Dflow is a Python framework for constructing scientific computing workflows (e.g. concurrent learning workflows) employing Argo Workflows as the workflow engine.
For dflow's users (e.g. ML application developers), dflow offers user-friendly functional programming interfaces for building their own workflows. Users need not be concerned with process control, task scheduling, observability and disaster tolerance. Users can track workflow status and handle exceptions by APIs as well as from frontend UI. Thereby users are enabled to concentrate on implementing operators and orchestrating workflows.
For dflow's developers, dflow wraps on argo SDK, keeps details of computing and storage resources from users, and provides extension abilities. While argo is a cloud-native workflow engine, dflow uses containers to decouple computing logic and scheduling logic, and uses Kubernetes to make workflows observable, reproducible and robust. Dflow is designed to be based on a distributed, heterogeneous infrastructure. The most common computing resources in scientific computing may be HPC clusters. Users can either use remote executor to manage HPC jobs within dflow (dflow-extender), or use operator to uniformly abstract HPC resources in the framework of Kubernetes (wlm-operator).
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- 1.1. Architecture
- 1.2. Common layer
- 1.2.1. Parameters and artifacts
- 1.2.2. OP template
- 1.2.3. Step
- 1.2.4. Workflow
- 1.3. Interface layer
- 1.3.1. Python OP
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- 2.1. Prepare Kubernetes cluster
- 2.2. Setup Argo Workflows
- 2.3. Install dflow
- 2.4. Run an example
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- 3.1. Common layer
- 3.1.1. Workflow management
- 3.1.2. Upload/download artifact
- 3.1.3. Output parameter and artifact of Steps
- 3.1.4. Conditional step, parameter and artifact
- 3.1.5. Produce parallel steps using loop
- 3.1.6. Timeout
- 3.1.7. Continue on failed
- 3.1.8. Continue on success number/ratio of parallel steps
- 3.1.9. Optional input artifact
- 3.1.10. Default value for output parameter
- 3.1.11. Key of step
- 3.1.12. Reuse step
- 3.1.13. Executor
- 3.1.14. Submit Slurm job by wlm-operator
- 3.2. Interface layer
- 3.2.1. Slices
- 3.2.2. Retry and error handling
- 3.2.3. Progress
- 3.2.4. Upload python packages for development
- 3.1. Common layer
The dflow consists of a common layer and an interface layer. Interface layer takes various OP templates from users, usually in the form of python classes, and transforms them into base OP templates that common layer can handle.
Common layer is an extension over argo client which provides functionalities such as file processing, computing resources management, workflow submission and management, etc.
Parameters and artifacts are data stored by the workflow and passed within the workflow. Parameters are saved as strings which can be displayed in the UI, while artifacts are saved as files.
OP template (shown as base OP in the figure above) is the fundamental building block of a workflow. It defines an operation to be executed given the input and output. Both the input and output can be parameters and/or artifacts. The most common OP template is the container OP template. Necessary arguments to be defined for the operation are the container image and scripts to be executed. Currently, two types of container OP templates are supported: ShellOPTemplate, PythonScriptOPTemplate. Shell OP template (ShellOPTemplate) defines an operation by a shell script and Python script OP template (PythonScriptOPTemplate) defines an operation by a Python script.
To use the ShellOPTemplate:
from dflow import ShellOPTemplate
simple_example_templ = ShellOPTemplate(
name="Hello",
image="alpine:latest",
script="cp /tmp/foo.txt /tmp/bar.txt && echo {{inputs.parameters.msg}} > /tmp/msg.txt",
)The above example defines a ShellOPTemplate with name = "Hello" and container image alpine:latest. The operation is to copy /tmp/foo.txt (input artifacts) to /tmp/bar.txt (output artifacts) and printout the properties of the parameters with name msg (input parameters) and redirect it to /tmp/msg.txt (value in the file is the properties of the output parameters).
To define the parameters and artifacts of this OPTemplate:
from dflow import InputParameter, InputArtifact, OutputParameter, OutputArtifact
# define input
simple_example_templ.inputs.parameters = {"msg": InputParameter()}
simple_example_templ.inputs.artifacts = {"inp_art": InputArtifact(path="/tmp/foo.txt")}
# define output
simple_example_templ.outputs.parameters = {
"msg": OutputParameter(value_from_path="/tmp/msg.txt")
}
simple_example_templ.outputs.parameters = {
"out_art": OutputArtifact(path="/tmp/bar.txt")
}In the above example, there are three things to clarify.
- The value of the input parameter is optional for the OP template, if provided, it will be regarded as the default value which can be overridden at run time.
- For the output parameter, the source where its value comes from should be specified. For the container OP template, the value may come from a certain file generated in the container (
value_from_path). - The paths to the input and output artifact in the container are required to be specified.
On the same level, one can also define a PythonScriptOPTemplate to achieve the same operation.
Step is the central block for building a workflow. A Step is created by instantiating an OP template. When a Step is initialized, values of all input parameters and sources of all input artifacts declared in the OP template must be specified.
from dflow import Step
simple_example_step = Step(
name="step0",
template=simple_example_templ,
parameters={"msg": "HelloWorld!"},
artifacts={"inp_art": foo},
)This step will instantiate the OP template created in 1.2.2. Note that foo is an artifact either uploaded from local or output of another step.
Workflow is the connecting block for building a workflow. A Workflow is created by adding Step together.
from dflow import Workflow
wf = Workflow(name="hello-world")
wf.add(simple_example_step)Submit a workflow by
wf.submit()An example using all the elements discussed in 1.2 is shown here:
Interface layer handles more Python-native OPs defined in the form of class.
PythonOPTemplate is another kind of OP template. It inherits from PythonScriptOPTemplate but allows users to define operation (OP) in the form of a Python class. As Python is a weak typed language, we impose strict type checking to PythonOP to alleviate ambiguity and unexpected behaviors.
The structures of the inputs and outputs of a PythonOP are defined in the static methods get_input_sign and get_output_sign. Each of them returns a OPIOSign object, which is a dictionary mapping from the name of a parameter/artifact to its sign.
The execution of the PythonOP is defined in the execute method. The execute method receives a OPIO object as input and outputs a OPIO object. OPIO is a dictionary mapping from the name of a parameter/artifact to its value/path. The type of the parameter value or the artifact path should be in accord with that declared in the sign. Type checking is implemented before and after the execute method.
from dflow.python import OP, OPIO, OPIOSign, Artifact
from pathlib import Path
import shutil
class SimpleExample(OP):
def __init__(self):
pass
@classmethod
def get_input_sign(cls):
return OPIOSign(
{
"msg": str,
"inp_art": Artifact(Path),
}
)
@classmethod
def get_output_sign(cls):
return OPIOSign(
{
"msg": str,
"out_art": Artifact(Path),
}
)
@OP.exec_sign_check
def execute(
self,
op_in: OPIO,
) -> OPIO:
shutil.copy(op_in["inp_art"], "bar.txt")
out_msg = op_in["msg"]
op_out = OPIO(
{
"msg": out_msg,
"out_art": Path("bar.txt"),
}
)
return op_outThe above example defines an OP SimpleExample. The operation is to copy foo.txt to bar.txt and write the properties of the parameters with name msg to msg.txt.
To use the above class as a PythonOPTemplate, we need to pass the above class to PythonOPTemplate and specify the container image. Note that pydflow must be installed in this image
from dflow.python import PythonOPTemplate
simple_example_templ = PythonOPTemplate(SimpleExample, image="dptechnology/dflow")An example using all the elements discussed in 1.3 is shown here:
Firstly, you will need a Kubernetes cluster. To setup a Kubernetes cluster on your laptop, you can download the Minikube on your PC and make sure you have Docker up and running on you PC.
After downloading, you can initiate the Kubernetes cluster using:
minikube start
2.2. Setup Argo Workflows
To get started quickly, you can use the quick start manifest. It will install Argo Workflow as well as some commonly used components:
kubectl create ns argo
kubectl apply -n argo -f https://raw.githubusercontent.com/dptech-corp/dflow/master/manifests/quick-start-postgres.yaml
If you are running Argo Workflows locally (e.g. using Minikube or Docker for Desktop), open a port-forward so you can access the namespace:
kubectl -n argo port-forward deployment/argo-server 2746:2746
This will serve the user interface on https://localhost:2746
For access to the minio object storage, open a port-forward for minio
kubectl -n argo port-forward deployment/minio 9000:9000
Make sure your Python version is not less than 3.6 and install dflow
pip install pydflow
Submit a simple workflow
python examples/test_steps.py
Then you can check the submitted workflow through argo's UI.
3. User Guide (dflow-doc)
After a workflow is submitted by wf.submit(), one can track it with APIs
wf.id: workflow ID in argowf.query_status(): query workflow status, return"Pending","Running","Suceeded", etc.wf.query_step(name=None): query step by name (support for regex), return an argo step objectstep.phase: phase of a step,"Pending","Running",Succeeded, etc.step.outputs.parameters: a dictionary of output parametersstep.outputs.artifacts: a dictionary of output artifacts
Dflow offers tools for uploading files to Minio and downloading files from Minio (default object storage in the quick start). User can upload a list of files or directories and get an artifact object, which can be used as argument of a step
artifact = upload_artifact([path1, path2])
step = Step(
...
artifacts={"foo": artifact}
)User can also download the output artifact of a step queried from a workflow (to current directory for default)
step = wf.query_step(name="hello")
download_artifact(step.outputs.artifacts["bar"])Note: dflow retains the relative path of the uploaded file/directory with respect to the current directory during uploading. If file/directory outside current directory is uploaded, its absolute path is used as the relative path in the artifact. If you want a different directory structure in the artifact with the local one, you can make soft links and then upload.
The output parameter of a Steps can be set to come from a step of it by steps.outputs.parameters["msg"].value_from_parameter = step.outputs.parameters["msg"]. Here, step must be contained in steps. For assigning output artifact for a Steps, use steps.outputs.artifacts["foo"]._from = step.outputs.parameters["foo"].
Set a step to be conditional by Step(..., when=expr) where expr is an boolean expression in string format. Such as "%s < %s" % (par1, par2). The when argument is often used as the breaking condition of recursive steps. The output parameter of a Steps can be assigned as optional by
steps.outputs.parameters["msg"].value_from_expression = if_expression(
_if=par1 < par2,
_then=par3,
_else=par4
)Similarly, the output artifact of a Steps can be assigned as optional by
steps.outputs.artifacts["foo"].from_expression = if_expression(
_if=par1 < par2,
_then=art1,
_else=art2
)with_param and with_sequence are 2 arguments of Step for automatically generating a list of parallel steps. These steps share a common OP template, and only differ in the input parameters.
A step using with_param option generates parallel steps on a list (usually from another parameter), the parallelism equals to the length of the list. Each parallel step picks an item from the list by "{{item}}", such as
step = Step(
...
parameters={"msg": "{{item}}"},
with_param=steps.inputs.parameters["msg_list"]
)A step using with_sequence option generates parallel steps on a numeric sequence. with_sequence is usually used in coordination with argo_sequence (return a sequence, start from 0 for default) and argo_len (return length of a list). Each parallel step picks a number from the sequence by "{{item}}", such as
step = Step(
...
parameters={"i": "{{item}}"},
with_sequence=argo_sequence(argo_len(steps.inputs.parameters["msg_list"]))
)Set the timeout of a step by Step(..., timeout=t). The unit is second.
Set the workflow to continue when a step fails by Step(..., continue_on_failed=True).
Set the workflow to continue when certain number/ratio of parallel steps succeed by Step(..., continue_on_num_success=n) or Step(..., continue_on_success_ratio=r).
Set an input artifact to be optional by op_template.inputs.artifacts["foo"].optional = True.
Set default value for a output parameter by op_template.outputs.parameters["msg"].default = default_value. The default value will be used when the expression in value_from_expression fails or the step is skipped.
You can set a key for a step by Step(..., key="some-key") for the convenience of locating the step. The key can be regarded as an input parameter which may contain reference of other parameters. For instance, the key of a step can change with iterations of a dynamic loop. Once key is assigned to a step, the step can be query by wf.query_step(key="some-key"). If the key is unique within the workflow, the query_step method returns a list consist of only one element.
Workflows often have some steps that are expensive to compute. The outputs of previously run steps can be reused for submitting a new workflow. E.g. a failed workflow can be restarted from a certain point after some modification of the workflow template or even outputs of completed steps. For example, submit a workflow with reused steps by wf.submit(reuse_step=[step0, step1]). Here, step0 and step1 are previously run steps returned by query_step method. Before the new workflow runs a step, it will detect if there exists a reused step whose key matches that of the step about to run. If hit, the workflow will skip the step and set its outputs as those of the reused step. To modify outputs of a step before reusing, use step0.modify_output_parameter(par_name, value) for parameters and step0.modify_output_artifact(art_name, artifact) for artifacts.
By default, for a "script step" (a step whose template is a script OP template), the Shell script or Python script runs in the container directly. Alternatively, one can modify the executor to run the script. Dflow offers an extension point for "script step" Step(..., executor=my_executor). Here, my_executor should be an instance of class derived from Executor. A Executor-derived class should specify image and command to be used for the executor, as well as a method get_script which converts original command and script to new script run by the executor.
class Executor(object):
image = None
command = None
def get_script(self, command, script):
passSlurmRemoteExecutor is provided as an example of executor. The executor submits a slurm job to a remote host and synchronize its status and logs to the dflow step. The central logic of the executor is implemented in the Golang project Dflow-extender. If you want to run a step on a slurm cluster remotely, do something like
Step(
...,
executor=SlurmRemoteExecutor(host="1.2.3.4",
username="myuser",
password="mypasswd",
header="""#!/bin/bash
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -p cpu""")
)Following the installation steps in the wlm-operator project to add Slurm partitions as virtual nodes to Kubernetes (use manifests configurator.yaml, operator-rbac.yaml, operator.yaml in this project which modified some RBAC configurations)
$ kubectl get nodes
NAME STATUS ROLES AGE VERSION
minikube Ready control-plane,master 49d v1.22.3
slurm-minikube-cpu Ready agent 131m v1.13.1-vk-N/A
slurm-minikube-dplc-ai-v100x8 Ready agent 131m v1.13.1-vk-N/A
slurm-minikube-v100 Ready agent 131m v1.13.1-vk-N/A
Then you can assign a step to be executed on a virtual node (i.e. submit a Slurm job to the corresponding partition to complete the step)
step = Step(
...
executor=SlurmJobTemplate(
header="#!/bin/sh\n#SBATCH --nodes=1",
node_selector={"kubernetes.io/hostname": "slurm-minikube-v100"}
)
)Slices helps user to slice input parameters/artifacts (which must be lists) to feed parallel steps and stack their output parameters/artifacts to lists in the same pattern. For example,
step = Step(name="parallel-tasks",
template=PythonOPTemplate(
...,
slices=Slices("{{item}}",
input_parameter=["msg"],
input_artifact=["data"],
output_artifact=["log"])
),
parameters = {
"msg": msg_list
},
artifacts={
"data": data_list
},
with_param=argo_range(5)
)In this example, each item in msg_list is passed to a parallel step as the input parameter msg, each part in data_list is passed to a parallel step as the input artifact data. Finally, the output artifacts log of all parallel steps are collected to one artifact step.outputs.artifacts["log"].
Dflow catches TransientError and FatalError thrown from OP. User can set maximum number of retries on TransientError by PythonOPTemplate(..., retry_on_transient_error=n). Timeout error is regarded as fatal error for default. To treat timeout error as transient error, set PythonOPTemplate(..., timeout_as_transient_error=True).
A OP can update progress in the runtime so that user can track its real-time progress
class Progress(OP):
progress_total = 100
...
def execute(op_in):
for i in range(10):
self.progress_current = 10 * (i + 1)
...To avoid frequently making image during development, dflow offers a interface to upload local packages into container of OP and add them to $PYTHONPATH, such as PythonOPTemplate(python_packages=["/opt/anaconda3/lib/python3.9/site-packages/numpy"]). One can also globally specify packages to be uploaded, which will affect all OPs
from dflow import upload_packages
upload_packages.append("/opt/anaconda3/lib/python3.9/site-packages/numpy")