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/install.py create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/mnist.py create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/requirements.txt create mode 100644 Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/settings.js create mode 100644 Tunghai-CS-project-main/kubeflow/examples/Dockerfile create mode 100644 Tunghai-CS-project-main/kubeflow/examples/README.md create mode 100644 Tunghai-CS-project-main/kubeflow/examples/build.sh create mode 100644 Tunghai-CS-project-main/kubeflow/examples/docker-compose.yaml create mode 100644 Tunghai-CS-project-main/kubeflow/examples/package-lock.json create mode 100644 Tunghai-CS-project-main/kubeflow/examples/package.json create mode 100644 Tunghai-CS-project-main/kubeflow/examples/run.sh create mode 100644 Tunghai-CS-project-main/kubeflow/examples/scripts/entrypoint.sh diff --git a/Tunghai-CS-project-main/README.md b/Tunghai-CS-project-main/README.md new file mode 100644 index 000000000..ee30e018e --- /dev/null +++ b/Tunghai-CS-project-main/README.md @@ -0,0 +1,61 @@ +目的 : + +這是一個node-red搭配kubeflow的作品，node-red作為畫面顯示，而kubeflow則是資料處理。在這個作品我將LSTM、mnist節點放在node-red，點擊該節點後，kubeflow則執行對應模型，模型所使用的輸入資料是normal.csv & abnormal.csv，輸出則是資料經模型訓練後的準確度。 + +安裝 : + +1. 將檔案 git clone 到 windows/System32 +2. 執行 Docker +3. 執行 Wsl 並依序輸入 cd kube-nodered, cd examples, 最後是 ./run.sh 1.connect-kubeflow +4. 查 Docker 是否有生成的容器和映像檔 +5. 執行 Docker 映像檔並執行 node-red +6. 執行 node-red後, 點擊 'intsall dependency', 接著使用者可以在'six pipeline' 頁面執行模型 +7. 到路徑 kube-nodered\examples\1.connect-kubeflow\py 中調整目錄底下的模型Python檔案，將登錄帳密改為使用者的 +=============================================================================== + +架構 : + +客製化節點的關鍵在於更改.flows.json、加入js.和html到nodepipe資料夾、加入模型python到py資料夾和加入模型pipeline到pipeline資料夾。node-red顯示的部分 : .flow.json更改會影響node-red節點的顯示，nodepipe的js.和html可以客製一個節點的功能欄位，如下拉選單。Kubeflow執行的部分 : 模型的python和pipeline。 +以下是各個檔案的路徑 +1. C:\Windows\System32\kube-nodered\examples\1.connect-kubeflo的.flow.json +2. C:\Windows\System32\kube-nodered\examples\1.connect-kubeflow\node_modules\nodepipe的js.和html +3. C:\Windows\System32\kube-nodered\examples\1.connect-kubeflow\py的模型python +4. C:\Windows\System32\kube-nodered\examples\1.connect-kubeflow\py\pipelines的模型pipeline +5. +================================================================================ + +輸入和輸出 : + +輸入資料 +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/773957ef-5aeb-4321-b0b2-8dfd712c1356) +輸出結果 +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/721fba7b-5aaf-4539-bd54-9f40b4d63de8) + +================================================================================= + +操作說明 : + +打開node-red後，先點擊圖中按鈕，安裝所需套件 +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/653de771-bba0-4408-acbb-a5174e34475f) +點擊圖中圓圈，切換分頁到模型節點 +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/82516d3f-4c72-4ee0-89cf-79aa699ce51e) +切換後的分頁 +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/2b89c8cc-4c89-44d5-9a4a-52e69be2170e) +點擊圖中按鈕，kubeflow會執行對應pipeline +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/16fb3142-a75b-42e7-a7c3-13bb8b955765) +可在畫面右側確認執行狀況，圖中範例為LSTM pipeline正在執行 +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/90829686-8627-4304-9047-1151ac02f59f) +在node-red上點擊執行後，可在kubeflow上看到新增的執行pipeline +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/f6fd314e-7365-430c-978f-ca1ab0f18e18) +Kubeflow上的執行結果 +![image](https://github.com/NootNoot0/Tunghai-CS-project/assets/161794667/ba16438c-92bd-4f04-b369-b191abfd97f5) + + + + + + + + + + diff --git a/Tunghai-CS-project-main/kubeflow/LICENSE b/Tunghai-CS-project-main/kubeflow/LICENSE new file mode 100644 index 000000000..261eeb9e9 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Please refer the following example to launch an individual example: + +1. In terminal (If you on Windows system, please use WSL) +``` +$ git clone https://github.com/NightLightTw/kube-nodered.git +``` +2. Enter target folder +``` +cd kube-nodered/examples +``` +3. Enter account information and start +``` +KUBEFLOW_HOST= \ +KUBEFLOW_USERNAME= \ +KUBEFLOW_PASSWORD= \ +./run.sh +``` +> **Info:** Here please use 1.connect-kubeflow + +## Install dependencies + +1. Then you can go to UI, check it out: http://127.0.0.1:1880/ +![](https://hackmd.io/_uploads/HJ8Rbmdya.png) + +2. Click the “install dependency” button to install dependency items such as specific python libraries and wait for its completion +![](https://hackmd.io/_uploads/rygsMmuy6.png) + +3. Click the “list experiments” button to test the environment work! +![](https://hackmd.io/_uploads/HyXxfX_kp.png) + +## Using our nodes +Switch to the "three-pipeline" flow and press the button to trigger the pipeline process + +![](https://hackmd.io/_uploads/Hkr4mXdk6.png) + +On kubeflow: +![](https://hackmd.io/_uploads/H1_hEX_JT.png) + + + +> **Info:** If the environment variable does not work, please fill in the account password directly in the python file + + +## Test python files to interact with kubeflow +``` +# Open another terminal and check docker status +docker ps +#enter container +docker exec -it bash +#enter document folder +cd /data/1.connect-kubeflow/py/api_examples +#execute function +python3 +``` + +You can test the file in api_example +> **Info:** Some of these files require a custom name, description, or assigned id in + +## Possible problems and solution +Q1: MissingSchema Invalid URL '' + +A1: This problem means that the login information is not accessed correctly, which may be caused by the environment variable not being read. +You can directly override the login information of the specified file + +ex:![](https://hackmd.io/_uploads/ryx59rejBh.jpg) + +Change to your own login information +``` +host = "https://example@test.com" +username = "test01" +password = "123456" +``` + +# Modify your own custom nodes/pipeline +![implementation architecture](https://hackmd.io/_uploads/H1ZLgUsH2.png) + +## Kubeflow part +### Custom make pipeline’s yaml file +Please refer to [Kubeflow implementation：add Random Forest algorithm](https://hackmd.io/@Nhi7So-lTz2m5R6pHyCLcA/Sk1eZFTbh) + +### Take changing randomForest.py as an example + +Modify using your own yaml file path +> **Info:** Line 66: uploadfile='pipelines/only_randomforest.yaml' + +> **Info:** Line 122~129 use json parser for filtering different outputs from get_run() + +```python= +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for only_randomforest." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/only_randomforest.yaml' + name='pipeline-' + random_suffix() + description="This is a only_randomForest pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'run_only_randomForest' + random_suffix() + description = "This is a only_randomForest run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'random-forest-classifier-Accuracy': #change parameter + output_value = parameter['value'] + + if output_value is not None: + print(f"Random Forest Classifier Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) +``` + +## Node-red part +**Package nodered pyshell node** + +**A node mainly consists of two files** +* **Javascript file(.js)** +define what the node does +* **HTML file(.html)** +Define the properties of the node and the windows and help messages in the Node-RED editor + +**When finally package into npm module, will need package.json** + + +### **package.json** +A standard file for describing the content of node.js modules + +A standard package.json can be generated using npm init. This command will ask a series of questions to find a reasonable default value. When asked for the name of the module name: enter the example name node-red-contrib- + +When it is established, you need to manually add the node-red attribute +*p.s. Where the example files need to be changed * + + + +```json= +{ + "name": "node-red-contrib-pythonshell-custom", + ... + "node-red": { + "nodes": { + "decisionTree": "decisiontree.js", + "randomForest": "randomforest.js", + "logisticRegression": "logisticregression.js" + "":"" + } + }, + ... +} + +``` +### **HTML** +```javascript= + + + + + + + \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js new file mode 100644 index 000000000..d16c5fe18 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM.js @@ -0,0 +1,66 @@ +/** + * Copyright 2014 Sense Tecnic Systems, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + **/ + +var util = require("util"); +var httpclient; +var PythonshellNode = require('./LSTM0'); + +module.exports = function(RED) { + "use strict"; + + function PythonshellInNode(n) { + RED.nodes.createNode(this,n); + + var node = this; + node.config = n; // copy config to the backend so that down bellow we can have a reference + + var pyNode = new PythonshellNode(n); + + pyNode.setStatusCallback(node.status.bind(node)) + + node.on("input",function(msg) { + pyNode.onInput(msg, function(result){ + node.send(result); + }, function(err){ + node.error(err); + }); + }); + + node.on('close', ()=>pyNode.onClose()); + } + + RED.nodes.registerType("LSTM", PythonshellInNode); + + RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { + var node = RED.nodes.getNode(req.params.id); + if (node != null) { + try { + if (node.config.continuous){// see above comment + node.receive({payload: 'pythonshell@close'}) + } else { + node.receive(); + } + res.sendStatus(200); + } catch(err) { + res.sendStatus(500); + node.error(RED._("pythonshell.failed",{error:err.toString()})); + } + } else { + res.sendStatus(404); + } + }); + +} diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js new file mode 100644 index 000000000..86c0742f4 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/LSTM0.js @@ -0,0 +1,147 @@ +var fs = require("fs"); + +function PythonshellInNode(config) { + if (!config.pyfile){ + throw 'pyfile not present'; + } + this.pythonExec = config.python3 ? "python3" : "python"; + this.pyfile = '/data/1.connect-kubeflow/py/LSTM.py'; + this.virtualenv = config.virtualenv; + + if (!fs.existsSync(this.pyfile)) { + throw 'pyfile not exist'; + } + + if (this.virtualenv && !fs.existsSync(this.virtualenv)){ + throw 'configured virtualenv not exist, consider remove or change'; + } + + this.stdInData = config.stdInData; + this.continuous = this.stdInData ? true : config.continuous; + this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); + this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); + this.spawn = require('child_process').spawn; + this.onStatus = ()=>{} +} + +PythonshellInNode.prototype.onInput = function(msg, out, err) { + payload = msg.payload || ''; + if (typeof payload === 'object'){ + payload = JSON.stringify(payload); + } else if (typeof payload !== 'string'){ + payload = payload.toString(); + } + + if (payload === 'pythonshell@close'){ + if (this.py != null){ + this.onClose() + return + } else { + // trigger new execution + payload = '' + } + } + + if (this.continuous && !this.stdInData && this.py != null){ + this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) + return + } + + var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec + + if (this.stdInData){ + if (!this.py){ + this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { + cwd: this.pydir, + detached: true + }); + this.firstExecution = true + } else { + this.firstExecution = false + } + } else { + this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { + cwd: this.pydir + }); + } + + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + + // subsequence message, no need to setup callbacks + if (this.stdInData && !this.firstExecution){ + this.py.stdin.write(payload + '\n') + return + } + + var py = this.py; + var dataString = ''; + var errString = ''; + + py.stdout.on('data', data => { + clearTimeout(this.standbyTimer) + + this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) + + let dataStr = data.toString(); + + dataString += dataStr; + + if (dataString.endsWith("\n")){ + if (this.continuous){ + msg.payload = dataString; + out(msg); + dataString = '' + } + } + + this.standbyTimer = setTimeout(()=>{ + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + }, 2000) + + }); + + py.stderr.on('data', data => { + errString += String(data);// just a different way to do it + this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) + }); + + py.stderr.on('error', console.log) + py.stdout.on('error', console.log) + py.stdin.on('error', console.log) + py.on('error', console.log) + + py.on('close', code =>{ + if (code){ + err('exit code: ' + code + ', ' + errString); + this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) + } else if (!this.continuous){ + msg.payload = dataString.trim(); + out(msg); + this.onStatus({fill:"green",shape:"dot",text:"Done"}) + } else { + this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) + } + this.py = null + setTimeout(()=>{ + this.onStatus({}) + }, 2000) + }); + + if (this.stdInData){ + py.stdin.write(payload + '\n') + } +}; + +PythonshellInNode.prototype.onClose = function() { + if (this.py){ + this.py.kill() + this.py = null + } +}; + +PythonshellInNode.prototype.setStatusCallback = function(callback) { + this.onStatus = callback +}; + + +module.exports = PythonshellInNode \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js new file mode 100644 index 000000000..7a8f3a690 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisionTree0.js @@ -0,0 +1,147 @@ +var fs = require("fs"); + +function PythonshellInNode(config) { + if (!config.pyfile){ + throw 'pyfile not present'; + } + this.pythonExec = config.python3 ? "python3" : "python"; + this.pyfile = "/data/1.connect-kubeflow/py/decisionTree.py"; + this.virtualenv = config.virtualenv; + + if (!fs.existsSync(this.pyfile)) { + throw 'pyfile not exist'; + } + + if (this.virtualenv && !fs.existsSync(this.virtualenv)){ + throw 'configured virtualenv not exist, consider remove or change'; + } + + this.stdInData = config.stdInData; + this.continuous = this.stdInData ? true : config.continuous; + this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); + this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); + this.spawn = require('child_process').spawn; + this.onStatus = ()=>{} +} + +PythonshellInNode.prototype.onInput = function(msg, out, err) { + payload = msg.payload || ''; + if (typeof payload === 'object'){ + payload = JSON.stringify(payload); + } else if (typeof payload !== 'string'){ + payload = payload.toString(); + } + + if (payload === 'pythonshell@close'){ + if (this.py != null){ + this.onClose() + return + } else { + // trigger new execution + payload = '' + } + } + + if (this.continuous && !this.stdInData && this.py != null){ + this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) + return + } + + var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec + + if (this.stdInData){ + if (!this.py){ + this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { + cwd: this.pydir, + detached: true + }); + this.firstExecution = true + } else { + this.firstExecution = false + } + } else { + this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { + cwd: this.pydir + }); + } + + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + + // subsequence message, no need to setup callbacks + if (this.stdInData && !this.firstExecution){ + this.py.stdin.write(payload + '\n') + return + } + + var py = this.py; + var dataString = ''; + var errString = ''; + + py.stdout.on('data', data => { + clearTimeout(this.standbyTimer) + + this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) + + let dataStr = data.toString(); + + dataString += dataStr; + + if (dataString.endsWith("\n")){ + if (this.continuous){ + msg.payload = dataString; + out(msg); + dataString = '' + } + } + + this.standbyTimer = setTimeout(()=>{ + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + }, 2000) + + }); + + py.stderr.on('data', data => { + errString += String(data);// just a different way to do it + this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) + }); + + py.stderr.on('error', console.log) + py.stdout.on('error', console.log) + py.stdin.on('error', console.log) + py.on('error', console.log) + + py.on('close', code =>{ + if (code){ + err('exit code: ' + code + ', ' + errString); + this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) + } else if (!this.continuous){ + msg.payload = dataString.trim(); + out(msg); + this.onStatus({fill:"green",shape:"dot",text:"Done"}) + } else { + this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) + } + this.py = null + setTimeout(()=>{ + this.onStatus({}) + }, 2000) + }); + + if (this.stdInData){ + py.stdin.write(payload + '\n') + } +}; + +PythonshellInNode.prototype.onClose = function() { + if (this.py){ + this.py.kill() + this.py = null + } +}; + +PythonshellInNode.prototype.setStatusCallback = function(callback) { + this.onStatus = callback +}; + + +module.exports = PythonshellInNode \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html new file mode 100644 index 000000000..2045d7745 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.html @@ -0,0 +1,149 @@ +f + + + + + + + \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js new file mode 100644 index 000000000..1727299e0 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/decisiontree.js @@ -0,0 +1,66 @@ +/** + * Copyright 2014 Sense Tecnic Systems, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + **/ + +var util = require("util"); +var httpclient; +var PythonshellNode = require('./decisionTree0'); + +module.exports = function(RED) { + "use strict"; + + function PythonshellInNode(n) { + RED.nodes.createNode(this,n); + + var node = this; + node.config = n; // copy config to the backend so that down bellow we can have a reference + + var pyNode = new PythonshellNode(n); + + pyNode.setStatusCallback(node.status.bind(node)) + + node.on("input",function(msg) { + pyNode.onInput(msg, function(result){ + node.send(result); + }, function(err){ + node.error(err); + }); + }); + + node.on('close', ()=>pyNode.onClose()); + } + + RED.nodes.registerType("decisionTree", PythonshellInNode); + + RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { + var node = RED.nodes.getNode(req.params.id); + if (node != null) { + try { + if (node.config.continuous){// see above comment + node.receive({payload: 'pythonshell@close'}) + } else { + node.receive(); + } + res.sendStatus(200); + } catch(err) { + res.sendStatus(500); + node.error(RED._("pythonshell.failed",{error:err.toString()})); + } + } else { + res.sendStatus(404); + } + }); + +} diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html new file mode 100644 index 000000000..35f46a295 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.html @@ -0,0 +1,149 @@ +f + + + + + + + \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js new file mode 100644 index 000000000..edabfa19e --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl.js @@ -0,0 +1,66 @@ +/** + * Copyright 2014 Sense Tecnic Systems, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + **/ + +var util = require("util"); +var httpclient; +var PythonshellNode = require('./fl0'); + +module.exports = function(RED) { + "use strict"; + + function PythonshellInNode(n) { + RED.nodes.createNode(this,n); + + var node = this; + node.config = n; // copy config to the backend so that down bellow we can have a reference + + var pyNode = new PythonshellNode(n); + + pyNode.setStatusCallback(node.status.bind(node)) + + node.on("input",function(msg) { + pyNode.onInput(msg, function(result){ + node.send(result); + }, function(err){ + node.error(err); + }); + }); + + node.on('close', ()=>pyNode.onClose()); + } + + RED.nodes.registerType("fl", PythonshellInNode); + + RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { + var node = RED.nodes.getNode(req.params.id); + if (node != null) { + try { + if (node.config.continuous){// see above comment + node.receive({payload: 'pythonshell@close'}) + } else { + node.receive(); + } + res.sendStatus(200); + } catch(err) { + res.sendStatus(500); + node.error(RED._("pythonshell.failed",{error:err.toString()})); + } + } else { + res.sendStatus(404); + } + }); + +} diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js new file mode 100644 index 000000000..ee8b3f222 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/fl0.js @@ -0,0 +1,147 @@ +var fs = require("fs"); + +function PythonshellInNode(config) { + if (!config.pyfile){ + throw 'pyfile not present'; + } + this.pythonExec = config.python3 ? "python3" : "python"; + this.pyfile = "/data/1.connect-kubeflow/py/fl.py"; + this.virtualenv = config.virtualenv; + + if (!fs.existsSync(this.pyfile)) { + throw 'pyfile not exist'; + } + + if (this.virtualenv && !fs.existsSync(this.virtualenv)){ + throw 'configured virtualenv not exist, consider remove or change'; + } + + this.stdInData = config.stdInData; + this.continuous = this.stdInData ? true : config.continuous; + this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); + this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); + this.spawn = require('child_process').spawn; + this.onStatus = ()=>{} +} + +PythonshellInNode.prototype.onInput = function(msg, out, err) { + payload = msg.payload || ''; + if (typeof payload === 'object'){ + payload = JSON.stringify(payload); + } else if (typeof payload !== 'string'){ + payload = payload.toString(); + } + + if (payload === 'pythonshell@close'){ + if (this.py != null){ + this.onClose() + return + } else { + // trigger new execution + payload = '' + } + } + + if (this.continuous && !this.stdInData && this.py != null){ + this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) + return + } + + var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec + + if (this.stdInData){ + if (!this.py){ + this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { + cwd: this.pydir, + detached: true + }); + this.firstExecution = true + } else { + this.firstExecution = false + } + } else { + this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { + cwd: this.pydir + }); + } + + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + + // subsequence message, no need to setup callbacks + if (this.stdInData && !this.firstExecution){ + this.py.stdin.write(payload + '\n') + return + } + + var py = this.py; + var dataString = ''; + var errString = ''; + + py.stdout.on('data', data => { + clearTimeout(this.standbyTimer) + + this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) + + let dataStr = data.toString(); + + dataString += dataStr; + + if (dataString.endsWith("\n")){ + if (this.continuous){ + msg.payload = dataString; + out(msg); + dataString = '' + } + } + + this.standbyTimer = setTimeout(()=>{ + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + }, 2000) + + }); + + py.stderr.on('data', data => { + errString += String(data);// just a different way to do it + this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) + }); + + py.stderr.on('error', console.log) + py.stdout.on('error', console.log) + py.stdin.on('error', console.log) + py.on('error', console.log) + + py.on('close', code =>{ + if (code){ + err('exit code: ' + code + ', ' + errString); + this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) + } else if (!this.continuous){ + msg.payload = dataString.trim(); + out(msg); + this.onStatus({fill:"green",shape:"dot",text:"Done"}) + } else { + this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) + } + this.py = null + setTimeout(()=>{ + this.onStatus({}) + }, 2000) + }); + + if (this.stdInData){ + py.stdin.write(payload + '\n') + } +}; + +PythonshellInNode.prototype.onClose = function() { + if (this.py){ + this.py.kill() + this.py = null + } +}; + +PythonshellInNode.prototype.setStatusCallback = function(callback) { + this.onStatus = callback +}; + + +module.exports = PythonshellInNode \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html new file mode 100644 index 000000000..322e0e99d --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.html @@ -0,0 +1,149 @@ +f + + + + + + + \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js new file mode 100644 index 000000000..3c3e04c13 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist.js @@ -0,0 +1,66 @@ +/** + * Copyright 2014 Sense Tecnic Systems, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + **/ + +var util = require("util"); +var httpclient; +var PythonshellNode = require('./mnist0'); + +module.exports = function(RED) { + "use strict"; + + function PythonshellInNode(n) { + RED.nodes.createNode(this,n); + + var node = this; + node.config = n; // copy config to the backend so that down bellow we can have a reference + + var pyNode = new PythonshellNode(n); + + pyNode.setStatusCallback(node.status.bind(node)) + + node.on("input",function(msg) { + pyNode.onInput(msg, function(result){ + node.send(result); + }, function(err){ + node.error(err); + }); + }); + + node.on('close', ()=>pyNode.onClose()); + } + + RED.nodes.registerType("mnist", PythonshellInNode); + + RED.httpAdmin.post("/pythonshell/:id", RED.auth.needsPermission("pythonshell.query"), function(req,res) { + var node = RED.nodes.getNode(req.params.id); + if (node != null) { + try { + if (node.config.continuous){// see above comment + node.receive({payload: 'pythonshell@close'}) + } else { + node.receive(); + } + res.sendStatus(200); + } catch(err) { + res.sendStatus(500); + node.error(RED._("pythonshell.failed",{error:err.toString()})); + } + } else { + res.sendStatus(404); + } + }); + +} diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js new file mode 100644 index 000000000..58ebc34a3 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/mnist0.js @@ -0,0 +1,147 @@ +var fs = require("fs"); + +function PythonshellInNode(config) { + if (!config.pyfile){ + throw 'pyfile not present'; + } + this.pythonExec = config.python3 ? "python3" : "python"; + this.pyfile = "/data/1.connect-kubeflow/py/mnist.py"; + this.virtualenv = config.virtualenv; + + if (!fs.existsSync(this.pyfile)) { + throw 'pyfile not exist'; + } + + if (this.virtualenv && !fs.existsSync(this.virtualenv)){ + throw 'configured virtualenv not exist, consider remove or change'; + } + + this.stdInData = config.stdInData; + this.continuous = this.stdInData ? true : config.continuous; + this.pydir = this.pyfile.substring(0, this.pyfile.lastIndexOf('/')); + this.pyfile = this.pyfile.substring(this.pyfile.lastIndexOf('/') + 1, this.pyfile.length); + this.spawn = require('child_process').spawn; + this.onStatus = ()=>{} +} + +PythonshellInNode.prototype.onInput = function(msg, out, err) { + payload = msg.payload || ''; + if (typeof payload === 'object'){ + payload = JSON.stringify(payload); + } else if (typeof payload !== 'string'){ + payload = payload.toString(); + } + + if (payload === 'pythonshell@close'){ + if (this.py != null){ + this.onClose() + return + } else { + // trigger new execution + payload = '' + } + } + + if (this.continuous && !this.stdInData && this.py != null){ + this.onStatus({fill:"yellow",shape:"dot",text:"Not accepting input"}) + return + } + + var spawnCmd = (this.virtualenv ? this.virtualenv + '/bin/' : '') + this.pythonExec + + if (this.stdInData){ + if (!this.py){ + this.py = this.spawn(spawnCmd, ['-u', this.pyfile], { + cwd: this.pydir, + detached: true + }); + this.firstExecution = true + } else { + this.firstExecution = false + } + } else { + this.py = this.spawn(spawnCmd, ['-u', this.pyfile, payload], { + cwd: this.pydir + }); + } + + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + + // subsequence message, no need to setup callbacks + if (this.stdInData && !this.firstExecution){ + this.py.stdin.write(payload + '\n') + return + } + + var py = this.py; + var dataString = ''; + var errString = ''; + + py.stdout.on('data', data => { + clearTimeout(this.standbyTimer) + + this.onStatus({fill:"green",shape:"dot",text:"Processing data"}) + + let dataStr = data.toString(); + + dataString += dataStr; + + if (dataString.endsWith("\n")){ + if (this.continuous){ + msg.payload = dataString; + out(msg); + dataString = '' + } + } + + this.standbyTimer = setTimeout(()=>{ + this.onStatus({fill:"green",shape:"dot",text:"Standby"}) + }, 2000) + + }); + + py.stderr.on('data', data => { + errString += String(data);// just a different way to do it + this.onStatus({fill:"red",shape:"dot",text:"Error: " + errString}) + }); + + py.stderr.on('error', console.log) + py.stdout.on('error', console.log) + py.stdin.on('error', console.log) + py.on('error', console.log) + + py.on('close', code =>{ + if (code){ + err('exit code: ' + code + ', ' + errString); + this.onStatus({fill:"red",shape:"dot",text:"Exited: " + code}) + } else if (!this.continuous){ + msg.payload = dataString.trim(); + out(msg); + this.onStatus({fill:"green",shape:"dot",text:"Done"}) + } else { + this.onStatus({fill:"yellow",shape:"dot",text:"Script Closed"}) + } + this.py = null + setTimeout(()=>{ + this.onStatus({}) + }, 2000) + }); + + if (this.stdInData){ + py.stdin.write(payload + '\n') + } +}; + +PythonshellInNode.prototype.onClose = function() { + if (this.py){ + this.py.kill() + this.py = null + } +}; + +PythonshellInNode.prototype.setStatusCallback = function(callback) { + this.onStatus = callback +}; + + +module.exports = PythonshellInNode \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json new file mode 100644 index 000000000..fcf46cd21 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/node_modules/nodepipe/package.json @@ -0,0 +1,30 @@ +{ + "name": "node-red-contrib-pythonshell-custom", + "version": "1.0.0", + "description": "modified version of node-red-contrib-pythonshell with additional features", + "scripts": { + "test": "./node_modules/mocha/bin/mocha" + }, + "keywords": [ + "distributed", + "python", + "node", + "node-red" + ], + "author": "WuChunYen", + "license": "ISC", + "dependencies": {}, + "node-red": { + "nodes": { + "decisionTree": "decisiontree.js", + "randomForest": "randomforest.js", + "logisticRegression": "logisticregression.js", + "fl": "fl.js", + "mnist": "mnist.js", + "LSTM": "LSTM.js" + } + }, + "devDependencies": { + "mocha": "^5.0.4" + } +} diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/package.json b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/package.json new file mode 100644 index 000000000..b788fd491 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/package.json @@ -0,0 +1,6 @@ +{ + "name": "node-red-project", + "description": "A Node-RED Project", + "version": "0.0.1", + "private": true +} \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/LSTM.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/LSTM.py new file mode 100644 index 000000000..0eb2cb76a --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/LSTM.py @@ -0,0 +1,132 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "https://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for LSTM." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/LSTM_pipeline.yaml' + name='pipeline-' + random_suffix() + description="This is a LSTM pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'LSTM' + random_suffix() + description = "This is a LSTM run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'random-forest-classifier-Accuracy': #change parameter + output_value = parameter['value'] + + if output_value is not None: + print(f"Random Forest Classifier Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/README.md b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/README.md new file mode 100644 index 000000000..06bb167e4 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/README.md @@ -0,0 +1,18 @@ +## How to use +``` +KUBEFLOW_HOST= \ +KUBEFLOW_USERNAME= \ +KUBEFLOW_PASSWORD= \ +python3 +``` +## three pipelines +流程架構: +create_experiment -> upload_pipeline -> create_run -> get_run -> filter -> result +### decisionTree.py +功能：以乳癌資料集訓練decisionTree模型並回傳準確率 + +### logisticRegression.py +功能：以乳癌資料集訓練logisticRegression模型並回傳準確率 + +### randomForest.py +功能：以乳癌資料集訓練randomForest模型並回傳準確率 diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/__pycache__/kfp_login.cpython-38.pyc b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/__pycache__/kfp_login.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..193a91af9ab988978f2373b5b3ad1d940277f916 GIT binary patch literal 1992 zcmah}&2JM&6rb5IZ=AIg0;F(h=YRx`iqlH%#dQ(1Q2G(2Aqh$qR5|ONak9>OXPKEL z#Bwf$V^QguR7%uS@BJfs?0K*4g@1txrEg|!6rfUPwe#`jz2Cg=yR}*s!MJn%9{aJ5 z&|ms+dbn`74!`^eCWQUXVas24K=*yAc>Y6 zxbSn4$SjFf!zpao13@y8h67gkSt#Wm7qsvMQ@KbZTT6{c1y-%*=kUu17@-j;GXAVX z_rO-@2p^-T_@~taXXNxPxVNeE#5!+9^oG_P~4GC z{Weniw(x`-a(15ZsIRcSEt`qRgj z_#M^&C^Z_;nxWaW{$#>6Gv@kp(I0iDjaQTmzD*-+lp`zaRwYnmIbH?#)~K$y{J z7tsihEPCe90;IVo_JMoaESfq@3N+Qm8W7agl8#h*$zgoWCB0Nn>$t|fu6-O)M43S#~YisG|jTPAD 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a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_experiment.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_experiment.py new file mode 100644 index 000000000..8ac8d7321 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_experiment.py @@ -0,0 +1,59 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="" # str | The ID of the name to be create. + description='' # str | The description experiment. + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + api_response = api_instance.create_experiment(body) + pprint(api_response) + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py new file mode 100644 index 000000000..b02691de7 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/create_run.py @@ -0,0 +1,58 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from pprint import pprint +from kfp_server_api.rest import ApiException +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.RunServiceApi(api_client) + pipeline_id = '' # str | The ID of the pipeline. + experiment_id = '' # str | The ID of the experiment. + display_name = '' # str | The name of the run to be create. + description = '' # str | The description of run. + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + + try: + # Creates a new run. + api_response = api_instance.create_run(body) + pprint(api_response) + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py new file mode 100644 index 000000000..31d9205c7 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_experiment.py @@ -0,0 +1,51 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.ExperimentServiceApi(api_client) + experiment_id = '' # str | The ID of the experiment to be deleted. + + try: + # Deletes an experiment without deleting the experiment's runs and recurring runs. + # To avoid unexpected behaviors, delete an experiment's runs and recurring runs before deleting the experiment. + api_response = api_instance.delete_experiment(experiment_id) + pprint(api_response) + except ApiException as e: + print("Exception when calling ExperimentServiceApi->delete_experiment: %s\n" % e) \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py new file mode 100644 index 000000000..c04d595ef --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_pipeline.py @@ -0,0 +1,50 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.PipelineServiceApi(api_client) + id = '' # str | The ID of the pipeline to be deleted. + + try: + # Deletes a pipeline and its pipeline versions. + api_response = api_instance.delete_pipeline(id) + pprint(api_response) + except ApiException as e: + print("Exception when calling PipelineServiceApi->delete_pipeline: %s\n" % e) \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py new file mode 100644 index 000000000..aefc7dca3 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/delete_run.py @@ -0,0 +1,50 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +import kfp +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.RunServiceApi(api_client) + id = '' # str | The ID of the run to be deleted. + + try: + # Deletes a run. + api_response = api_instance.delete_run(id) + pprint(api_response) + except ApiException as e: + print("Exception when calling RunServiceApi->delete_run: %s\n" % e) \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py new file mode 100644 index 000000000..f62c56bdc --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/get_run.py @@ -0,0 +1,64 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces +import json + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.RunServiceApi(api_client) + run_id = '' # str | The ID of the run to be retrieved. + + try: + # Finds a specific run by ID. + api_response = api_instance.get_run(run_id) + output = api_response.pipeline_runtime.workflow_manifest + output = json.loads(output) + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + output_value = None + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'decision-tree-classifier-Accuracy': + output_value = parameter['value'] + break + + if output_value is not None: + print(f"Decision Tree Classifier Accuracy: {output_value}") + else: + print("Parameter not found.") + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py new file mode 100644 index 000000000..7c95abcf0 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_login.py @@ -0,0 +1,93 @@ +import re +from urllib.parse import urlsplit + +import requests + +# NOTE: the following code is referred from https://github.com/kubeflow/website/issues/2916 +def get_istio_auth_session(url: str, username: str, password: str) -> dict: + """ + Determine if the specified URL is secured by Dex and try to obtain a session cookie. + WARNING: only Dex `staticPasswords` and `LDAP` authentication are currently supported + (we default default to using `staticPasswords` if both are enabled) + + :param url: Kubeflow server URL, including protocol + :param username: Dex `staticPasswords` or `LDAP` username + :param password: Dex `staticPasswords` or `LDAP` password + :return: auth session information + """ + # define the default return object + auth_session = { + "endpoint_url": url, # KF endpoint URL + "redirect_url": None, # KF redirect URL, if applicable + "dex_login_url": None, # Dex login URL (for POST of credentials) + "is_secured": None, # True if KF endpoint is secured + "session_cookie": None # Resulting session cookies in the form "key1=value1; key2=value2" + } + + # use a persistent session (for cookies) + with requests.Session() as s: + + ################ + # Determine if Endpoint is Secured + ################ + resp = s.get(url, allow_redirects=True) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {url}" + ) + + auth_session["redirect_url"] = resp.url + + # if we were NOT redirected, then the endpoint is UNSECURED + if len(resp.history) == 0: + auth_session["is_secured"] = False + return auth_session + else: + auth_session["is_secured"] = True + + ################ + # Get Dex Login URL + ################ + redirect_url_obj = urlsplit(auth_session["redirect_url"]) + + # if we are at `/auth?=xxxx` path, we need to select an auth type + if re.search(r"/auth$", redirect_url_obj.path): + # default to "staticPasswords" auth type + redirect_url_obj = redirect_url_obj._replace( + path=re.sub(r"/auth$", "/auth/local", redirect_url_obj.path) + ) + + # if we are at `/auth/xxxx/login` path, then no further action is needed (we can use it for login POST) + if re.search(r"/auth/.*/login$", redirect_url_obj.path): + auth_session["dex_login_url"] = redirect_url_obj.geturl() + + # else, we need to be redirected to the actual login page + else: + # this GET should redirect us to the `/auth/xxxx/login` path + resp = s.get(redirect_url_obj.geturl(), allow_redirects=True) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {redirect_url_obj.geturl()}" + ) + + # set the login url + auth_session["dex_login_url"] = resp.url + + ################ + # Attempt Dex Login + ################ + resp = s.post( + auth_session["dex_login_url"], + data={"login": username, "password": password}, + allow_redirects=True + ) + if len(resp.history) == 0: + raise RuntimeError( + f"Login credentials were probably invalid - " + f"No redirect after POST to: {auth_session['dex_login_url']}" + ) + + # store the session cookies in a "key1=value1; key2=value2" string + auth_session["session_cookie"] = "; ".join([f"{c.name}={c.value}" for c in s.cookies]) + + return auth_session \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py new file mode 100644 index 000000000..3c7c0dfdc --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/kfp_namespace.py @@ -0,0 +1,19 @@ +import os +import requests + +from typing import TypedDict + +def retrieve_namespaces(host: str, auth_session: TypedDict) -> str: + workgroup_endpoint = os.path.join(host, "api/workgroup/env-info") + + cookies = {} + cookie_tokens = auth_session["session_cookie"].split("=") + print(cookie_tokens[0]) + cookies[cookie_tokens[0]]=cookie_tokens[1] + resp = requests.get(workgroup_endpoint, cookies=cookies) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {workgroup_endpoint}" + ) + return [ns["namespace"] for ns in resp.json()["namespaces"] if + ns["role"]=="owner"] \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py new file mode 100644 index 000000000..2e4729bea --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_experiments.py @@ -0,0 +1,44 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.ExperimentServiceApi(api_client) + resource_reference_key_type = "NAMESPACE" + resource_reference_key_id = namespaces[0] + list_experiment_response = api_instance.list_experiment(resource_reference_key_type=resource_reference_key_type, resource_reference_key_id=resource_reference_key_id) + for experiment in list_experiment_response.experiments: + pprint(experiment) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py new file mode 100644 index 000000000..6e2be4b5e --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/list_pipelines.py @@ -0,0 +1,43 @@ +from __future__ import print_function +import string +import time +import kfp_server_api +import os +import requests +import kfp +import json +from pprint import pprint +from kfp_server_api.rest import ApiException +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + api_instance = kfp_server_api.PipelineServiceApi(api_client) + list_pipeline_response = api_instance.list_pipelines() + for pipelines in list_pipeline_response.pipelines: + print(pipelines) \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py new file mode 100644 index 000000000..410cf50aa --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/api_examples/upload_pipeline.py @@ -0,0 +1,52 @@ +from __future__ import print_function +import string +import random +import time +import kfp_server_api +import os +import requests +from kfp_server_api.rest import ApiException + +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + + +host = os.getenv("KUBEFLOW_HOST") +username = os.getenv("KUBEFLOW_USERNAME") +password = os.getenv("KUBEFLOW_PASSWORD") + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='' # The yaml file in your local path. + name='pipeline-' + random_suffix() + description='' # str | The description of pipeline. + try: + api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + print(api_response) + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py new file mode 100644 index 000000000..16293f1d6 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/decisionTree.py @@ -0,0 +1,132 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "http://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for only_decision_tree." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/only_decision_tree.yaml' + name='pipeline-' + random_suffix() + description="This is a only_decision_tree pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'run_only_decision_tree' + random_suffix() + description = "This is a only_decision_tree run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'decision-tree-classifier-Accuracy': + output_value = parameter['value'] + + if output_value is not None: + print(f"Decision Tree Classifier Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/fl.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/fl.py new file mode 100644 index 000000000..6aeb83f9d --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/fl.py @@ -0,0 +1,132 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "https://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for fl." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/fl_pipeline.yaml' + name='pipeline-' + random_suffix() + description="This is a fl pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'fl' + random_suffix() + description = "This is a fl run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'client-last_accuracy': + output_value = parameter['value'] + + if output_value is not None: + print(f"fl Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/helloworld.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/helloworld.py new file mode 100644 index 000000000..97e823fb5 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/helloworld.py @@ -0,0 +1,6 @@ +import sys + +while True: + line = sys.stdin.readline() + print('this is send from python') + print(line) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/install.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/install.py new file mode 100644 index 000000000..470d38321 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/install.py @@ -0,0 +1,23 @@ +import os +import subprocess + +def python3_version(): + return subprocess.check_call(["python3", "--version"]) + +def which(command): + return subprocess.check_call(["which", command]) + +def pip3_install_requirements(): + return subprocess.check_call(["pip3", "install", "-r", "requirements.txt", + "--user"]) + +def pip3_install_kfp(): + return subprocess.check_call(["pip3", "install", + "git+https://github.com/kubeflow/pipelines.git@1.8.19#subdirectory=backend/api/python_http_client", + "--user"]) + +python3_version() +pip3_install_requirements() +pip3_install_kfp() + +print("done") diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py new file mode 100644 index 000000000..da7ef822f --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_example.py @@ -0,0 +1,44 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +from kfp_server_api.rest import ApiException + +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "http://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +print("available namespace: {}".format(namespaces)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the API class + api_instance = kfp_server_api.ExperimentServiceApi(api_client) + resource_reference_key_type = "NAMESPACE" + resource_reference_key_id = namespaces[0] + list_experiment_response = api_instance.list_experiment(resource_reference_key_type=resource_reference_key_type, resource_reference_key_id=resource_reference_key_id) + for experiment in list_experiment_response.experiments: + print(experiment) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py new file mode 100644 index 000000000..7c95abcf0 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_login.py @@ -0,0 +1,93 @@ +import re +from urllib.parse import urlsplit + +import requests + +# NOTE: the following code is referred from https://github.com/kubeflow/website/issues/2916 +def get_istio_auth_session(url: str, username: str, password: str) -> dict: + """ + Determine if the specified URL is secured by Dex and try to obtain a session cookie. + WARNING: only Dex `staticPasswords` and `LDAP` authentication are currently supported + (we default default to using `staticPasswords` if both are enabled) + + :param url: Kubeflow server URL, including protocol + :param username: Dex `staticPasswords` or `LDAP` username + :param password: Dex `staticPasswords` or `LDAP` password + :return: auth session information + """ + # define the default return object + auth_session = { + "endpoint_url": url, # KF endpoint URL + "redirect_url": None, # KF redirect URL, if applicable + "dex_login_url": None, # Dex login URL (for POST of credentials) + "is_secured": None, # True if KF endpoint is secured + "session_cookie": None # Resulting session cookies in the form "key1=value1; key2=value2" + } + + # use a persistent session (for cookies) + with requests.Session() as s: + + ################ + # Determine if Endpoint is Secured + ################ + resp = s.get(url, allow_redirects=True) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {url}" + ) + + auth_session["redirect_url"] = resp.url + + # if we were NOT redirected, then the endpoint is UNSECURED + if len(resp.history) == 0: + auth_session["is_secured"] = False + return auth_session + else: + auth_session["is_secured"] = True + + ################ + # Get Dex Login URL + ################ + redirect_url_obj = urlsplit(auth_session["redirect_url"]) + + # if we are at `/auth?=xxxx` path, we need to select an auth type + if re.search(r"/auth$", redirect_url_obj.path): + # default to "staticPasswords" auth type + redirect_url_obj = redirect_url_obj._replace( + path=re.sub(r"/auth$", "/auth/local", redirect_url_obj.path) + ) + + # if we are at `/auth/xxxx/login` path, then no further action is needed (we can use it for login POST) + if re.search(r"/auth/.*/login$", redirect_url_obj.path): + auth_session["dex_login_url"] = redirect_url_obj.geturl() + + # else, we need to be redirected to the actual login page + else: + # this GET should redirect us to the `/auth/xxxx/login` path + resp = s.get(redirect_url_obj.geturl(), allow_redirects=True) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {redirect_url_obj.geturl()}" + ) + + # set the login url + auth_session["dex_login_url"] = resp.url + + ################ + # Attempt Dex Login + ################ + resp = s.post( + auth_session["dex_login_url"], + data={"login": username, "password": password}, + allow_redirects=True + ) + if len(resp.history) == 0: + raise RuntimeError( + f"Login credentials were probably invalid - " + f"No redirect after POST to: {auth_session['dex_login_url']}" + ) + + # store the session cookies in a "key1=value1; key2=value2" string + auth_session["session_cookie"] = "; ".join([f"{c.name}={c.value}" for c in s.cookies]) + + return auth_session \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py new file mode 100644 index 000000000..3c7c0dfdc --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/kfp_namespace.py @@ -0,0 +1,19 @@ +import os +import requests + +from typing import TypedDict + +def retrieve_namespaces(host: str, auth_session: TypedDict) -> str: + workgroup_endpoint = os.path.join(host, "api/workgroup/env-info") + + cookies = {} + cookie_tokens = auth_session["session_cookie"].split("=") + print(cookie_tokens[0]) + cookies[cookie_tokens[0]]=cookie_tokens[1] + resp = requests.get(workgroup_endpoint, cookies=cookies) + if resp.status_code != 200: + raise RuntimeError( + f"HTTP status code '{resp.status_code}' for GET against: {workgroup_endpoint}" + ) + return [ns["namespace"] for ns in resp.json()["namespaces"] if + ns["role"]=="owner"] \ No newline at end of file diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/mnist.py b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/mnist.py new file mode 100644 index 000000000..fc2911a05 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/mnist.py @@ -0,0 +1,132 @@ +from __future__ import print_function + +import time +import kfp_server_api +import os +import requests +import string +import random +import json +from kfp_server_api.rest import ApiException +from pprint import pprint +from kfp_login import get_istio_auth_session +from kfp_namespace import retrieve_namespaces + +host = "https://ai4edu.thu01.footprint-ai.com" +username = "thu02" +password = "M*$3sNF7" + +auth_session = get_istio_auth_session( + url=host, + username=username, + password=password + ) + +# The client must configure the authentication and authorization parameters +# in accordance with the API server security policy. +# Examples for each auth method are provided below, use the example that +# satisfies your auth use case. + +# Configure API key authorization: Bearer +configuration = kfp_server_api.Configuration( + host = os.path.join(host, "pipeline"), +) +configuration.debug = True + +namespaces = retrieve_namespaces(host, auth_session) +#print("available namespace: {}".format(namespaces)) + +def random_suffix() -> string: + return ''.join(random.choices(string.ascii_lowercase + string.digits, k=10)) + +# Enter a context with an instance of the API client +with kfp_server_api.ApiClient(configuration, cookie=auth_session["session_cookie"]) as api_client: + # Create an instance of the Experiment API class + experiment_api_instance = kfp_server_api.ExperimentServiceApi(api_client) + name="experiment-" + random_suffix() + description="This is a experiment for mnist." + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey( + type=kfp_server_api.models.ApiResourceType.NAMESPACE, + id=resource_reference_key_id + ), + relationship=kfp_server_api.models.ApiRelationship.OWNER + )] + body = kfp_server_api.ApiExperiment(name=name, description=description, resource_references=resource_references) # ApiExperiment | The experiment to be created. + try: + # Creates a new experiment. + experiment_api_response = experiment_api_instance.create_experiment(body) + experiment_id = experiment_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling ExperimentServiceApi->create_experiment: %s\n" % e) + + # Create an instance of the pipeline API class + api_instance = kfp_server_api.PipelineUploadServiceApi(api_client) + uploadfile='pipelines/mnist_pipeline.yaml' + name='pipeline-' + random_suffix() + description="This is a mnist pipline." + try: + pipeline_api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description) + pipeline_id = pipeline_api_response.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e) + + # Create an instance of the run API class + run_api_instance = kfp_server_api.RunServiceApi(api_client) + display_name = 'mnist' + random_suffix() + description = "This is a mnist run." + pipeline_spec = kfp_server_api.ApiPipelineSpec(pipeline_id=pipeline_id) + resource_reference_key_id = namespaces[0] + resource_references=[kfp_server_api.models.ApiResourceReference( + key=kfp_server_api.models.ApiResourceKey(id=experiment_id, type=kfp_server_api.models.ApiResourceType.EXPERIMENT), + relationship=kfp_server_api.models.ApiRelationship.OWNER )] + body = kfp_server_api.ApiRun(name=display_name, description=description, pipeline_spec=pipeline_spec, resource_references=resource_references) # ApiRun | + try: + # Creates a new run. + run_api_response = run_api_instance.create_run(body) + run_id = run_api_response.run.id # str | The ID of the run to be retrieved. + except ApiException as e: + print("Exception when calling RunServiceApi->create_run: %s\n" % e) + + Completed_flag = False + polling_interval = 10 # Time in seconds between polls + + while not Completed_flag: + try: + time.sleep(1) + # Finds a specific run by ID. + api_instance = run_api_instance.get_run(run_id) + output = api_instance.pipeline_runtime.workflow_manifest + output = json.loads(output) + + try: + nodes = output['status']['nodes'] + conditions = output['status']['conditions'] # Comfirm completion. + + except KeyError: + nodes = {} + conditions = [] + + output_value = None + Completed_flag = conditions[1]['status'] if len(conditions) > 1 else False + + except ApiException as e: + print("Exception when calling RunServiceApi->get_run: %s\n" % e) + break + + if not Completed_flag: + print("Pipeline is still running. Waiting...") + time.sleep(polling_interval-1) + + for node_id, node in nodes.items(): + if 'inputs' in node and 'parameters' in node['inputs']: + for parameter in node['inputs']['parameters']: + if parameter['name'] == 'client-last_accuracy': + output_value = parameter['value'] + + if output_value is not None: + print(f"fl Accuracy: {output_value}") + else: + print("Parameter not found.") + print(nodes) diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml new file mode 100644 index 000000000..11f53462f --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/LSTM_pipeline.yaml @@ -0,0 +1,651 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: fl-test- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2024-01-02T05:53:41.650368', + pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "kubeflow-user-thu01", + "name": "namespace", "optional": true}], "name": "FL test"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: fl-test + templates: + - name: client + container: + args: [--batch, '1', '----output-paths', /tmp/outputs/last_accuracy/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def client(batch):\n import json\n import requests\n import time\n\ + \ import pandas as pd\n import numpy as np\n import tensorflow as\ + \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ + \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ + \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ + \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ + \ tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import\ + \ Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras\ + \ import backend as K\n\n normal_url='https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'\ + \ \n abnormal_url='https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download'\n\ + \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ + \ num_features = len(normal_data.columns)\n print(num_features)\n \ + \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ + \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ + \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ + \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ + \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ + \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ + \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ + \n input_shape = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n\ + \ @staticmethod\n def build(shape, classes):\n model\ + \ = Sequential()\n model.add(LSTM(units=64, input_shape=input_shape,\ + \ return_sequences=True))\n model.add(Dropout(0.2))\n \ + \ model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n \ + \ model.add(Dense(units=num_classes, activation='softmax'))\n\n \ + \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ + \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ + \ #The client should have its own data, not like this. It's a lazy method.\n\ + \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ + \n #seperate shard into data and labels lists\n data, label\ + \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ + \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ + \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ + \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ + \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ + \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ + \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ + \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ + \ \n optimizer=optimizer, \n metrics=metrics)\n\ + \n if(comm_round == 0):\n history = client_model.fit(dataset,\ + \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ + \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ + \ test_loss, test_accuracy = client_model.evaluate(dataset)\n \ + \ print(f'Test accuracy: {test_accuracy}')\n\n local_weight = client_model.get_weights()\n\ + \ local_weight = [np.array(w).tolist() for w in local_weight]\n\n \ + \ client_data = {\"local_count\": local_count,'bs': bs, 'local_weight':\ + \ json.dumps(local_weight)}\n\n while True:\n try:\n \ + \ weight = (requests.post(server_url,data=client_data))\n\n \ + \ if weight.status_code == 200:\n print(f\"\ + exist\")\n\n break\n else:\n \ + \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ + \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ + \ data = weight.json()\n avg_weight = data.get('result')\n \ + \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ + \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ + \ \n try:\n response = requests.get(shutdown_url)\n except\ + \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ + \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ + \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ + \ str:\n if isinstance(float_value, str):\n return float_value\n\ + \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ + \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ + \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ + _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ + --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ + _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ + \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ + _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ + \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + outputs: + parameters: + - name: client-last_accuracy + valueFrom: {path: /tmp/outputs/last_accuracy/data} + artifacts: + - {name: client-last_accuracy, path: /tmp/outputs/last_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": + "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' + || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def client(batch):\n import json\n import requests\n import time\n import + pandas as pd\n import numpy as np\n import tensorflow as tf\n from + tensorflow.keras.models import Sequential\n from tensorflow.keras.layers + import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from + tensorflow.keras.layers import Activation\n from tensorflow.keras.layers + import Flatten\n from tensorflow.keras.layers import Dense\n from + tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import + Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras + import backend as K\n\n normal_url=''https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'' + \n abnormal_url=''https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download''\n normal_data + = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features + = len(normal_data.columns)\n print(num_features)\n normal_label = + np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, + 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label + = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data + = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), + num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data + = list(zip(data, data_label))\n data_length=len(full_data)\n\n input_shape + = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n @staticmethod\n def + build(shape, classes):\n model = Sequential()\n model.add(LSTM(units=64, + input_shape=input_shape, return_sequences=True))\n model.add(Dropout(0.2))\n model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n model.add(Dense(units=num_classes, + activation=''softmax''))\n\n return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] + #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] + #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data + len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate + shard into data and labels lists\n data, label = zip(*data_shard)\n dataset + = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return + dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs + = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics + = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for + comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model + = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, + \n metrics=metrics)\n\n if(comm_round == 0):\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n\n test_loss, test_accuracy + = client_model.evaluate(dataset)\n print(f''Test accuracy: {test_accuracy}'')\n\n local_weight + = client_model.get_weights()\n local_weight = [np.array(w).tolist() + for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': + bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight + = (requests.post(server_url,data=client_data))\n\n if weight.status_code + == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server + error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not + exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight + = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight + = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response + = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already + shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef + _serialize_float(float_value: float) -> str:\n if isinstance(float_value, + str):\n return float_value\n if not isinstance(float_value, (float, + int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + float.''.format(\n str(float_value), str(type(float_value))))\n return + str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', + description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, + required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": + "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": + "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": + "1"}'} + - name: client-2 + container: + args: [--batch, '2', '----output-paths', /tmp/outputs/last_accuracy/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def client(batch):\n import json\n import requests\n import time\n\ + \ import pandas as pd\n import numpy as np\n import tensorflow as\ + \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ + \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ + \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ + \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ + \ tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import\ + \ Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras\ + \ import backend as K\n\n normal_url='https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'\ + \ \n abnormal_url='https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download'\n\ + \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ + \ num_features = len(normal_data.columns)\n print(num_features)\n \ + \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ + \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ + \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ + \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ + \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ + \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ + \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ + \n input_shape = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n\ + \ @staticmethod\n def build(shape, classes):\n model\ + \ = Sequential()\n model.add(LSTM(units=64, input_shape=input_shape,\ + \ return_sequences=True))\n model.add(Dropout(0.2))\n \ + \ model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n \ + \ model.add(Dense(units=num_classes, activation='softmax'))\n\n \ + \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ + \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ + \ #The client should have its own data, not like this. It's a lazy method.\n\ + \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ + \n #seperate shard into data and labels lists\n data, label\ + \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ + \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ + \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ + \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ + \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ + \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ + \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ + \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ + \ \n optimizer=optimizer, \n metrics=metrics)\n\ + \n if(comm_round == 0):\n history = client_model.fit(dataset,\ + \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ + \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ + \ test_loss, test_accuracy = client_model.evaluate(dataset)\n \ + \ print(f'Test accuracy: {test_accuracy}')\n\n local_weight = client_model.get_weights()\n\ + \ local_weight = [np.array(w).tolist() for w in local_weight]\n\n \ + \ client_data = {\"local_count\": local_count,'bs': bs, 'local_weight':\ + \ json.dumps(local_weight)}\n\n while True:\n try:\n \ + \ weight = (requests.post(server_url,data=client_data))\n\n \ + \ if weight.status_code == 200:\n print(f\"\ + exist\")\n\n break\n else:\n \ + \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ + \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ + \ data = weight.json()\n avg_weight = data.get('result')\n \ + \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ + \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ + \ \n try:\n response = requests.get(shutdown_url)\n except\ + \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ + \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ + \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ + \ str:\n if isinstance(float_value, str):\n return float_value\n\ + \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ + \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ + \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ + _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ + --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ + _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ + \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ + _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ + \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + outputs: + artifacts: + - {name: client-2-last_accuracy, path: /tmp/outputs/last_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": + "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' + || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def client(batch):\n import json\n import requests\n import time\n import + pandas as pd\n import numpy as np\n import tensorflow as tf\n from + tensorflow.keras.models import Sequential\n from tensorflow.keras.layers + import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from + tensorflow.keras.layers import Activation\n from tensorflow.keras.layers + import Flatten\n from tensorflow.keras.layers import Dense\n from + tensorflow.keras.layers import LSTM\n from tensorflow.keras.layers import + Dropout\n from tensorflow.keras.optimizers import SGD\n from tensorflow.keras + import backend as K\n\n normal_url=''https://drive.google.com/uc?id=16SaNuh7P_UTIMKLX-7umTnDe27uKYRsK&export=download'' + \n abnormal_url=''https://drive.google.com/uc?id=1INzlvIOGcLAgXwSL-0ktN6hhy--gjtwp&export=download''\n normal_data + = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features + = len(normal_data.columns)\n print(num_features)\n normal_label = + np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, + 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label + = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data + = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), + num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data + = list(zip(data, data_label))\n data_length=len(full_data)\n\n input_shape + = (17, 1)\n num_classes = 2\n\n class SimpleMLP:\n @staticmethod\n def + build(shape, classes):\n model = Sequential()\n model.add(LSTM(units=64, + input_shape=input_shape, return_sequences=True))\n model.add(Dropout(0.2))\n model.add(LSTM(units=64))\n model.add(Dropout(0.2))\n model.add(Dense(units=num_classes, + activation=''softmax''))\n\n return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] + #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] + #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data + len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate + shard into data and labels lists\n data, label = zip(*data_shard)\n dataset + = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return + dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs + = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics + = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for + comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model + = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, + \n metrics=metrics)\n\n if(comm_round == 0):\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n\n test_loss, test_accuracy + = client_model.evaluate(dataset)\n print(f''Test accuracy: {test_accuracy}'')\n\n local_weight + = client_model.get_weights()\n local_weight = [np.array(w).tolist() + for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': + bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight + = (requests.post(server_url,data=client_data))\n\n if weight.status_code + == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server + error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not + exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight + = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight + = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response + = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already + shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef + _serialize_float(float_value: float) -> str:\n if isinstance(float_value, + str):\n return float_value\n if not isinstance(float_value, (float, + int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + float.''.format(\n str(float_value), str(type(float_value))))\n return + str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', + description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, + required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": + "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": + "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": + "2"}'} + - name: delete-service + resource: + action: delete + flags: [--wait=false] + manifest: | + apiVersion: v1 + kind: Service + metadata: + name: http-service + spec: + ports: + - port: 80 + protocol: TCP + targetPort: 8080 + selector: + app: http-service + type: NodePort + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: fl-test + dag: + tasks: + - {name: client, template: client} + - {name: client-2, template: client-2} + - name: delete-service + template: delete-service + dependencies: [server] + - {name: http-service, template: http-service} + - name: server + template: server + dependencies: [http-service] + - name: show-results + template: show-results + dependencies: [client] + arguments: + parameters: + - {name: client-last_accuracy, value: '{{tasks.client.outputs.parameters.client-last_accuracy}}'} + - name: http-service + resource: + action: create + manifest: | + apiVersion: v1 + kind: Service + metadata: + name: http-service + spec: + ports: + - port: 5000 + protocol: TCP + targetPort: 8080 + selector: + app: http-service + outputs: + parameters: + - name: http-service-manifest + valueFrom: {jsonPath: '{}'} + - name: http-service-name + valueFrom: {jsonPath: '{.metadata.name}'} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: server + container: + args: [] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'flask' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'flask' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def server():\n import json\n import pandas as pd\n import numpy\ + \ as np\n import pickle\n import threading\n import time\n import\ + \ tensorflow as tf\n from flask import Flask, jsonify,request\n import\ + \ os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list\ + \ = []\n global_value = { #Share variable\n 'last_run_statue'\ + \ : False, #last run finish or not\n 'data_statue' : None,\ + \ #global_count finish or not\n 'global_count' : None,\n\ + \ 'scale_statue' : None,\n 'weight_statue'\ + \ : None,\n 'average_weights' : None,\n \ + \ 'shutdown' : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n \ + \ init_lock = threading.Lock()\n clients_local_count_lock = threading.Lock()\n\ + \ scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock\ + \ = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n\ + \ def before_request():\n print('get request')\n\n @app.route('/data',\ + \ methods=['POST'])\n def flask_server():\n with init_lock: #check\ + \ last run is finish and init varible\n\n while True:\n\n \ + \ if(len(clients_local_count)==0 and global_value['last_run_statue']\ + \ == False):#init the variable by first client enter\n \ + \ global_value['last_run_statue'] = True\n global_value['data_statue']\ + \ = False\n global_value['scale_statue'] = False\n \ + \ global_value['weight_statue'] = False\n \ + \ break\n\n elif(global_value['last_run_statue'] == True):\n\ + \ break\n time.sleep(3)\n\n local_count\ + \ = int(request.form.get('local_count')) #get data\n bs =\ + \ int(request.form.get('bs'))\n local_weight = json.loads(request.form.get('local_weight'))\n\ + \ local_weight = [np.array(lst) for lst in local_weight]\n\n \ + \ def scale_model_weights(weight, scalar):\n weight_final = []\n\ + \ steps = len(weight)\n for i in range(steps):\n \ + \ weight_final.append(scalar * weight[i])\n return\ + \ weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n \ + \ avg_grad = list()\n #get the average grad accross all\ + \ client gradients\n for grad_list_tuple in zip(*scaled_weight_list):\n\ + \ layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)\n\ + \ avg_grad.append(layer_mean)\n\n return avg_grad\n\ + \n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\ + \n with scaled_local_weight_list_lock:\n while True:\n\n\ + \ if (len(clients_local_count) == NUM_OF_CLIENTS and global_value['data_statue']\ + \ != True):\n global_value['last_run_statue'] = False\n\ + \ sum_of_local_count=sum(clients_local_count)\n\n \ + \ global_value['global_count'] = sum_of_local_count \n\n\ + \ scaling_factor=local_count/global_value['global_count']\n\ + \ scaled_weights = scale_model_weights(local_weight, scaling_factor)\n\ + \ scaled_local_weight_list.append(scaled_weights)\n\n \ + \ global_value['scale_statue'] = True \n \ + \ global_value['data_statue'] = True\n break\n \ + \ elif (global_value['data_statue'] == True and global_value['scale_statue']\ + \ == True):\n scaling_factor=local_count/global_value['global_count']\n\ + \ scaled_weights =scale_model_weights(local_weight, scaling_factor)\n\ + \ scaled_local_weight_list.append(scaled_weights)\n\n \ + \ break\n time.sleep(1)\n\n with cal_weight_lock:\n\ + \n while True:\n if(len(scaled_local_weight_list)\ + \ == NUM_OF_CLIENTS and global_value['weight_statue'] != True):\n\n \ + \ global_value['average_weights'] = sum_scaled_weights(scaled_local_weight_list)\n\ + \ global_value['weight_statue'] = True\n \ + \ global_value['average_weights'] = json.dumps([np.array(w).tolist()\ + \ for w in global_value['average_weights']])\n\n break\n\ + \n elif(global_value['weight_statue'] == True):\n\n \ + \ break\n\n time.sleep(1)\n\n clients_local_count.clear()\n\ + \ scaled_local_weight_list.clear()\n\n return jsonify({'result':\ + \ (global_value['average_weights'])})\n\n @app.route('/shutdown', methods=['GET'])\n\ + \ def shutdown_server():\n global_value['shutdown'] +=1 \n \ + \ with shutdown_lock:\n while True:\n if(global_value['shutdown']\ + \ == NUM_OF_CLIENTS):\n os._exit(0)\n \ + \ return 'Server shutting down...'\n time.sleep(1)\n\n \ + \ app.run(host=\"0.0.0.0\", port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Server',\ + \ description='')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs =\ + \ server(**_parsed_args)\n" + image: tensorflow/tensorflow + ports: + - {containerPort: 8080, name: my-port} + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + metadata: + labels: + app: http-service + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": [], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 + -m pip install --quiet --no-warn-script-location ''flask'' ''pandas'' || + PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''flask'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def server():\n import json\n import pandas as pd\n import numpy + as np\n import pickle\n import threading\n import time\n import + tensorflow as tf\n from flask import Flask, jsonify,request\n import + os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list + = []\n global_value = { #Share variable\n ''last_run_statue'' + : False, #last run finish or not\n ''data_statue'' : + None, #global_count finish or not\n ''global_count'' + : None,\n ''scale_statue'' : None,\n ''weight_statue'' + : None,\n ''average_weights'' : None,\n ''shutdown'' + : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n init_lock = threading.Lock()\n clients_local_count_lock + = threading.Lock()\n scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock + = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n def + before_request():\n print(''get request'')\n\n @app.route(''/data'', + methods=[''POST''])\n def flask_server():\n with init_lock: #check + last run is finish and init varible\n\n while True:\n\n if(len(clients_local_count)==0 + and global_value[''last_run_statue''] == False):#init the variable by first + client enter\n global_value[''last_run_statue''] = True\n global_value[''data_statue''] + = False\n global_value[''scale_statue''] = False\n global_value[''weight_statue''] + = False\n break\n\n elif(global_value[''last_run_statue''] + == True):\n break\n time.sleep(3)\n\n local_count + = int(request.form.get(''local_count'')) #get data\n bs + = int(request.form.get(''bs''))\n local_weight = json.loads(request.form.get(''local_weight''))\n local_weight + = [np.array(lst) for lst in local_weight]\n\n def scale_model_weights(weight, + scalar):\n weight_final = []\n steps = len(weight)\n for + i in range(steps):\n weight_final.append(scalar * weight[i])\n return + weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n avg_grad + = list()\n #get the average grad accross all client gradients\n for + grad_list_tuple in zip(*scaled_weight_list):\n layer_mean + = tf.math.reduce_sum(grad_list_tuple, axis=0)\n avg_grad.append(layer_mean)\n\n return + avg_grad\n\n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\n with + scaled_local_weight_list_lock:\n while True:\n\n if + (len(clients_local_count) == NUM_OF_CLIENTS and global_value[''data_statue''] + != True):\n global_value[''last_run_statue''] = False\n sum_of_local_count=sum(clients_local_count)\n\n global_value[''global_count''] + = sum_of_local_count \n\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights + = scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n global_value[''scale_statue''] + = True \n global_value[''data_statue''] = True\n break\n elif + (global_value[''data_statue''] == True and global_value[''scale_statue''] + == True):\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights + =scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n break\n time.sleep(1)\n\n with + cal_weight_lock:\n\n while True:\n if(len(scaled_local_weight_list) + == NUM_OF_CLIENTS and global_value[''weight_statue''] != True):\n\n global_value[''average_weights''] + = sum_scaled_weights(scaled_local_weight_list)\n global_value[''weight_statue''] + = True\n global_value[''average_weights''] = json.dumps([np.array(w).tolist() + for w in global_value[''average_weights'']])\n\n break\n\n elif(global_value[''weight_statue''] + == True):\n\n break\n\n time.sleep(1)\n\n clients_local_count.clear()\n scaled_local_weight_list.clear()\n\n return + jsonify({''result'': (global_value[''average_weights''])})\n\n @app.route(''/shutdown'', + methods=[''GET''])\n def shutdown_server():\n global_value[''shutdown''] + +=1 \n with shutdown_lock:\n while True:\n if(global_value[''shutdown''] + == NUM_OF_CLIENTS):\n os._exit(0)\n return + ''Server shutting down...''\n time.sleep(1)\n\n app.run(host=\"0.0.0.0\", + port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Server'', + description='''')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs + = server(**_parsed_args)\n"], "image": "tensorflow/tensorflow"}}, "name": + "Server"}', pipelines.kubeflow.org/component_ref: '{}'} + - name: show-results + container: + args: [--test-acc, '{{inputs.parameters.client-last_accuracy}}', '----output-paths', + /tmp/outputs/test_accuracy/data] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def show_results(test_acc): + return([test_acc]) + + def _serialize_float(float_value: float) -> str: + if isinstance(float_value, str): + return float_value + if not isinstance(float_value, (float, int)): + raise TypeError('Value "{}" has type "{}" instead of float.'.format( + str(float_value), str(type(float_value)))) + return str(float_value) + + import argparse + _parser = argparse.ArgumentParser(prog='Show results', description='') + _parser.add_argument("--test-acc", dest="test_acc", type=float, required=True, default=argparse.SUPPRESS) + _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1) + _parsed_args = vars(_parser.parse_args()) + _output_files = _parsed_args.pop("_output_paths", []) + + _outputs = show_results(**_parsed_args) + + _output_serializers = [ + _serialize_float, + + ] + + import os + for idx, output_file in enumerate(_output_files): + try: + os.makedirs(os.path.dirname(output_file)) + except OSError: + pass + with open(output_file, 'w') as f: + f.write(_output_serializers[idx](_outputs[idx])) + image: python:3.7 + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + inputs: + parameters: + - {name: client-last_accuracy} + outputs: + artifacts: + - {name: show-results-test_accuracy, path: /tmp/outputs/test_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--test-acc", {"inputValue": "test_acc"}, "----output-paths", + {"outputPath": "test_accuracy"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def show_results(test_acc):\n return([test_acc])\n\ndef _serialize_float(float_value: + float) -> str:\n if isinstance(float_value, str):\n return float_value\n if + not isinstance(float_value, (float, int)):\n raise TypeError(''Value + \"{}\" has type \"{}\" instead of float.''.format(\n str(float_value), + str(type(float_value))))\n return str(float_value)\n\nimport argparse\n_parser + = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--test-acc\", + dest=\"test_acc\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = show_results(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "python:3.7"}}, "inputs": [{"name": "test_acc", "type": "Float"}], + "name": "Show results", "outputs": [{"name": "test_accuracy", "type": "Float"}]}', + pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"test_acc": + "{{inputs.parameters.client-last_accuracy}}"}'} + arguments: + parameters: + - {name: namespace, value: kubeflow-user-thu01} + serviceAccountName: pipeline-runner diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml new file mode 100644 index 000000000..8381600f3 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/fl_pipeline.yaml @@ -0,0 +1,639 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: fl-test- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-12-13T03:33:40.267552', + pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "kubeflow-user-thu01", + "name": "namespace", "optional": true}], "name": "FL test"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: fl-test + templates: + - name: client + container: + args: [--batch, '1', '----output-paths', /tmp/outputs/last_accuracy/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def client(batch):\n import json\n import requests\n import time\n\ + \ import pandas as pd\n import numpy as np\n import tensorflow as\ + \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ + \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ + \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ + \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ + \ tensorflow.keras.optimizers import SGD\n from tensorflow.keras import\ + \ backend as K\n\n normal_url='https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'\ + \ \n abnormal_url='https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy'\n\ + \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ + \ num_features = len(normal_data.columns)\n print(num_features)\n \ + \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ + \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ + \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ + \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ + \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ + \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ + \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ + \n class SimpleMLP:\n @staticmethod\n def build(shape, classes):\n\ + \ model = Sequential()\n model.add(Conv1D(filters=4,\ + \ kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n\ + \ model.add(Flatten())\n model.add(Dense(8, activation=\"\ + relu\"))\n model.add(Dense(2, activation = 'softmax'))\n\n \ + \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ + \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ + \ #The client should have its own data, not like this. It's a lazy method.\n\ + \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ + \n #seperate shard into data and labels lists\n data, label\ + \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ + \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ + \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ + \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ + \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ + \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ + \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ + \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ + \ \n optimizer=optimizer, \n metrics=metrics)\n\ + \n if(comm_round == 0):\n history = client_model.fit(dataset,\ + \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ + \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ + \ local_weight = client_model.get_weights()\n local_weight =\ + \ [np.array(w).tolist() for w in local_weight]\n\n client_data = {\"\ + local_count\": local_count,'bs': bs, 'local_weight': json.dumps(local_weight)}\n\ + \n while True:\n try:\n weight = (requests.post(server_url,data=client_data))\n\ + \n if weight.status_code == 200:\n print(f\"\ + exist\")\n\n break\n else:\n \ + \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ + \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ + \ data = weight.json()\n avg_weight = data.get('result')\n \ + \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ + \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ + \ \n try:\n response = requests.get(shutdown_url)\n except\ + \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ + \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ + \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ + \ str:\n if isinstance(float_value, str):\n return float_value\n\ + \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ + \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ + \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ + _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ + --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ + _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ + \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ + _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ + \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + outputs: + parameters: + - name: client-last_accuracy + valueFrom: {path: /tmp/outputs/last_accuracy/data} + artifacts: + - {name: client-last_accuracy, path: /tmp/outputs/last_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": + "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' + || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def client(batch):\n import json\n import requests\n import time\n import + pandas as pd\n import numpy as np\n import tensorflow as tf\n from + tensorflow.keras.models import Sequential\n from tensorflow.keras.layers + import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from + tensorflow.keras.layers import Activation\n from tensorflow.keras.layers + import Flatten\n from tensorflow.keras.layers import Dense\n from + tensorflow.keras.optimizers import SGD\n from tensorflow.keras import + backend as K\n\n normal_url=''https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'' + \n abnormal_url=''https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy''\n normal_data + = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features + = len(normal_data.columns)\n print(num_features)\n normal_label = + np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, + 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label + = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data + = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), + num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data + = list(zip(data, data_label))\n data_length=len(full_data)\n\n class + SimpleMLP:\n @staticmethod\n def build(shape, classes):\n model + = Sequential()\n model.add(Conv1D(filters=4, kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n model.add(Flatten())\n model.add(Dense(8, + activation=\"relu\"))\n model.add(Dense(2, activation = ''softmax''))\n\n return + model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] + #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] + #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data + len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate + shard into data and labels lists\n data, label = zip(*data_shard)\n dataset + = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return + dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs + = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics + = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for + comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model + = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, + \n metrics=metrics)\n\n if(comm_round == 0):\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n\n local_weight + = client_model.get_weights()\n local_weight = [np.array(w).tolist() + for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': + bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight + = (requests.post(server_url,data=client_data))\n\n if weight.status_code + == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server + error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not + exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight + = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight + = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response + = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already + shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef + _serialize_float(float_value: float) -> str:\n if isinstance(float_value, + str):\n return float_value\n if not isinstance(float_value, (float, + int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + float.''.format(\n str(float_value), str(type(float_value))))\n return + str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', + description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, + required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": + "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": + "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": + "1"}'} + - name: client-2 + container: + args: [--batch, '2', '----output-paths', /tmp/outputs/last_accuracy/data] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'requests' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'requests' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def client(batch):\n import json\n import requests\n import time\n\ + \ import pandas as pd\n import numpy as np\n import tensorflow as\ + \ tf\n from tensorflow.keras.models import Sequential\n from tensorflow.keras.layers\ + \ import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n \ + \ from tensorflow.keras.layers import Activation\n from tensorflow.keras.layers\ + \ import Flatten\n from tensorflow.keras.layers import Dense\n from\ + \ tensorflow.keras.optimizers import SGD\n from tensorflow.keras import\ + \ backend as K\n\n normal_url='https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'\ + \ \n abnormal_url='https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy'\n\ + \ normal_data = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n\ + \ num_features = len(normal_data.columns)\n print(num_features)\n \ + \ normal_label = np.array([[1, 0]] * len(normal_data))\n abnormal_label\ + \ = np.array([[0, 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data,\ + \ abnormal_data))\n data_label = np.vstack((normal_label, abnormal_label))\n\ + \n shuffler = np.random.permutation(len(data))\n data = data[shuffler]\n\ + \ data_label = data_label[shuffler]\n\n data = data.reshape(len(data),\ + \ num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\ + \n full_data = list(zip(data, data_label))\n data_length=len(full_data)\n\ + \n class SimpleMLP:\n @staticmethod\n def build(shape, classes):\n\ + \ model = Sequential()\n model.add(Conv1D(filters=4,\ + \ kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n\ + \ model.add(Flatten())\n model.add(Dense(8, activation=\"\ + relu\"))\n model.add(Dense(2, activation = 'softmax'))\n\n \ + \ return model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)]\ + \ #batch data\n else:\n full_data=full_data[int(data_length/2):data_length]\ + \ #The client should have its own data, not like this. It's a lazy method.\n\ + \n print('data len= ',len(full_data))\n def batch_data(data_shard, bs=32):\n\ + \n #seperate shard into data and labels lists\n data, label\ + \ = zip(*data_shard)\n dataset = tf.data.Dataset.from_tensor_slices((list(data),\ + \ list(label)))\n return dataset.shuffle(len(label)).batch(bs)\n\n\ + \ dataset=batch_data(full_data)\n #print(dataset)\n\n bs = next(iter(dataset))[0].shape[0]\n\ + \ local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n\ + \ loss='categorical_crossentropy'\n metrics = ['accuracy']\n optimizer\ + \ = 'adam'\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\ + \n for comm_round in range(1):\n print('The ',comm_round+1, 'round')\n\ + \ client_model = smlp_model.build(17, 1)\n client_model.compile(loss=loss,\ + \ \n optimizer=optimizer, \n metrics=metrics)\n\ + \n if(comm_round == 0):\n history = client_model.fit(dataset,\ + \ epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n\ + \ history = client_model.fit(dataset, epochs=5, verbose=1)\n\n\ + \ local_weight = client_model.get_weights()\n local_weight =\ + \ [np.array(w).tolist() for w in local_weight]\n\n client_data = {\"\ + local_count\": local_count,'bs': bs, 'local_weight': json.dumps(local_weight)}\n\ + \n while True:\n try:\n weight = (requests.post(server_url,data=client_data))\n\ + \n if weight.status_code == 200:\n print(f\"\ + exist\")\n\n break\n else:\n \ + \ print(f\"server error\")\n\n except requests.exceptions.RequestException:\n\ + \n print(f\"not exist\")\n\n time.sleep(5)\n\n \ + \ data = weight.json()\n avg_weight = data.get('result')\n \ + \ avg_weight = json.loads(avg_weight)\n avg_weight = [np.array(lst)\ + \ for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\"\ + \ \n try:\n response = requests.get(shutdown_url)\n except\ + \ requests.exceptions.ConnectionError:\n print('already shutdown')\n\ + \ last_accuracy = history.history['accuracy'][-1]\n print(last_accuracy)\n\ + \ return([last_accuracy])\n\ndef _serialize_float(float_value: float) ->\ + \ str:\n if isinstance(float_value, str):\n return float_value\n\ + \ if not isinstance(float_value, (float, int)):\n raise TypeError('Value\ + \ \"{}\" has type \"{}\" instead of float.'.format(\n str(float_value),\ + \ str(type(float_value))))\n return str(float_value)\n\nimport argparse\n\ + _parser = argparse.ArgumentParser(prog='Client', description='')\n_parser.add_argument(\"\ + --batch\", dest=\"batch\", type=int, required=True, default=argparse.SUPPRESS)\n\ + _parser.add_argument(\"----output-paths\", dest=\"_output_paths\", type=str,\ + \ nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"\ + _output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers\ + \ = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + outputs: + artifacts: + - {name: client-2-last_accuracy, path: /tmp/outputs/last_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--batch", {"inputValue": "batch"}, "----output-paths", {"outputPath": + "last_accuracy"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 + python3 -m pip install --quiet --no-warn-script-location ''requests'' ''pandas'' + || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''requests'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def client(batch):\n import json\n import requests\n import time\n import + pandas as pd\n import numpy as np\n import tensorflow as tf\n from + tensorflow.keras.models import Sequential\n from tensorflow.keras.layers + import Conv1D\n from tensorflow.keras.layers import MaxPooling1D\n from + tensorflow.keras.layers import Activation\n from tensorflow.keras.layers + import Flatten\n from tensorflow.keras.layers import Dense\n from + tensorflow.keras.optimizers import SGD\n from tensorflow.keras import + backend as K\n\n normal_url=''https://drive.google.com/u/0/uc?id=1TQHKkP6yzuhcxw_JCtby9jQwY2AMLiNi&export=download'' + \n abnormal_url=''https://drive.google.com/uc?export=download&id=1i22tQI2vib0fsd1wwVP1tEydmGEksmpy''\n normal_data + = pd.read_csv(normal_url)\n abnormal_data = pd.read_csv(abnormal_url)\n num_features + = len(normal_data.columns)\n print(num_features)\n normal_label = + np.array([[1, 0]] * len(normal_data))\n abnormal_label = np.array([[0, + 1]] * len(abnormal_data))\n\n data = np.vstack((normal_data, abnormal_data))\n data_label + = np.vstack((normal_label, abnormal_label))\n\n shuffler = np.random.permutation(len(data))\n data + = data[shuffler]\n data_label = data_label[shuffler]\n\n data = data.reshape(len(data), + num_features, 1)\n data_label = data_label.reshape(len(data_label), 2)\n\n full_data + = list(zip(data, data_label))\n data_length=len(full_data)\n\n class + SimpleMLP:\n @staticmethod\n def build(shape, classes):\n model + = Sequential()\n model.add(Conv1D(filters=4, kernel_size=3, input_shape=(17,1)))\n model.add(MaxPooling1D(3))\n model.add(Flatten())\n model.add(Dense(8, + activation=\"relu\"))\n model.add(Dense(2, activation = ''softmax''))\n\n return + model\n\n if(batch==1):\n full_data=full_data[0:int(data_length/2)] + #batch data\n else:\n full_data=full_data[int(data_length/2):data_length] + #The client should have its own data, not like this. It''s a lazy method.\n\n print(''data + len= '',len(full_data))\n def batch_data(data_shard, bs=32):\n\n #seperate + shard into data and labels lists\n data, label = zip(*data_shard)\n dataset + = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n return + dataset.shuffle(len(label)).batch(bs)\n\n dataset=batch_data(full_data)\n #print(dataset)\n\n bs + = next(iter(dataset))[0].shape[0]\n local_count = tf.data.experimental.cardinality(dataset).numpy()*bs\n\n loss=''categorical_crossentropy''\n metrics + = [''accuracy'']\n optimizer = ''adam''\n\n smlp_model = SimpleMLP()\n\n server_url=\"http://http-service:5000/data\"\n for + comm_round in range(1):\n print(''The '',comm_round+1, ''round'')\n client_model + = smlp_model.build(17, 1)\n client_model.compile(loss=loss, \n optimizer=optimizer, + \n metrics=metrics)\n\n if(comm_round == 0):\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n else:\n client_model.set_weights(avg_weight)\n history + = client_model.fit(dataset, epochs=5, verbose=1)\n\n local_weight + = client_model.get_weights()\n local_weight = [np.array(w).tolist() + for w in local_weight]\n\n client_data = {\"local_count\": local_count,''bs'': + bs, ''local_weight'': json.dumps(local_weight)}\n\n while True:\n try:\n weight + = (requests.post(server_url,data=client_data))\n\n if weight.status_code + == 200:\n print(f\"exist\")\n\n break\n else:\n print(f\"server + error\")\n\n except requests.exceptions.RequestException:\n\n print(f\"not + exist\")\n\n time.sleep(5)\n\n data = weight.json()\n avg_weight + = data.get(''result'')\n avg_weight = json.loads(avg_weight)\n avg_weight + = [np.array(lst) for lst in avg_weight]\n\n shutdown_url=\"http://http-service:5000/shutdown\" \n try:\n response + = requests.get(shutdown_url)\n except requests.exceptions.ConnectionError:\n print(''already + shutdown'')\n last_accuracy = history.history[''accuracy''][-1]\n print(last_accuracy)\n return([last_accuracy])\n\ndef + _serialize_float(float_value: float) -> str:\n if isinstance(float_value, + str):\n return float_value\n if not isinstance(float_value, (float, + int)):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + float.''.format(\n str(float_value), str(type(float_value))))\n return + str(float_value)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Client'', + description='''')\n_parser.add_argument(\"--batch\", dest=\"batch\", type=int, + required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = client(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow"}}, "inputs": [{"name": "batch", "type": + "Integer"}], "name": "Client", "outputs": [{"name": "last_accuracy", "type": + "Float"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"batch": + "2"}'} + - name: delete-service + resource: + action: delete + flags: [--wait=false] + manifest: | + apiVersion: v1 + kind: Service + metadata: + name: http-service + spec: + ports: + - port: 80 + protocol: TCP + targetPort: 8080 + selector: + app: http-service + type: NodePort + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: fl-test + dag: + tasks: + - {name: client, template: client} + - {name: client-2, template: client-2} + - name: delete-service + template: delete-service + dependencies: [server] + - {name: http-service, template: http-service} + - name: server + template: server + dependencies: [http-service] + - name: show-results + template: show-results + dependencies: [client] + arguments: + parameters: + - {name: client-last_accuracy, value: '{{tasks.client.outputs.parameters.client-last_accuracy}}'} + - name: http-service + resource: + action: create + manifest: | + apiVersion: v1 + kind: Service + metadata: + name: http-service + spec: + ports: + - port: 5000 + protocol: TCP + targetPort: 8080 + selector: + app: http-service + outputs: + parameters: + - name: http-service-manifest + valueFrom: {jsonPath: '{}'} + - name: http-service-name + valueFrom: {jsonPath: '{.metadata.name}'} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: server + container: + args: [] + command: + - sh + - -c + - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + 'flask' 'pandas' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install + --quiet --no-warn-script-location 'flask' 'pandas' --user) && "$0" "$@" + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def server():\n import json\n import pandas as pd\n import numpy\ + \ as np\n import pickle\n import threading\n import time\n import\ + \ tensorflow as tf\n from flask import Flask, jsonify,request\n import\ + \ os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list\ + \ = []\n global_value = { #Share variable\n 'last_run_statue'\ + \ : False, #last run finish or not\n 'data_statue' : None,\ + \ #global_count finish or not\n 'global_count' : None,\n\ + \ 'scale_statue' : None,\n 'weight_statue'\ + \ : None,\n 'average_weights' : None,\n \ + \ 'shutdown' : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n \ + \ init_lock = threading.Lock()\n clients_local_count_lock = threading.Lock()\n\ + \ scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock\ + \ = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n\ + \ def before_request():\n print('get request')\n\n @app.route('/data',\ + \ methods=['POST'])\n def flask_server():\n with init_lock: #check\ + \ last run is finish and init varible\n\n while True:\n\n \ + \ if(len(clients_local_count)==0 and global_value['last_run_statue']\ + \ == False):#init the variable by first client enter\n \ + \ global_value['last_run_statue'] = True\n global_value['data_statue']\ + \ = False\n global_value['scale_statue'] = False\n \ + \ global_value['weight_statue'] = False\n \ + \ break\n\n elif(global_value['last_run_statue'] == True):\n\ + \ break\n time.sleep(3)\n\n local_count\ + \ = int(request.form.get('local_count')) #get data\n bs =\ + \ int(request.form.get('bs'))\n local_weight = json.loads(request.form.get('local_weight'))\n\ + \ local_weight = [np.array(lst) for lst in local_weight]\n\n \ + \ def scale_model_weights(weight, scalar):\n weight_final = []\n\ + \ steps = len(weight)\n for i in range(steps):\n \ + \ weight_final.append(scalar * weight[i])\n return\ + \ weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n \ + \ avg_grad = list()\n #get the average grad accross all\ + \ client gradients\n for grad_list_tuple in zip(*scaled_weight_list):\n\ + \ layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)\n\ + \ avg_grad.append(layer_mean)\n\n return avg_grad\n\ + \n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\ + \n with scaled_local_weight_list_lock:\n while True:\n\n\ + \ if (len(clients_local_count) == NUM_OF_CLIENTS and global_value['data_statue']\ + \ != True):\n global_value['last_run_statue'] = False\n\ + \ sum_of_local_count=sum(clients_local_count)\n\n \ + \ global_value['global_count'] = sum_of_local_count \n\n\ + \ scaling_factor=local_count/global_value['global_count']\n\ + \ scaled_weights = scale_model_weights(local_weight, scaling_factor)\n\ + \ scaled_local_weight_list.append(scaled_weights)\n\n \ + \ global_value['scale_statue'] = True \n \ + \ global_value['data_statue'] = True\n break\n \ + \ elif (global_value['data_statue'] == True and global_value['scale_statue']\ + \ == True):\n scaling_factor=local_count/global_value['global_count']\n\ + \ scaled_weights =scale_model_weights(local_weight, scaling_factor)\n\ + \ scaled_local_weight_list.append(scaled_weights)\n\n \ + \ break\n time.sleep(1)\n\n with cal_weight_lock:\n\ + \n while True:\n if(len(scaled_local_weight_list)\ + \ == NUM_OF_CLIENTS and global_value['weight_statue'] != True):\n\n \ + \ global_value['average_weights'] = sum_scaled_weights(scaled_local_weight_list)\n\ + \ global_value['weight_statue'] = True\n \ + \ global_value['average_weights'] = json.dumps([np.array(w).tolist()\ + \ for w in global_value['average_weights']])\n\n break\n\ + \n elif(global_value['weight_statue'] == True):\n\n \ + \ break\n\n time.sleep(1)\n\n clients_local_count.clear()\n\ + \ scaled_local_weight_list.clear()\n\n return jsonify({'result':\ + \ (global_value['average_weights'])})\n\n @app.route('/shutdown', methods=['GET'])\n\ + \ def shutdown_server():\n global_value['shutdown'] +=1 \n \ + \ with shutdown_lock:\n while True:\n if(global_value['shutdown']\ + \ == NUM_OF_CLIENTS):\n os._exit(0)\n \ + \ return 'Server shutting down...'\n time.sleep(1)\n\n \ + \ app.run(host=\"0.0.0.0\", port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Server',\ + \ description='')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs =\ + \ server(**_parsed_args)\n" + image: tensorflow/tensorflow + ports: + - {containerPort: 8080, name: my-port} + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + metadata: + labels: + app: http-service + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": [], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 + -m pip install --quiet --no-warn-script-location ''flask'' ''pandas'' || + PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location + ''flask'' ''pandas'' --user) && \"$0\" \"$@\"", "sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def server():\n import json\n import pandas as pd\n import numpy + as np\n import pickle\n import threading\n import time\n import + tensorflow as tf\n from flask import Flask, jsonify,request\n import + os\n\n app = Flask(__name__)\n clients_local_count = []\n scaled_local_weight_list + = []\n global_value = { #Share variable\n ''last_run_statue'' + : False, #last run finish or not\n ''data_statue'' : + None, #global_count finish or not\n ''global_count'' + : None,\n ''scale_statue'' : None,\n ''weight_statue'' + : None,\n ''average_weights'' : None,\n ''shutdown'' + : 0}\n\n NUM_OF_CLIENTS = 2 #number of clients\n\n init_lock = threading.Lock()\n clients_local_count_lock + = threading.Lock()\n scaled_local_weight_list_lock = threading.Lock()\n cal_weight_lock + = threading.Lock()\n shutdown_lock = threading.Lock()\n\n @app.before_request\n def + before_request():\n print(''get request'')\n\n @app.route(''/data'', + methods=[''POST''])\n def flask_server():\n with init_lock: #check + last run is finish and init varible\n\n while True:\n\n if(len(clients_local_count)==0 + and global_value[''last_run_statue''] == False):#init the variable by first + client enter\n global_value[''last_run_statue''] = True\n global_value[''data_statue''] + = False\n global_value[''scale_statue''] = False\n global_value[''weight_statue''] + = False\n break\n\n elif(global_value[''last_run_statue''] + == True):\n break\n time.sleep(3)\n\n local_count + = int(request.form.get(''local_count'')) #get data\n bs + = int(request.form.get(''bs''))\n local_weight = json.loads(request.form.get(''local_weight''))\n local_weight + = [np.array(lst) for lst in local_weight]\n\n def scale_model_weights(weight, + scalar):\n weight_final = []\n steps = len(weight)\n for + i in range(steps):\n weight_final.append(scalar * weight[i])\n return + weight_final\n def sum_scaled_weights(scaled_weight_list):\n\n avg_grad + = list()\n #get the average grad accross all client gradients\n for + grad_list_tuple in zip(*scaled_weight_list):\n layer_mean + = tf.math.reduce_sum(grad_list_tuple, axis=0)\n avg_grad.append(layer_mean)\n\n return + avg_grad\n\n with clients_local_count_lock:\n clients_local_count.append(int(local_count))\n\n with + scaled_local_weight_list_lock:\n while True:\n\n if + (len(clients_local_count) == NUM_OF_CLIENTS and global_value[''data_statue''] + != True):\n global_value[''last_run_statue''] = False\n sum_of_local_count=sum(clients_local_count)\n\n global_value[''global_count''] + = sum_of_local_count \n\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights + = scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n global_value[''scale_statue''] + = True \n global_value[''data_statue''] = True\n break\n elif + (global_value[''data_statue''] == True and global_value[''scale_statue''] + == True):\n scaling_factor=local_count/global_value[''global_count'']\n scaled_weights + =scale_model_weights(local_weight, scaling_factor)\n scaled_local_weight_list.append(scaled_weights)\n\n break\n time.sleep(1)\n\n with + cal_weight_lock:\n\n while True:\n if(len(scaled_local_weight_list) + == NUM_OF_CLIENTS and global_value[''weight_statue''] != True):\n\n global_value[''average_weights''] + = sum_scaled_weights(scaled_local_weight_list)\n global_value[''weight_statue''] + = True\n global_value[''average_weights''] = json.dumps([np.array(w).tolist() + for w in global_value[''average_weights'']])\n\n break\n\n elif(global_value[''weight_statue''] + == True):\n\n break\n\n time.sleep(1)\n\n clients_local_count.clear()\n scaled_local_weight_list.clear()\n\n return + jsonify({''result'': (global_value[''average_weights''])})\n\n @app.route(''/shutdown'', + methods=[''GET''])\n def shutdown_server():\n global_value[''shutdown''] + +=1 \n with shutdown_lock:\n while True:\n if(global_value[''shutdown''] + == NUM_OF_CLIENTS):\n os._exit(0)\n return + ''Server shutting down...''\n time.sleep(1)\n\n app.run(host=\"0.0.0.0\", + port=8080)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Server'', + description='''')\n_parsed_args = vars(_parser.parse_args())\n\n_outputs + = server(**_parsed_args)\n"], "image": "tensorflow/tensorflow"}}, "name": + "Server"}', pipelines.kubeflow.org/component_ref: '{}'} + - name: show-results + container: + args: [--test-acc, '{{inputs.parameters.client-last_accuracy}}', '----output-paths', + /tmp/outputs/test_accuracy/data] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def show_results(test_acc): + return([test_acc]) + + def _serialize_float(float_value: float) -> str: + if isinstance(float_value, str): + return float_value + if not isinstance(float_value, (float, int)): + raise TypeError('Value "{}" has type "{}" instead of float.'.format( + str(float_value), str(type(float_value)))) + return str(float_value) + + import argparse + _parser = argparse.ArgumentParser(prog='Show results', description='') + _parser.add_argument("--test-acc", dest="test_acc", type=float, required=True, default=argparse.SUPPRESS) + _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1) + _parsed_args = vars(_parser.parse_args()) + _output_files = _parsed_args.pop("_output_paths", []) + + _outputs = show_results(**_parsed_args) + + _output_serializers = [ + _serialize_float, + + ] + + import os + for idx, output_file in enumerate(_output_files): + try: + os.makedirs(os.path.dirname(output_file)) + except OSError: + pass + with open(output_file, 'w') as f: + f.write(_output_serializers[idx](_outputs[idx])) + image: python:3.7 + resources: + limits: {cpu: '0.2'} + requests: {cpu: '0.2'} + inputs: + parameters: + - {name: client-last_accuracy} + outputs: + artifacts: + - {name: show-results-test_accuracy, path: /tmp/outputs/test_accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--test-acc", {"inputValue": "test_acc"}, "----output-paths", + {"outputPath": "test_accuracy"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def show_results(test_acc):\n return([test_acc])\n\ndef _serialize_float(float_value: + float) -> str:\n if isinstance(float_value, str):\n return float_value\n if + not isinstance(float_value, (float, int)):\n raise TypeError(''Value + \"{}\" has type \"{}\" instead of float.''.format(\n str(float_value), + str(type(float_value))))\n return str(float_value)\n\nimport argparse\n_parser + = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--test-acc\", + dest=\"test_acc\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = show_results(**_parsed_args)\n\n_output_serializers + = [\n _serialize_float,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "python:3.7"}}, "inputs": [{"name": "test_acc", "type": "Float"}], + "name": "Show results", "outputs": [{"name": "test_accuracy", "type": "Float"}]}', + pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"test_acc": + "{{inputs.parameters.client-last_accuracy}}"}'} + arguments: + parameters: + - {name: namespace, value: kubeflow-user-thu01} + serviceAccountName: pipeline-runner diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml new file mode 100644 index 000000000..b8f353c80 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/mnist_pipeline.yaml @@ -0,0 +1,211 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: mnist-pipeline- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-12-26T13:06:02.352865', + pipelines.kubeflow.org/pipeline_spec: '{"description": "A pipeline to train a + model on mnist dataset and start a tensorboard.", "name": "mnist pipeline"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: mnist-pipeline + templates: + - name: create-tensorboard-visualization + container: + args: [] + command: + - sh + - -ex + - -c + - | + log_dir="$0" + output_metadata_path="$1" + pod_template_spec="$2" + image="$3" + + mkdir -p "$(dirname "$output_metadata_path")" + + echo ' + { + "outputs" : [{ + "type": "tensorboard", + "source": "'"$log_dir"'", + "image": "'"$image"'", + "pod_template_spec": '"$pod_template_spec"' + }] + } + ' >"$output_metadata_path" + - volume://mypvc/logs + - /tmp/outputs/mlpipeline-ui-metadata/data + - '{"spec": {"containers": [{"volumeMounts": [{"mountPath": "/data", "name": + "mypvc"}], "resources": {"requests": {"cpu": "250m"}, "limits": {"cpu": "500m"}}}], + "serviceAccountName": "default-editor", "volumes": [{"name": "mypvc", "persistentVolumeClaim": + {"claimName": "my-awesome-kf-workshop-1703595962"}}]}}' + - footprintai/tensorboard:2.7.0 + image: alpine + resources: + limits: {cpu: '1'} + requests: {cpu: '1'} + outputs: + artifacts: + - {name: mlpipeline-ui-metadata, path: /tmp/outputs/mlpipeline-ui-metadata/data} + metadata: + annotations: {author: Alexey Volkov , canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/tensorboard/prepare_tensorboard/component.yaml', + pipelines.kubeflow.org/component_spec: '{"description": "Pre-creates Tensorboard + visualization for a given Log dir URI.\nThis way the Tensorboard can be + viewed before the training completes.\nThe output Log dir URI should be + passed to a trainer component that will write Tensorboard logs to that directory.\n", + "implementation": {"container": {"command": ["sh", "-ex", "-c", "log_dir=\"$0\"\noutput_metadata_path=\"$1\"\npod_template_spec=\"$2\"\nimage=\"$3\"\n\nmkdir + -p \"$(dirname \"$output_metadata_path\")\"\n\necho ''\n {\n \"outputs\" + : [{\n \"type\": \"tensorboard\",\n \"source\": \"''\"$log_dir\"''\",\n \"image\": + \"''\"$image\"''\",\n \"pod_template_spec\": ''\"$pod_template_spec\"''\n }]\n }\n'' + >\"$output_metadata_path\"\n", {"inputValue": "Log dir URI"}, {"outputPath": + "mlpipeline-ui-metadata"}, {"inputValue": "Pod Template Spec"}, {"inputValue": + "Image"}], "image": "alpine"}}, "inputs": [{"name": "Log dir URI", "type": + "String"}, {"default": "", "name": "Image", "type": "String"}, {"default": + "null", "name": "Pod Template Spec", "type": "String"}], "metadata": {"annotations": + {"author": "Alexey Volkov ", "canonical_location": + "https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/tensorboard/prepare_tensorboard/component.yaml"}}, + "name": "Create Tensorboard visualization", "outputs": [{"name": "mlpipeline-ui-metadata", + "type": "kfp.v1.ui-metadata"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "cc3c37c54619129e4f57e4564bc5df0ba9719a305e6145238f2ae7e54d87f2ef", "url": + "https://raw.githubusercontent.com/kubeflow/pipelines/1b107eb4bb2510ecb99fd5f4fb438cbf7c96a87a/components/contrib/tensorflow/tensorboard/prepare_tensorboard/component.yaml"}', + pipelines.kubeflow.org/arguments.parameters: '{"Image": "footprintai/tensorboard:2.7.0", + "Log dir URI": "volume://mypvc/logs", "Pod Template Spec": "{\"spec\": {\"containers\": + [{\"volumeMounts\": [{\"mountPath\": \"/data\", \"name\": \"mypvc\"}], \"resources\": + {\"requests\": {\"cpu\": \"250m\"}, \"limits\": {\"cpu\": \"500m\"}}}], + \"serviceAccountName\": \"default-editor\", \"volumes\": [{\"name\": \"mypvc\", + \"persistentVolumeClaim\": {\"claimName\": \"my-awesome-kf-workshop-1703595962\"}}]}}"}'} + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + - name: mnist-func + container: + args: [--log-folder, /data, '----output-paths', /tmp/outputs/logdir/data] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - "def mnist_func(log_folder):\n\n print('mnist_func:', log_folder)\n \ + \ import tensorflow as tf\n import json\n mnist = tf.keras.datasets.mnist\n\ + \ (x_train,y_train), (x_test, y_test) = mnist.load_data()\n x_train,\ + \ x_test = x_train/255.0, x_test/255.0\n\n def create_model():\n \ + \ return tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape\ + \ = (28,28)),\n tf.keras.layers.Dense(512, activation = 'relu'),\n\ + \ tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10,\ + \ activation = 'softmax')\n ])\n model = create_model()\n model.compile(optimizer='adam',\n\ + \ loss='sparse_categorical_crossentropy',\n \ + \ metrics=['accuracy'])\n import datetime\n import os\n\n ###\ + \ add tensorboard logout callback\n log_dir = os.path.join(log_folder,\ + \ \"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n tensorboard_callback\ + \ = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n \ + \ ######\n\n model.fit(x=x_train, \n y=y_train, \n \ + \ epochs=5, \n validation_data=(x_test, y_test), \n \ + \ callbacks=[tensorboard_callback])\n\n print('At least tensorboard\ + \ callbacks are correct')\n print('logdir:', log_dir)\n return ([log_dir])\n\ + \ndef _serialize_str(str_value: str) -> str:\n if not isinstance(str_value,\ + \ str):\n raise TypeError('Value \"{}\" has type \"{}\" instead of\ + \ str.'.format(\n str(str_value), str(type(str_value))))\n return\ + \ str_value\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Mnist\ + \ func', description='')\n_parser.add_argument(\"--log-folder\", dest=\"log_folder\"\ + , type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"\ + ----output-paths\", dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args\ + \ = vars(_parser.parse_args())\n_output_files = _parsed_args.pop(\"_output_paths\"\ + , [])\n\n_outputs = mnist_func(**_parsed_args)\n\n_output_serializers = [\n\ + \ _serialize_str,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n\ + \ try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n\ + \ pass\n with open(output_file, 'w') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n" + image: tensorflow/tensorflow:2.0.0-py3 + resources: + limits: {cpu: '1'} + requests: {cpu: '1'} + volumeMounts: + - {mountPath: /data, name: mypvc} + inputs: + parameters: + - {name: mypvc-name} + outputs: + artifacts: + - {name: mnist-func-logdir, path: /tmp/outputs/logdir/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--log-folder", {"inputValue": "log_folder"}, "----output-paths", + {"outputPath": "logdir"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def mnist_func(log_folder):\n\n print(''mnist_func:'', log_folder)\n import + tensorflow as tf\n import json\n mnist = tf.keras.datasets.mnist\n (x_train,y_train), + (x_test, y_test) = mnist.load_data()\n x_train, x_test = x_train/255.0, + x_test/255.0\n\n def create_model():\n return tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape + = (28,28)),\n tf.keras.layers.Dense(512, activation = ''relu''),\n tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10, + activation = ''softmax'')\n ])\n model = create_model()\n model.compile(optimizer=''adam'',\n loss=''sparse_categorical_crossentropy'',\n metrics=[''accuracy''])\n import + datetime\n import os\n\n ### add tensorboard logout callback\n log_dir + = os.path.join(log_folder, \"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n tensorboard_callback + = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n ######\n\n model.fit(x=x_train, + \n y=y_train, \n epochs=5, \n validation_data=(x_test, + y_test), \n callbacks=[tensorboard_callback])\n\n print(''At + least tensorboard callbacks are correct'')\n print(''logdir:'', log_dir)\n return + ([log_dir])\n\ndef _serialize_str(str_value: str) -> str:\n if not isinstance(str_value, + str):\n raise TypeError(''Value \"{}\" has type \"{}\" instead of + str.''.format(\n str(str_value), str(type(str_value))))\n return + str_value\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Mnist + func'', description='''')\n_parser.add_argument(\"--log-folder\", dest=\"log_folder\", + type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\", + dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files + = _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = mnist_func(**_parsed_args)\n\n_output_serializers + = [\n _serialize_str,\n\n]\n\nimport os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except + OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"], + "image": "tensorflow/tensorflow:2.0.0-py3"}}, "inputs": [{"name": "log_folder", + "type": "String"}], "name": "Mnist func", "outputs": [{"name": "logdir", + "type": "String"}]}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"log_folder": + "/data"}'} + volumes: + - name: mypvc + persistentVolumeClaim: {claimName: '{{inputs.parameters.mypvc-name}}'} + - name: mnist-pipeline + dag: + tasks: + - {name: create-tensorboard-visualization, template: create-tensorboard-visualization} + - name: mnist-func + template: mnist-func + dependencies: [create-tensorboard-visualization, mypvc] + arguments: + parameters: + - {name: mypvc-name, value: '{{tasks.mypvc.outputs.parameters.mypvc-name}}'} + - {name: mypvc, template: mypvc} + - name: mypvc + resource: + action: create + manifest: | + apiVersion: v1 + kind: PersistentVolumeClaim + metadata: + name: '{{workflow.name}}-my-awesome-kf-workshop-1703595962' + spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: 1Gi + outputs: + parameters: + - name: mypvc-manifest + valueFrom: {jsonPath: '{}'} + - name: mypvc-name + valueFrom: {jsonPath: '{.metadata.name}'} + - name: mypvc-size + valueFrom: {jsonPath: '{.status.capacity.storage}'} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + arguments: + parameters: [] + serviceAccountName: pipeline-runner diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml new file mode 100644 index 000000000..b1a4a1e25 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/only_decision_tree.yaml @@ -0,0 +1,131 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: only-decision-tree- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.20, pipelines.kubeflow.org/pipeline_compilation_time: '2023-04-28T21:42:00.682134', + pipelines.kubeflow.org/pipeline_spec: '{"description": "Applies Decision Tree + for classification problem.", "name": "only_decision_tree"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.20} +spec: + entrypoint: only-decision-tree + templates: + - name: decision-tree-classifier + container: + args: [] + command: [python, decision_tree.py, --data, /tmp/inputs/Data/data, --accuracy, + /tmp/outputs/Accuracy/data] + image: lightnighttw/kubeflow:decision_tree_v2 + resources: + limits: + cpu: 2 + inputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/inputs/Data/data} + outputs: + parameters: + - name: decision-tree-classifier-Accuracy + valueFrom: {path: /tmp/outputs/Accuracy/data} + artifacts: + - {name: decision-tree-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains + a decision tree classifier", "implementation": {"container": {"command": + ["python", "decision_tree.py", "--data", {"inputPath": "Data"}, "--accuracy", + {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:decision_tree_v2"}}, + "inputs": [{"description": "Path where data is stored.", "name": "Data", + "type": "LocalPath"}], "name": "Decision Tree classifier", "outputs": [{"description": + "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "c5c232a9654213b3b222693949b71a5d561d04ed09543c20b6f8f2eab651ae0b", "url": + "decision_tree/decision_tree.yaml"}'} + - name: download-data-function + container: + args: [] + command: [python, download_data.py, --data, /tmp/outputs/Data/data] + image: lightnighttw/kubeflow:download_data + resources: + limits: + cpu: 2 + outputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/outputs/Data/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Download + toy data from sklearn datasets", "implementation": {"container": {"command": + ["python", "download_data.py", "--data", {"outputPath": "Data"}], "image": + "lightnighttw/kubeflow:download_data"}}, "name": "Download Data Function", + "outputs": [{"description": "Path where data will be stored.", "name": "Data", + "type": "LocalPath"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "467750defdccfec51c3af2a7eb853f74235f5f97329006d72bf33ff6e15ed02d", "url": + "download_data/download_data.yaml"}'} + - name: only-decision-tree + dag: + tasks: + - name: decision-tree-classifier + template: decision-tree-classifier + dependencies: [download-data-function] + arguments: + artifacts: + - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} + - {name: download-data-function, template: download-data-function} + - name: show-results + template: show-results + dependencies: [decision-tree-classifier] + arguments: + parameters: + - {name: decision-tree-classifier-Accuracy, value: '{{tasks.decision-tree-classifier.outputs.parameters.decision-tree-classifier-Accuracy}}'} + - name: show-results + container: + args: [--decision-tree, '{{inputs.parameters.decision-tree-classifier-Accuracy}}'] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def show_results(decision_tree): + # the results are shown. + + print(f"Decision tree (accuracy): {decision_tree}") + + import argparse + _parser = argparse.ArgumentParser(prog='Show results', description='') + _parser.add_argument("--decision-tree", dest="decision_tree", type=float, required=True, default=argparse.SUPPRESS) + _parsed_args = vars(_parser.parse_args()) + + _outputs = show_results(**_parsed_args) + image: python:3.7 + resources: + limits: + cpu: 2 + inputs: + parameters: + - {name: decision-tree-classifier-Accuracy} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.20 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--decision-tree", {"inputValue": "decision_tree"}], "command": + ["sh", "-ec", "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 + -u \"$program_path\" \"$@\"\n", "def show_results(decision_tree):\n # + the results are shown.\n\n print(f\"Decision tree (accuracy): {decision_tree}\")\n\nimport + argparse\n_parser = argparse.ArgumentParser(prog=''Show results'', description='''')\n_parser.add_argument(\"--decision-tree\", + dest=\"decision_tree\", type=float, required=True, default=argparse.SUPPRESS)\n_parsed_args + = vars(_parser.parse_args())\n\n_outputs = show_results(**_parsed_args)\n"], + "image": "python:3.7"}}, "inputs": [{"name": "decision_tree", "type": "Float"}], + "name": "Show results"}', pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"decision_tree": + "{{inputs.parameters.decision-tree-classifier-Accuracy}}"}'} + arguments: + parameters: [] + serviceAccountName: pipeline-runner diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml new file mode 100644 index 000000000..2e10bcefa --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/pipelines/three-pipeline.yaml @@ -0,0 +1,229 @@ +apiVersion: argoproj.io/v1alpha1 +kind: Workflow +metadata: + generateName: three-pipeline- + annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: '2023-04-28T12:05:44.365082', + pipelines.kubeflow.org/pipeline_spec: '{"description": "Applies Decision Tree, + random forest and Logistic Regression for classification problem.", "name": + "Three Pipeline"}'} + labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9} +spec: + entrypoint: three-pipeline + templates: + - name: decision-tree-classifier + container: + args: [] + command: [python, decision_tree.py, --data, /tmp/inputs/Data/data, --accuracy, + /tmp/outputs/Accuracy/data] + image: lightnighttw/kubeflow:decision_tree_v2 + resources: + limits: + cpu: 2 + inputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/inputs/Data/data} + outputs: + parameters: + - name: decision-tree-classifier-Accuracy + valueFrom: {path: /tmp/outputs/Accuracy/data} + artifacts: + - {name: decision-tree-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains + a decision tree classifier", "implementation": {"container": {"command": + ["python", "decision_tree.py", "--data", {"inputPath": "Data"}, "--accuracy", + {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:decision_tree_v2"}}, + "inputs": [{"description": "Path where data is stored.", "name": "Data", + "type": "LocalPath"}], "name": "Decision Tree classifier", "outputs": [{"description": + "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "c5c232a9654213b3b222693949b71a5d561d04ed09543c20b6f8f2eab651ae0b", "url": + "decision_tree/decision_tree.yaml"}'} + - name: download-data-function + container: + args: [] + command: [python, download_data.py, --data, /tmp/outputs/Data/data] + image: lightnighttw/kubeflow:download_data + resources: + limits: + cpu: 2 + outputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/outputs/Data/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Download + toy data from sklearn datasets", "implementation": {"container": {"command": + ["python", "download_data.py", "--data", {"outputPath": "Data"}], "image": + "lightnighttw/kubeflow:download_data"}}, "name": "Download Data Function", + "outputs": [{"description": "Path where data will be stored.", "name": "Data", + "type": "LocalPath"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "467750defdccfec51c3af2a7eb853f74235f5f97329006d72bf33ff6e15ed02d", "url": + "download_data/download_data.yaml"}'} + - name: logistic-regression-classifier + container: + args: [] + command: [python, logistic_regression.py, --data, /tmp/inputs/Data/data, --accuracy, + /tmp/outputs/Accuracy/data] + image: lightnighttw/kubeflow:logistic_regression + resources: + limits: + cpu: 2 + inputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/inputs/Data/data} + outputs: + parameters: + - name: logistic-regression-classifier-Accuracy + valueFrom: {path: /tmp/outputs/Accuracy/data} + artifacts: + - {name: logistic-regression-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Trains + a Logistic Regression Classifier", "implementation": {"container": {"command": + ["python", "logistic_regression.py", "--data", {"inputPath": "Data"}, "--accuracy", + {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:logistic_regression"}}, + "inputs": [{"description": "Path where data is stored.", "name": "Data", + "type": "LocalPath"}], "name": "Logistic Regression Classifier", "outputs": + [{"description": "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', + pipelines.kubeflow.org/component_ref: '{"digest": "a8d1e77d07d18a75bef200aee96f35136833fc4bb535f33fd949a307beb094c2", + "url": "logistic_regression/logistic_regression.yaml"}'} + - name: random-forest-classifier + container: + args: [] + command: [python, randomforest.py, --data, /tmp/inputs/Data/data, --accuracy, + /tmp/outputs/Accuracy/data] + image: lightnighttw/kubeflow:random_forest_v4 + resources: + limits: + cpu: 2 + inputs: + artifacts: + - {name: download-data-function-Data, path: /tmp/inputs/Data/data} + outputs: + parameters: + - name: random-forest-classifier-Accuracy + valueFrom: {path: /tmp/outputs/Accuracy/data} + artifacts: + - {name: random-forest-classifier-Accuracy, path: /tmp/outputs/Accuracy/data} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"description": "Train + a random forest classifier", "implementation": {"container": {"command": + ["python", "randomforest.py", "--data", {"inputPath": "Data"}, "--accuracy", + {"outputPath": "Accuracy"}], "image": "lightnighttw/kubeflow:random_forest_v4"}}, + "inputs": [{"description": "Path where data is stored.", "name": "Data", + "type": "LocalPath"}], "name": "Random Forest classifier", "outputs": [{"description": + "Accuracy metric", "name": "Accuracy", "type": "Float"}]}', pipelines.kubeflow.org/component_ref: '{"digest": + "b49b12da3371976eddf41d662685bb49d71b419d516de65efdd90938d2c706bc", "url": + "randomForest/random_forest.yaml"}'} + - name: show-results + container: + args: [--decision-tree, '{{inputs.parameters.decision-tree-classifier-Accuracy}}', + --logistic-regression, '{{inputs.parameters.logistic-regression-classifier-Accuracy}}', + --random-forest, '{{inputs.parameters.random-forest-classifier-Accuracy}}'] + command: + - sh + - -ec + - | + program_path=$(mktemp) + printf "%s" "$0" > "$program_path" + python3 -u "$program_path" "$@" + - | + def show_results(decision_tree, logistic_regression, random_forest): + # Given the outputs from decision_tree and logistic regression components + # the results are shown. + + print(f"Decision tree (accuracy): {decision_tree}") + print(f"Logistic regression (accuracy): {logistic_regression}") + print(f"Random forest (accuracy): {random_forest}") + + import argparse + _parser = argparse.ArgumentParser(prog='Show results', description='') + _parser.add_argument("--decision-tree", dest="decision_tree", type=float, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--logistic-regression", dest="logistic_regression", type=float, required=True, default=argparse.SUPPRESS) + _parser.add_argument("--random-forest", dest="random_forest", type=float, required=True, default=argparse.SUPPRESS) + _parsed_args = vars(_parser.parse_args()) + + _outputs = show_results(**_parsed_args) + image: python:3.7 + resources: + limits: + cpu: 2 + inputs: + parameters: + - {name: decision-tree-classifier-Accuracy} + - {name: logistic-regression-classifier-Accuracy} + - {name: random-forest-classifier-Accuracy} + metadata: + labels: + pipelines.kubeflow.org/kfp_sdk_version: 1.8.9 + pipelines.kubeflow.org/pipeline-sdk-type: kfp + pipelines.kubeflow.org/enable_caching: "true" + annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container": + {"args": ["--decision-tree", {"inputValue": "decision_tree"}, "--logistic-regression", + {"inputValue": "logistic_regression"}, "--random-forest", {"inputValue": + "random_forest"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf + \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", + "def show_results(decision_tree, logistic_regression, random_forest):\n # + Given the outputs from decision_tree and logistic regression components\n # + the results are shown.\n\n print(f\"Decision tree (accuracy): {decision_tree}\")\n print(f\"Logistic + regression (accuracy): {logistic_regression}\")\n print(f\"Random forest + (accuracy): {random_forest}\")\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Show + results'', description='''')\n_parser.add_argument(\"--decision-tree\", + dest=\"decision_tree\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--logistic-regression\", + dest=\"logistic_regression\", type=float, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--random-forest\", + dest=\"random_forest\", type=float, required=True, default=argparse.SUPPRESS)\n_parsed_args + = vars(_parser.parse_args())\n\n_outputs = show_results(**_parsed_args)\n"], + "image": "python:3.7"}}, "inputs": [{"name": "decision_tree", "type": "Float"}, + {"name": "logistic_regression", "type": "Float"}, {"name": "random_forest", + "type": "Float"}], "name": "Show results"}', pipelines.kubeflow.org/component_ref: '{}', + pipelines.kubeflow.org/arguments.parameters: '{"decision_tree": "{{inputs.parameters.decision-tree-classifier-Accuracy}}", + "logistic_regression": "{{inputs.parameters.logistic-regression-classifier-Accuracy}}", + "random_forest": "{{inputs.parameters.random-forest-classifier-Accuracy}}"}'} + - name: three-pipeline + dag: + tasks: + - name: decision-tree-classifier + template: decision-tree-classifier + dependencies: [download-data-function] + arguments: + artifacts: + - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} + - {name: download-data-function, template: download-data-function} + - name: logistic-regression-classifier + template: logistic-regression-classifier + dependencies: [download-data-function] + arguments: + artifacts: + - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} + - name: random-forest-classifier + template: random-forest-classifier + dependencies: [download-data-function] + arguments: + artifacts: + - {name: download-data-function-Data, from: '{{tasks.download-data-function.outputs.artifacts.download-data-function-Data}}'} + - name: show-results + template: show-results + dependencies: [decision-tree-classifier, logistic-regression-classifier, random-forest-classifier] + arguments: + parameters: + - {name: decision-tree-classifier-Accuracy, value: '{{tasks.decision-tree-classifier.outputs.parameters.decision-tree-classifier-Accuracy}}'} + - {name: logistic-regression-classifier-Accuracy, value: '{{tasks.logistic-regression-classifier.outputs.parameters.logistic-regression-classifier-Accuracy}}'} + - {name: random-forest-classifier-Accuracy, value: '{{tasks.random-forest-classifier.outputs.parameters.random-forest-classifier-Accuracy}}'} + arguments: + parameters: [] + serviceAccountName: pipeline-runner diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/requirements.txt b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/requirements.txt new file mode 100644 index 000000000..0a8547bcc --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/py/requirements.txt @@ -0,0 +1,2 @@ +setuptools +wheel diff --git a/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/settings.js b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/settings.js new file mode 100644 index 000000000..332013ee1 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/1.connect-kubeflow/settings.js @@ -0,0 +1,498 @@ +/** + * This is the default settings file provided by Node-RED. + * + * It can contain any valid JavaScript code that will get run when Node-RED + * is started. + * + * Lines that start with // are commented out. + * Each entry should be separated from the entries above and below by a comma ',' + * + * For more information about individual settings, refer to the documentation: + * https://nodered.org/docs/user-guide/runtime/configuration + * + * The settings are split into the following sections: + * - Flow File and User Directory Settings + * - Security + * - Server Settings + * - Runtime Settings + * - Editor Settings + * - Node Settings + * + **/ + +module.exports = { + +/******************************************************************************* + * Flow File and User Directory Settings + * - flowFile + * - credentialSecret + * - flowFilePretty + * - userDir + * - nodesDir + ******************************************************************************/ + + /** The file containing the flows. If not set, defaults to flows_.json **/ + flowFile: 'flows.json', + + /** By default, credentials are encrypted in storage using a generated key. To + * specify your own secret, set the following property. + * If you want to disable encryption of credentials, set this property to false. + * Note: once you set this property, do not change it - doing so will prevent + * node-red from being able to decrypt your existing credentials and they will be + * lost. + */ + //credentialSecret: "a-secret-key", + credentialSecret: process.env.NODE_RED_CREDENTIAL_SECRET, + + /** By default, the flow JSON will be formatted over multiple lines making + * it easier to compare changes when using version control. + * To disable pretty-printing of the JSON set the following property to false. + */ + flowFilePretty: true, + + /** By default, all user data is stored in a directory called `.node-red` under + * the user's home directory. To use a different location, the following + * property can be used + */ + //userDir: '/home/nol/.node-red/', + + /** Node-RED scans the `nodes` directory in the userDir to find local node files. + * The following property can be used to specify an additional directory to scan. + */ + //nodesDir: '/home/nol/.node-red/nodes', + +/******************************************************************************* + * Security + * - adminAuth + * - https + * - httpsRefreshInterval + * - requireHttps + * - httpNodeAuth + * - httpStaticAuth + ******************************************************************************/ + + /** To password protect the Node-RED editor and admin API, the following + * property can be used. See http://nodered.org/docs/security.html for details. + */ + //adminAuth: { + // type: "credentials", + // users: [{ + // username: "admin", + // password: "$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN.", + // permissions: "*" + // }] + //}, + + /** The following property can be used to enable HTTPS + * This property can be either an object, containing both a (private) key + * and a (public) certificate, or a function that returns such an object. + * See http://nodejs.org/api/https.html#https_https_createserver_options_requestlistener + * for details of its contents. + */ + + /** Option 1: static object */ + //https: { + // key: require("fs").readFileSync('privkey.pem'), + // cert: require("fs").readFileSync('cert.pem') + //}, + + /** Option 2: function that returns the HTTP configuration object */ + // https: function() { + // // This function should return the options object, or a Promise + // // that resolves to the options object + // return { + // key: require("fs").readFileSync('privkey.pem'), + // cert: require("fs").readFileSync('cert.pem') + // } + // }, + + /** If the `https` setting is a function, the following setting can be used + * to set how often, in hours, the function will be called. That can be used + * to refresh any certificates. + */ + //httpsRefreshInterval : 12, + + /** The following property can be used to cause insecure HTTP connections to + * be redirected to HTTPS. + */ + //requireHttps: true, + + /** To password protect the node-defined HTTP endpoints (httpNodeRoot), + * including node-red-dashboard, or the static content (httpStatic), the + * following properties can be used. + * The `pass` field is a bcrypt hash of the password. + * See http://nodered.org/docs/security.html#generating-the-password-hash + */ + //httpNodeAuth: {user:"user",pass:"$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN."}, + //httpStaticAuth: {user:"user",pass:"$2a$08$zZWtXTja0fB1pzD4sHCMyOCMYz2Z6dNbM6tl8sJogENOMcxWV9DN."}, + +/******************************************************************************* + * Server Settings + * - uiPort + * - uiHost + * - apiMaxLength + * - httpServerOptions + * - httpAdminRoot + * - httpAdminMiddleware + * - httpNodeRoot + * - httpNodeCors + * - httpNodeMiddleware + * - httpStatic + ******************************************************************************/ + + /** the tcp port that the Node-RED web server is listening on */ + uiPort: process.env.PORT || 1880, + + /** By default, the Node-RED UI accepts connections on all IPv4 interfaces. + * To listen on all IPv6 addresses, set uiHost to "::", + * The following property can be used to listen on a specific interface. For + * example, the following would only allow connections from the local machine. + */ + //uiHost: "127.0.0.1", + + /** The maximum size of HTTP request that will be accepted by the runtime api. + * Default: 5mb + */ + //apiMaxLength: '5mb', + + /** The following property can be used to pass custom options to the Express.js + * server used by Node-RED. For a full list of available options, refer + * to http://expressjs.com/en/api.html#app.settings.table + */ + //httpServerOptions: { }, + + /** By default, the Node-RED UI is available at http://localhost:1880/ + * The following property can be used to specify a different root path. + * If set to false, this is disabled. + */ + //httpAdminRoot: '/admin', + + /** The following property can be used to add a custom middleware function + * in front of all admin http routes. For example, to set custom http + * headers. It can be a single function or an array of middleware functions. + */ + // httpAdminMiddleware: function(req,res,next) { + // // Set the X-Frame-Options header to limit where the editor + // // can be embedded + // //res.set('X-Frame-Options', 'sameorigin'); + // next(); + // }, + + + /** Some nodes, such as HTTP In, can be used to listen for incoming http requests. + * By default, these are served relative to '/'. The following property + * can be used to specifiy a different root path. If set to false, this is + * disabled. + */ + //httpNodeRoot: '/red-nodes', + + /** The following property can be used to configure cross-origin resource sharing + * in the HTTP nodes. + * See https://github.com/troygoode/node-cors#configuration-options for + * details on its contents. The following is a basic permissive set of options: + */ + //httpNodeCors: { + // origin: "*", + // methods: "GET,PUT,POST,DELETE" + //}, + + /** If you need to set an http proxy please set an environment variable + * called http_proxy (or HTTP_PROXY) outside of Node-RED in the operating system. + * For example - http_proxy=http://myproxy.com:8080 + * (Setting it here will have no effect) + * You may also specify no_proxy (or NO_PROXY) to supply a comma separated + * list of domains to not proxy, eg - no_proxy=.acme.co,.acme.co.uk + */ + + /** The following property can be used to add a custom middleware function + * in front of all http in nodes. This allows custom authentication to be + * applied to all http in nodes, or any other sort of common request processing. + * It can be a single function or an array of middleware functions. + */ + //httpNodeMiddleware: function(req,res,next) { + // // Handle/reject the request, or pass it on to the http in node by calling next(); + // // Optionally skip our rawBodyParser by setting this to true; + // //req.skipRawBodyParser = true; + // next(); + //}, + + /** When httpAdminRoot is used to move the UI to a different root path, the + * following property can be used to identify a directory of static content + * that should be served at http://localhost:1880/. + */ + //httpStatic: '/home/nol/node-red-static/', + +/******************************************************************************* + * Runtime Settings + * - lang + * - logging + * - contextStorage + * - exportGlobalContextKeys + * - externalModules + ******************************************************************************/ + + /** Uncomment the following to run node-red in your preferred language. + * Available languages include: en-US (default), ja, de, zh-CN, zh-TW, ru, ko + * Some languages are more complete than others. + */ + // lang: "de", + + /** Configure the logging output */ + logging: { + /** Only console logging is currently supported */ + console: { + /** Level of logging to be recorded. Options are: + * fatal - only those errors which make the application unusable should be recorded + * error - record errors which are deemed fatal for a particular request + fatal errors + * warn - record problems which are non fatal + errors + fatal errors + * info - record information about the general running of the application + warn + error + fatal errors + * debug - record information which is more verbose than info + info + warn + error + fatal errors + * trace - record very detailed logging + debug + info + warn + error + fatal errors + * off - turn off all logging (doesn't affect metrics or audit) + */ + level: "info", + /** Whether or not to include metric events in the log output */ + metrics: false, + /** Whether or not to include audit events in the log output */ + audit: false + } + }, + + /** Context Storage + * The following property can be used to enable context storage. The configuration + * provided here will enable file-based context that flushes to disk every 30 seconds. + * Refer to the documentation for further options: https://nodered.org/docs/api/context/ + */ + //contextStorage: { + // default: { + // module:"localfilesystem" + // }, + //}, + + /** `global.keys()` returns a list of all properties set in global context. + * This allows them to be displayed in the Context Sidebar within the editor. + * In some circumstances it is not desirable to expose them to the editor. The + * following property can be used to hide any property set in `functionGlobalContext` + * from being list by `global.keys()`. + * By default, the property is set to false to avoid accidental exposure of + * their values. Setting this to true will cause the keys to be listed. + */ + exportGlobalContextKeys: false, + + /** Configure how the runtime will handle external npm modules. + * This covers: + * - whether the editor will allow new node modules to be installed + * - whether nodes, such as the Function node are allowed to have their + * own dynamically configured dependencies. + * The allow/denyList options can be used to limit what modules the runtime + * will install/load. It can use '*' as a wildcard that matches anything. + */ + externalModules: { + // autoInstall: false, /** Whether the runtime will attempt to automatically install missing modules */ + // autoInstallRetry: 30, /** Interval, in seconds, between reinstall attempts */ + // palette: { /** Configuration for the Palette Manager */ + // allowInstall: true, /** Enable the Palette Manager in the editor */ + // allowUpdate: true, /** Allow modules to be updated in the Palette Manager */ + // allowUpload: true, /** Allow module tgz files to be uploaded and installed */ + // allowList: ['*'], + // denyList: [], + // allowUpdateList: ['*'], + // denyUpdateList: [] + // }, + // modules: { /** Configuration for node-specified modules */ + // allowInstall: true, + // allowList: [], + // denyList: [] + // } + }, + + +/******************************************************************************* + * Editor Settings + * - disableEditor + * - editorTheme + ******************************************************************************/ + + /** The following property can be used to disable the editor. The admin API + * is not affected by this option. To disable both the editor and the admin + * API, use either the httpRoot or httpAdminRoot properties + */ + //disableEditor: false, + + /** Customising the editor + * See https://nodered.org/docs/user-guide/runtime/configuration#editor-themes + * for all available options. + */ + editorTheme: { + /** The following property can be used to set a custom theme for the editor. + * See https://github.com/node-red-contrib-themes/theme-collection for + * a collection of themes to chose from. + */ + //theme: "", + + /** To disable the 'Welcome to Node-RED' tour that is displayed the first + * time you access the editor for each release of Node-RED, set this to false + */ + //tours: false, + + palette: { + /** The following property can be used to order the categories in the editor + * palette. If a node's category is not in the list, the category will get + * added to the end of the palette. + * If not set, the following default order is used: + */ + //categories: ['subflows', 'common', 'function', 'network', 'sequence', 'parser', 'storage'], + }, + + projects: { + /** To enable the Projects feature, set this value to true */ + enabled: false, + workflow: { + /** Set the default projects workflow mode. + * - manual - you must manually commit changes + * - auto - changes are automatically committed + * This can be overridden per-user from the 'Git config' + * section of 'User Settings' within the editor + */ + mode: "manual" + } + }, + + codeEditor: { + /** Select the text editor component used by the editor. + * Defaults to "ace", but can be set to "ace" or "monaco" + */ + lib: "ace", + options: { + /** The follow options only apply if the editor is set to "monaco" + * + * theme - must match the file name of a theme in + * packages/node_modules/@node-red/editor-client/src/vendor/monaco/dist/theme + * e.g. "tomorrow-night", "upstream-sunburst", "github", "my-theme" + */ + theme: "vs", + /** other overrides can be set e.g. fontSize, fontFamily, fontLigatures etc. + * for the full list, see https://microsoft.github.io/monaco-editor/api/interfaces/monaco.editor.istandaloneeditorconstructionoptions.html + */ + //fontSize: 14, + //fontFamily: "Cascadia Code, Fira Code, Consolas, 'Courier New', monospace", + //fontLigatures: true, + } + } + }, + +/******************************************************************************* + * Node Settings + * - fileWorkingDirectory + * - functionGlobalContext + * - functionExternalModules + * - nodeMessageBufferMaxLength + * - ui (for use with Node-RED Dashboard) + * - debugUseColors + * - debugMaxLength + * - execMaxBufferSize + * - httpRequestTimeout + * - mqttReconnectTime + * - serialReconnectTime + * - socketReconnectTime + * - socketTimeout + * - tcpMsgQueueSize + * - inboundWebSocketTimeout + * - tlsConfigDisableLocalFiles + * - webSocketNodeVerifyClient + ******************************************************************************/ + + /** The working directory to handle relative file paths from within the File nodes + * defaults to the working directory of the Node-RED process. + */ + //fileWorkingDirectory: "", + + /** Allow the Function node to load additional npm modules directly */ + functionExternalModules: true, + + /** The following property can be used to set predefined values in Global Context. + * This allows extra node modules to be made available with in Function node. + * For example, the following: + * functionGlobalContext: { os:require('os') } + * will allow the `os` module to be accessed in a Function node using: + * global.get("os") + */ + functionGlobalContext: { + // os:require('os'), + }, + + /** The maximum number of messages nodes will buffer internally as part of their + * operation. This applies across a range of nodes that operate on message sequences. + * defaults to no limit. A value of 0 also means no limit is applied. + */ + //nodeMessageBufferMaxLength: 0, + + /** If you installed the optional node-red-dashboard you can set it's path + * relative to httpNodeRoot + * Other optional properties include + * readOnly:{boolean}, + * middleware:{function or array}, (req,res,next) - http middleware + * ioMiddleware:{function or array}, (socket,next) - socket.io middleware + */ + //ui: { path: "ui" }, + + /** Colourise the console output of the debug node */ + //debugUseColors: true, + + /** The maximum length, in characters, of any message sent to the debug sidebar tab */ + debugMaxLength: 1000, + + /** Maximum buffer size for the exec node. Defaults to 10Mb */ + //execMaxBufferSize: 10000000, + + /** Timeout in milliseconds for HTTP request connections. Defaults to 120s */ + //httpRequestTimeout: 120000, + + /** Retry time in milliseconds for MQTT connections */ + mqttReconnectTime: 15000, + + /** Retry time in milliseconds for Serial port connections */ + serialReconnectTime: 15000, + + /** Retry time in milliseconds for TCP socket connections */ + //socketReconnectTime: 10000, + + /** Timeout in milliseconds for TCP server socket connections. Defaults to no timeout */ + //socketTimeout: 120000, + + /** Maximum number of messages to wait in queue while attempting to connect to TCP socket + * defaults to 1000 + */ + //tcpMsgQueueSize: 2000, + + /** Timeout in milliseconds for inbound WebSocket connections that do not + * match any configured node. Defaults to 5000 + */ + //inboundWebSocketTimeout: 5000, + + /** To disable the option for using local files for storing keys and + * certificates in the TLS configuration node, set this to true. + */ + //tlsConfigDisableLocalFiles: true, + + /** The following property can be used to verify websocket connection attempts. + * This allows, for example, the HTTP request headers to be checked to ensure + * they include valid authentication information. + */ + //webSocketNodeVerifyClient: function(info) { + // /** 'info' has three properties: + // * - origin : the value in the Origin header + // * - req : the HTTP request + // * - secure : true if req.connection.authorized or req.connection.encrypted is set + // * + // * The function should return true if the connection should be accepted, false otherwise. + // * + // * Alternatively, if this function is defined to accept a second argument, callback, + // * it can be used to verify the client asynchronously. + // * The callback takes three arguments: + // * - result : boolean, whether to accept the connection or not + // * - code : if result is false, the HTTP error status to return + // * - reason: if result is false, the HTTP reason string to return + // */ + //}, +} diff --git a/Tunghai-CS-project-main/kubeflow/examples/Dockerfile b/Tunghai-CS-project-main/kubeflow/examples/Dockerfile new file mode 100644 index 000000000..f195a77fd --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/Dockerfile @@ -0,0 +1,20 @@ +FROM nodered/node-red:2.2.3-12 + +ARG PREFIX + +ENV NODE_OPTIONS=--max_old_space_size=128 + +USER root + +RUN apk update && \ + apk add py3-pip + +USER node-red +# Copy package.json to the WORKDIR so npm builds all +# of your added nodes modules for Node-RED +COPY package.json . +RUN npm install --unsafe-perm --no-update-notifier --no-fund --only=production + +ADD scripts/entrypoint.sh . + +ENTRYPOINT ["./entrypoint.sh"] diff --git a/Tunghai-CS-project-main/kubeflow/examples/README.md b/Tunghai-CS-project-main/kubeflow/examples/README.md new file mode 100644 index 000000000..c56f4dd28 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/README.md @@ -0,0 +1,40 @@ +#### Examples + +This example folder contains multiple node-red flow setups, each is isolated with a folder that allows you to run the different examples with the same settings. + +##### Add customized npm package + +You can add customized npm package via `npm install` or directly modify `packages.json`. Then rebuild your container image. + +##### Build + +Simply to run the following command to build node-red image + +``` +./build.sh +``` + +##### Run the container image + +To run the container image, use + +``` +KUBEFLOW_HOST= \ +KUBEFLOW_USERNAME= \ +KUBEFLOW_PASSWORD= \ +./run.sh +``` + +which would mount the current folder (i.e. ./example) onto the containers. We took this as a convenient step as you could change codes with node-red UI and the mounting volume allows the changes to be reflected onto your local file system. + +The example-args allows you to specify which example you want to run, for example + +``` +./run.sh 0.helloworld +``` + +would run the `0.helloworld` example. + +##### Visit vis UI + +then you can go to UI, check it out: http://127.0.0.1:1880/ diff --git a/Tunghai-CS-project-main/kubeflow/examples/build.sh b/Tunghai-CS-project-main/kubeflow/examples/build.sh new file mode 100644 index 000000000..37ad5136d --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/build.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash + +docker compose build diff --git a/Tunghai-CS-project-main/kubeflow/examples/docker-compose.yaml b/Tunghai-CS-project-main/kubeflow/examples/docker-compose.yaml new file mode 100644 index 000000000..2a9619477 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/docker-compose.yaml @@ -0,0 +1,33 @@ +################################################################################ +# Node-RED Stack or Compose +################################################################################ +# docker stack deploy node-red --compose-file docker-compose-node-red.yml +# docker-compose -f docker-compose-node-red.yml -p myNoderedProject up +################################################################################ +version: "3.7" + +services: + node-red: + image: reg.footprint-ai.com/public/kube-nodered:latest + build: + context: . + dockerfile: Dockerfile + labels: + # workaround around a docker-compose bug when image is also used by docker run (https://github.com/docker/compose/issues/10162) + com.docker.compose.container-number: "1" + environment: + - TZ=Asia/Taipei + - USERDIR=$USERDIR + - NODE_RED_CREDENTIAL_SECRET=noderedtutorial + - KUBEFLOW_HOST=$KUBEFLOW_HOST + - KUBEFLOW_USERNAME=$KUBEFLOW_USERNAME + - KUBEFLOW_PASSWORD=$KUBEFLOW_PASSWORD + ports: + - "1880:1880" + networks: + - node-red-net + volumes: + - ./:/data + +networks: + node-red-net: diff --git a/Tunghai-CS-project-main/kubeflow/examples/package-lock.json b/Tunghai-CS-project-main/kubeflow/examples/package-lock.json new file mode 100644 index 000000000..a333a1615 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/package-lock.json @@ -0,0 +1,10782 @@ +{ + "name": "node-red", + "version": "2.2.3", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "node-red", + "version": "2.2.3", + "license": "Apache-2.0", + "dependencies": { + "acorn": "8.7.0", + "acorn-walk": "8.2.0", + "ajv": "8.10.0", + "async-mutex": "0.3.2", + "basic-auth": "2.0.1", + "bcryptjs": "2.4.3", + "body-parser": "1.19.1", + "cheerio": "1.0.0-rc.10", + "clone": "2.1.2", + "content-type": "1.0.4", + "cookie": "0.4.2", + "cookie-parser": "1.4.6", + "cors": "2.8.5", + "cronosjs": "1.7.1", + "denque": "2.0.1", + "express": "4.17.2", + "express-session": "1.17.2", + "form-data": "4.0.0", 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programming for event-driven applications", + "homepage": "http://nodered.org", + "license": "Apache-2.0", + "repository": { + "type": "git", + "url": "https://github.com/node-red/node-red.git" + }, + "private": "true", + "scripts": { + "start": "node $NODE_OPTIONS node_modules/node-red/red.js", + "test": "grunt", + "build": "grunt build", + "dev": "grunt dev", + "build-dev": "grunt build-dev", + "docs": "grunt docs" + }, + "contributors": [ + { + "name": "Nick O'Leary" + }, + { + "name": "Dave Conway-Jones" + } + ], + "dependencies": { + "acorn": "8.7.0", + "acorn-walk": "8.2.0", + "ajv": "8.10.0", + "async-mutex": "0.3.2", + "basic-auth": "2.0.1", + "bcryptjs": "2.4.3", + "body-parser": "1.19.1", + "cheerio": "1.0.0-rc.10", + "clone": "2.1.2", + "content-type": "1.0.4", + "cookie": "0.4.2", + "cookie-parser": "1.4.6", + "cors": "2.8.5", + "cronosjs": "1.7.1", + "denque": "2.0.1", + "express": "4.17.2", + "express-session": "1.17.2", + "form-data": "4.0.0", + "fs-extra": 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"stoppable": "^1.1.0", + "supertest": "6.2.2" + }, + "engines": { + "node": ">=12" + } +} diff --git a/Tunghai-CS-project-main/kubeflow/examples/run.sh b/Tunghai-CS-project-main/kubeflow/examples/run.sh new file mode 100644 index 000000000..daca187e0 --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/run.sh @@ -0,0 +1,15 @@ +#!/usr/bin/env bash + +target=$1 + +if [ "$#" -eq 1 ] +then + USERDIR=/data/$target +else + USERDIR=/data/0.helloworld +fi + +echo "run with userdir=$USERDIR" + +USERDIR=$USERDIR docker compose up +#USERDIR=$USERDIR docker compose convert diff --git a/Tunghai-CS-project-main/kubeflow/examples/scripts/entrypoint.sh b/Tunghai-CS-project-main/kubeflow/examples/scripts/entrypoint.sh new file mode 100644 index 000000000..705054c0a --- /dev/null +++ b/Tunghai-CS-project-main/kubeflow/examples/scripts/entrypoint.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +trap stop SIGINT SIGTERM + +function stop() { + kill $CHILD_PID + wait $CHILD_PID +} + +/usr/local/bin/node $NODE_OPTIONS node_modules/node-red/red.js --userDir $USERDIR & + +CHILD_PID="$!" + +wait "${CHILD_PID}"