fix: Fix pipeline notebook instance_type

sagemaker-pipelines/nlp/amazon_comprehend_sagemaker_pipeline/sm_pipeline_with_comprehend.ipynb

@@ -199,7 +199,6 @@
    "source": [
     "Next, we define parameters that can be set for the execution of the pipeline. They serve as variables. We define the following:\n",
     "\n",
-    "- `ProcessingInstanceType`: The number of processing instances to use for the execution of the pipeline\n",
     "- `TrainData`: Location of the training data in S3\n",
     "- `TestData`: Location of the test data in S3\n",
     "- `RoleArn`: ARN (Amazon Resource Name) of the role used for pipeline execution\n",
@@ -216,9 +215,6 @@
    "outputs": [],
    "source": [
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
-    "processing_instance_type = ParameterString(\n",
-    "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.xlarge\"\n",
-    ")\n",
     "\n",
     "input_train = ParameterString(\n",
     "    name=\"TrainData\",\n",
@@ -250,7 +246,7 @@
    "source": [
     "sklearn_processor = SKLearnProcessor(\n",
     "    framework_version=\"0.23-1\",\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=\"comprehend-process\",\n",
     "    sagemaker_session=sagemaker_session,\n",
@@ -497,7 +493,6 @@
     "pipeline = Pipeline(\n",
     "    name=pipeline_name,\n",
     "    parameters=[\n",
-    "        processing_instance_type,\n",
     "        processing_instance_count,\n",
     "        input_train,\n",
     "        input_test,\n",

sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb

@@ -211,7 +211,6 @@
     "\n",
     "The parameters defined in this workflow include:\n",
     "\n",
-    "* `processing_instance_type` - The `ml.*` instance type of the processing job.\n",
     "* `processing_instance_count` - The instance count of the processing job.\n",
     "* `instance_type` - The `ml.*` instance type of the training job.\n",
     "* `model_approval_status` - The approval status to register with the trained model for CI/CD purposes (\"PendingManualApproval\" is the default).\n",
@@ -234,9 +233,6 @@
     "\n",
     "\n",
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
-    "processing_instance_type = ParameterString(\n",
-    "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.xlarge\"\n",
-    ")\n",
     "instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n",
     "model_approval_status = ParameterString(\n",
     "    name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n",
@@ -392,7 +388,7 @@
     "\n",
     "You also specify the `framework_version` to use throughout this notebook.\n",
     "\n",
-    "Note the `processing_instance_type` and `processing_instance_count` parameters used by the processor instance."
+    "Note the `processing_instance_count` parameter used by the processor instance."
    ]
   },
   {
@@ -408,7 +404,7 @@
     "\n",
     "sklearn_processor = SKLearnProcessor(\n",
     "    framework_version=framework_version,\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=\"sklearn-abalone-process\",\n",
     "    role=role,\n",
@@ -626,9 +622,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, create an instance of a `ScriptProcessor` processor and use it in the `ProcessingStep`.\n",
-    "\n",
-    "Note the `processing_instance_type` parameter passed into the processor."
+    "Next, create an instance of a `ScriptProcessor` processor and use it in the `ProcessingStep`."
    ]
   },
   {
@@ -643,7 +637,7 @@
     "script_eval = ScriptProcessor(\n",
     "    image_uri=image_uri,\n",
     "    command=[\"python3\"],\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=1,\n",
     "    base_job_name=\"script-abalone-eval\",\n",
     "    role=role,\n",
@@ -983,7 +977,6 @@
     "pipeline = Pipeline(\n",
     "    name=pipeline_name,\n",
     "    parameters=[\n",
-    "        processing_instance_type,\n",
     "        processing_instance_count,\n",
     "        instance_type,\n",
     "        model_approval_status,\n",
@@ -1179,7 +1172,6 @@
    "source": [
     "execution = pipeline.start(\n",
     "    parameters=dict(\n",
-    "        ProcessingInstanceType=\"ml.c5.xlarge\",\n",
     "        ModelApprovalStatus=\"Approved\",\n",
     "    )\n",
     ")"
@@ -1272,9 +1264,9 @@
  "metadata": {
   "instance_type": "ml.t3.medium",
   "kernelspec": {
-   "display_name": "Python 3 (Data Science)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-2:429704687514:image/datascience-1.0"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1286,9 +1278,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.6.14"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

sagemaker-pipelines/tabular/custom_callback_pipelines_step/sagemaker-pipelines-callback-step.ipynb

@@ -941,8 +941,7 @@
     "output_data = ParameterString(\n",
     "    name=\"OutputData\", default_value=f\"s3://{default_bucket}/{taxi_prefix}_output/\"\n",
     ")\n",
-    "training_instance_count = ParameterInteger(name=\"TrainingInstanceCount\", default_value=1)\n",
-    "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.c5.xlarge\")"
+    "training_instance_count = ParameterInteger(name=\"TrainingInstanceCount\", default_value=1)"
    ]
   },
   {
@@ -1036,7 +1035,7 @@
     "    sagemaker.get_execution_role(),\n",
     "    output_path=\"s3://{}/{}/output\".format(default_bucket, model_prefix),\n",
     "    instance_count=training_instance_count,\n",
-    "    instance_type=training_instance_type,\n",
+    "    instance_type=\"ml.c5.xlarge\",\n",
     "    sagemaker_session=session,\n",
     ")\n",
     "\n",
@@ -1229,7 +1228,6 @@
     "    name=pipeline_name,\n",
     "    parameters=[\n",
     "        input_data,\n",
-    "        training_instance_type,\n",
     "        training_instance_count,\n",
     "        id_out,\n",
     "    ],\n",

sagemaker-pipelines/tabular/customizing_build_train_deploy_project/modelbuild/pipelines/customer_churn/pipeline.py

@@ -90,9 +90,6 @@ def get_pipeline(
 
     # Parameters for pipeline execution
     processing_instance_count = ParameterInteger(name="ProcessingInstanceCount", default_value=1)
-    processing_instance_type = ParameterString(
-        name="ProcessingInstanceType", default_value="ml.m5.xlarge"
-    )
     training_instance_type = ParameterString(
         name="TrainingInstanceType", default_value="ml.m5.xlarge"
     )
@@ -108,7 +105,7 @@ def get_pipeline(
     # Processing step for feature engineering
     sklearn_processor = SKLearnProcessor(
         framework_version="0.23-1",
-        instance_type=processing_instance_type,
+        instance_type="ml.m5.xlarge",
         instance_count=processing_instance_count,
         base_job_name=f"{base_job_prefix}/sklearn-CustomerChurn-preprocess",  # choose any name
         sagemaker_session=sagemaker_session,
@@ -133,7 +130,7 @@ def get_pipeline(
         region=region,
         version="1.0-1",
         py_version="py3",
-        instance_type=training_instance_type,
+        instance_type="ml.m5.xlarge",
     )
     xgb_train = Estimator(
         image_uri=image_uri,
@@ -177,7 +174,7 @@ def get_pipeline(
     script_eval = ScriptProcessor(
         image_uri=image_uri,
         command=["python3"],
-        instance_type=processing_instance_type,
+        instance_type="ml.m5.xlarge",
         instance_count=1,
         base_job_name=f"{base_job_prefix}/script-CustomerChurn-eval",
         sagemaker_session=sagemaker_session,
@@ -254,7 +251,6 @@ def get_pipeline(
     pipeline = Pipeline(
         name=pipeline_name,
         parameters=[
-            processing_instance_type,
             processing_instance_count,
             training_instance_type,
             model_approval_status,

sagemaker-pipelines/tabular/customizing_build_train_deploy_project/sagemaker-pipelines-customized-project.ipynb

@@ -246,7 +246,7 @@
     "dictionary whose names are the parameter names, and whose values are the primitive values to use as overrides of the defaults.\n",
     "\n",
     "Of particular note, based on the performance of the model, we may want to kick off another pipeline execution, but this \n",
-    "time on a compute-optimized instance type and set the model approval status automatically be \"Approved\". This means \n",
+    "time set the model approval status automatically be \"Approved\". This means\n",
     "that the model package version generated by the `RegisterModel` step will automatically be ready for deployment through \n",
     "CI/CD pipelines, such as with SageMaker Projects.\n",
     "\n",
@@ -255,7 +255,6 @@
     "\n",
     "execution = pipeline.start(\n",
     "    parameters=dict(\n",
-    "        ProcessingInstanceType=\"ml.c5.xlarge\",\n",
     "        ModelApprovalStatus=\"Approved\",\n",
     "    )\n",
     ")\n",
@@ -285,17 +284,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.6"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb

@@ -204,9 +204,6 @@
     "s3_prefix = \"lambda-step-pipeline\"\n",
     "\n",
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
-    "processing_instance_type = ParameterString(\n",
-    "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.xlarge\"\n",
-    ")\n",
     "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n",
     "model_approval_status = ParameterString(\n",
     "    name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n",
@@ -378,7 +375,7 @@
     "\n",
     "sklearn_processor = SKLearnProcessor(\n",
     "    framework_version=\"0.23-1\",\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n",
     "    sagemaker_session=sagemaker_session,\n",
@@ -557,7 +554,7 @@
     "script_eval = ScriptProcessor(\n",
     "    image_uri=image_uri,\n",
     "    command=[\"python3\"],\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=1,\n",
     "    base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n",
     "    sagemaker_session=sagemaker_session,\n",
@@ -802,7 +799,6 @@
     "pipeline = Pipeline(\n",
     "    name=pipeline_name,\n",
     "    parameters=[\n",
-    "        processing_instance_type,\n",
     "        processing_instance_count,\n",
     "        training_instance_type,\n",
     "        input_data,\n",

sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb

@@ -238,9 +238,6 @@
    "outputs": [],
    "source": [
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
-    "processing_instance_type = ParameterString(\n",
-    "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.xlarge\"\n",
-    ")\n",
     "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n",
     "model_approval_status = ParameterString(\n",
     "    name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n",
@@ -454,7 +451,7 @@
    "source": [
     "sklearn_processor = SKLearnProcessor(\n",
     "    framework_version=\"0.23-1\",\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n",
     "    sagemaker_session=sagemaker_session,\n",
@@ -975,7 +972,7 @@
     "script_eval = ScriptProcessor(\n",
     "    image_uri=image_uri,\n",
     "    command=[\"python3\"],\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=1,\n",
     "    base_job_name=f\"{base_job_prefix}/script-abalone-eval\",\n",
     "    sagemaker_session=sagemaker_session,\n",
@@ -1169,7 +1166,6 @@
     "pipeline = Pipeline(\n",
     "    name=pipeline_name,\n",
     "    parameters=[\n",
-    "        processing_instance_type,\n",
     "        processing_instance_count,\n",
     "        training_instance_type,\n",
     "        model_approval_status,\n",

sagemaker-pipelines/tabular/tensorflow2-california-housing-sagemaker-pipelines-deploy-endpoint/tensorflow2-california-housing-sagemaker-pipelines-deploy-endpoint.ipynb

@@ -285,13 +285,7 @@
     "# raw input data\n",
     "input_data = ParameterString(name=\"InputData\", default_value=raw_s3)\n",
     "\n",
-    "# processing step parameters\n",
-    "processing_instance_type = ParameterString(\n",
-    "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.large\"\n",
-    ")\n",
-    "\n",
     "# training step parameters\n",
-    "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.large\")\n",
     "training_epochs = ParameterString(name=\"TrainingEpochs\", default_value=\"100\")\n",
     "\n",
     "# model performance step parameters\n",
@@ -376,7 +370,7 @@
     "sklearn_processor = SKLearnProcessor(\n",
     "    framework_version=framework_version,\n",
     "    role=role,\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.large\",\n",
     "    instance_count=1,\n",
     "    base_job_name=\"tf2-california-housing-processing-job\",\n",
     ")\n",
@@ -428,7 +422,7 @@
     "tf2_estimator = TensorFlow(\n",
     "    source_dir=\"code\",\n",
     "    entry_point=\"train.py\",\n",
-    "    instance_type=training_instance_type,\n",
+    "    instance_type=\"ml.m5.large\",\n",
     "    instance_count=1,\n",
     "    framework_version=tensorflow_version,\n",
     "    role=role,\n",
@@ -536,7 +530,7 @@
     "# The object contains information about what container to use, what instance type etc.\n",
     "evaluate_model_processor = SKLearnProcessor(\n",
     "    framework_version=framework_version,\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.large\",\n",
     "    instance_count=1,\n",
     "    base_job_name=\"tf2-california-housing-evaluate\",\n",
     "    role=role,\n",
@@ -778,7 +772,7 @@
     "step_create_model = CreateModelStep(\n",
     "    name=\"Create-California-Housing-Model\",\n",
     "    model=model,\n",
-    "    inputs=sagemaker.inputs.CreateModelInput(instance_type=endpoint_instance_type),\n",
+    "    inputs=sagemaker.inputs.CreateModelInput(instance_type=\"ml.m5.large\"),\n",
     ")"
    ]
   },
@@ -1005,8 +999,6 @@
     "pipeline = Pipeline(\n",
     "    name=pipeline_name,\n",
     "    parameters=[\n",
-    "        processing_instance_type,\n",
-    "        training_instance_type,\n",
     "        input_data,\n",
     "        training_epochs,\n",
     "        accuracy_mse_threshold,\n",
@@ -1278,9 +1270,9 @@
  "metadata": {
   "instance_type": "ml.t3.medium",
   "kernelspec": {
-   "display_name": "Python 3 (Data Science)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/datascience-1.0"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1292,7 +1284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.6.14"
   }
  },
  "nbformat": 4,