From 77f7ad7970381a3c9ab74fc8604ab8903ec55c9b Mon Sep 17 00:00:00 2001 From: Dewen Qi Date: Sat, 28 May 2022 00:42:14 -0700 Subject: [PATCH] fix: Fix pipeline notebook instance_type --- .../sm_pipeline_with_comprehend.ipynb | 7 +----- ...ocess-train-evaluate-batch-transform.ipynb | 24 +++++++------------ .../sagemaker-pipelines-callback-step.ipynb | 6 ++--- .../pipelines/customer_churn/pipeline.py | 10 +++----- ...gemaker-pipelines-customized-project.ipynb | 14 ++--------- .../sagemaker-pipelines-lambda-step.ipynb | 8 ++----- ...pipeline-model-monitor-clarify-steps.ipynb | 8 ++----- ...-sagemaker-pipelines-deploy-endpoint.ipynb | 22 ++++++----------- ...register and deploy a pipeline model.ipynb | 10 ++------ .../sagemaker-pipelines-tuning-step.ipynb | 8 ++----- 10 files changed, 31 insertions(+), 86 deletions(-) diff --git a/sagemaker-pipelines/nlp/amazon_comprehend_sagemaker_pipeline/sm_pipeline_with_comprehend.ipynb b/sagemaker-pipelines/nlp/amazon_comprehend_sagemaker_pipeline/sm_pipeline_with_comprehend.ipynb index 2733a522d8..ff057d98bd 100644 --- a/sagemaker-pipelines/nlp/amazon_comprehend_sagemaker_pipeline/sm_pipeline_with_comprehend.ipynb +++ b/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", diff --git a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb b/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb index aa52f27f72..891f939a6a 100644 --- a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb +++ b/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 -} \ No newline at end of file +} diff --git a/sagemaker-pipelines/tabular/custom_callback_pipelines_step/sagemaker-pipelines-callback-step.ipynb b/sagemaker-pipelines/tabular/custom_callback_pipelines_step/sagemaker-pipelines-callback-step.ipynb index a66eeda652..a36145affd 100644 --- a/sagemaker-pipelines/tabular/custom_callback_pipelines_step/sagemaker-pipelines-callback-step.ipynb +++ b/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", diff --git a/sagemaker-pipelines/tabular/customizing_build_train_deploy_project/modelbuild/pipelines/customer_churn/pipeline.py b/sagemaker-pipelines/tabular/customizing_build_train_deploy_project/modelbuild/pipelines/customer_churn/pipeline.py index c96e390799..173c552356 100644 --- a/sagemaker-pipelines/tabular/customizing_build_train_deploy_project/modelbuild/pipelines/customer_churn/pipeline.py +++ b/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, diff --git a/sagemaker-pipelines/tabular/customizing_build_train_deploy_project/sagemaker-pipelines-customized-project.ipynb b/sagemaker-pipelines/tabular/customizing_build_train_deploy_project/sagemaker-pipelines-customized-project.ipynb index 73ab2f5b7f..03a6b8da2c 100644 --- a/sagemaker-pipelines/tabular/customizing_build_train_deploy_project/sagemaker-pipelines-customized-project.ipynb +++ b/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 -} +} \ No newline at end of file diff --git a/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb b/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb index 61819d2936..9466e321c3 100644 --- a/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb +++ b/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", diff --git a/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb b/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb index 119438aff3..73bbebf015 100644 --- a/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb +++ b/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", diff --git a/sagemaker-pipelines/tabular/tensorflow2-california-housing-sagemaker-pipelines-deploy-endpoint/tensorflow2-california-housing-sagemaker-pipelines-deploy-endpoint.ipynb b/sagemaker-pipelines/tabular/tensorflow2-california-housing-sagemaker-pipelines-deploy-endpoint/tensorflow2-california-housing-sagemaker-pipelines-deploy-endpoint.ipynb index df4169f55b..ce49c20323 100644 --- a/sagemaker-pipelines/tabular/tensorflow2-california-housing-sagemaker-pipelines-deploy-endpoint/tensorflow2-california-housing-sagemaker-pipelines-deploy-endpoint.ipynb +++ b/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, diff --git a/sagemaker-pipelines/tabular/train-register-deploy-pipeline-model/train register and deploy a pipeline model.ipynb b/sagemaker-pipelines/tabular/train-register-deploy-pipeline-model/train register and deploy a pipeline model.ipynb index 89ea78dc74..db3cdb6dd7 100644 --- a/sagemaker-pipelines/tabular/train-register-deploy-pipeline-model/train register and deploy a pipeline model.ipynb +++ b/sagemaker-pipelines/tabular/train-register-deploy-pipeline-model/train register and deploy a pipeline model.ipynb @@ -150,12 +150,8 @@ "\n", "# processing step parameters\n", "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\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.xlarge\")\n", "training_epochs = ParameterString(name=\"TrainingEpochs\", default_value=\"100\")\n", "\n", "# model performance step parameters\n", @@ -339,7 +335,7 @@ "\n", "sklearn_processor = SKLearnProcessor(\n", " framework_version=sklearn_framework_version,\n", - " instance_type=processing_instance_type,\n", + " instance_type=\"ml.m5.large\",\n", " instance_count=processing_instance_count,\n", " base_job_name=\"sklearn-housing-data-process\",\n", " role=role,\n", @@ -505,7 +501,7 @@ "tf2_estimator = TensorFlow(\n", " source_dir=\"code\",\n", " entry_point=\"train.py\",\n", - " instance_type=training_instance_type,\n", + " instance_type=\"ml.m5.xlarge\",\n", " instance_count=1,\n", " framework_version=tensorflow_version,\n", " role=role,\n", @@ -822,9 +818,7 @@ "pipeline = Pipeline(\n", " name=pipeline_name,\n", " parameters=[\n", - " processing_instance_type,\n", " processing_instance_count,\n", - " training_instance_type,\n", " input_data,\n", " model_approval_status,\n", " training_epochs,\n", diff --git a/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb b/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb index 705be32146..678b72a0cd 100644 --- a/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb +++ b/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb @@ -109,9 +109,6 @@ "model_package_group_name = \"tuning-job-model-packages\"\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", @@ -287,7 +284,7 @@ "\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", @@ -635,7 +632,7 @@ "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-tuning-step-eval\",\n", " sagemaker_session=sagemaker_session,\n", @@ -736,7 +733,6 @@ "pipeline = Pipeline(\n", " name=\"tuning-step-pipeline\",\n", " parameters=[\n", - " processing_instance_type,\n", " processing_instance_count,\n", " training_instance_type,\n", " input_data,\n",