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Enable automation test for featurestore samples (#2585)
* Enable automation test for featurestore samples * Update setup resources * Fix typo and reformat the notebook * Add python package to cluster * Add import package * Run script sequentially * Put script run in the same process * Update to python output command * Replace output command in all notebooks during test * Update mounted path * Update the relative path * Restructure the file hierarchy * Regenerate feature store notebook workflow * Update relateive path * List folder in current path * Update relative folder path * Update storage account name and user object id * Get signed in user id * Update user object id * Update user id * Enable notebook 1 first
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.github/workflows/sdk-featurestore_sample-test_featurestore_sdk_samples.yml
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| # This code is autogenerated. | ||
| # Code is generated by running custom script: python3 readme.py | ||
| # Any manual changes to this file may cause incorrect behavior. | ||
| # Any manual changes will be overwritten if the code is regenerated. | ||
|
|
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| name: sdk-featurestore_sample-test_featurestore_sdk_samples | ||
| # This file is created by sdk/python/readme.py. | ||
| # Please do not edit directly. | ||
| on: | ||
| workflow_dispatch: | ||
| schedule: | ||
| - cron: "22 2/12 * * *" | ||
| pull_request: | ||
| branches: | ||
| - main | ||
| paths: | ||
| - sdk/python/featurestore_sample/** | ||
| - .github/workflows/sdk-featurestore_sample-test_featurestore_sdk_samples.yml | ||
| - sdk/python/dev-requirements.txt | ||
| - infra/bootstrapping/** | ||
| - sdk/python/setup.sh | ||
| concurrency: | ||
| group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ||
| cancel-in-progress: true | ||
| jobs: | ||
| build: | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - name: check out repo | ||
| uses: actions/checkout@v2 | ||
| - name: setup python | ||
| uses: actions/setup-python@v2 | ||
| with: | ||
| python-version: "3.8" | ||
| - name: pip install notebook reqs | ||
| run: pip install -r sdk/python/dev-requirements.txt | ||
| - name: azure login | ||
| uses: azure/login@v1 | ||
| with: | ||
| creds: ${{secrets.AZUREML_CREDENTIALS}} | ||
| - name: bootstrap resources | ||
| run: | | ||
| echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; | ||
| bash bootstrap.sh | ||
| working-directory: infra/bootstrapping | ||
| continue-on-error: false | ||
| - name: setup SDK | ||
| run: | | ||
| source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; | ||
| source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; | ||
| bash setup.sh | ||
| working-directory: sdk/python | ||
| continue-on-error: true | ||
| - name: setup-cli | ||
| run: | | ||
| source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; | ||
| source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; | ||
| bash setup.sh | ||
| working-directory: cli | ||
| continue-on-error: true | ||
| - name: setup feature-store resources | ||
| run: | | ||
| bash -x setup-resources.sh test_featurestore_sdk_samples.ipynb | ||
| working-directory: sdk/python/featurestore_sample | ||
| continue-on-error: true | ||
| - name: run featurestore_sample/test_featurestore_sdk_samples.ipynb | ||
| run: | | ||
| source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; | ||
| source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; | ||
| bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; | ||
| bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "test_featurestore_sdk_samples.ipynb"; | ||
| [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; | ||
| papermill -k python test_featurestore_sdk_samples.ipynb test_featurestore_sdk_samples.output.ipynb | ||
| working-directory: sdk/python/featurestore_sample | ||
| - name: upload notebook's working folder as an artifact | ||
| if: ${{ always() }} | ||
| uses: actions/upload-artifact@v2 | ||
| with: | ||
| name: test_featurestore_sdk_samples | ||
| path: sdk/python/featurestore_sample |
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| from pyspark.sql import SparkSession | ||
|
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| spark = SparkSession.builder.appName("AccessData").getOrCreate() | ||
|
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| print("=======Test Notebook 1============") | ||
| with open( | ||
| "notebooks/sdk_only/1. Develop a feature set and register with managed feature store.py" | ||
| ) as f: | ||
| exec(f.read()) | ||
|
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| ## Enable test for notebook 1 first | ||
| # print("=======Test Notebook 2============") | ||
| # with open( | ||
| # "notebooks/sdk_only/2. Enable materialization and backfill feature data.py" | ||
| # ) as f: | ||
| # exec(f.read()) | ||
|
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| # print("=======Test Notebook 3============") | ||
| # with open("notebooks/sdk_only/3. Experiment and train models using features.py") as f: | ||
| # exec(f.read()) | ||
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| # print("=======Test Notebook 3============") | ||
| # with open( | ||
| # "notebooks/sdk_only/4. Enable recurrent materialization and run batch inference.py" | ||
| # ) as f: | ||
| # exec(f.read()) |
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| pip install --upgrade jupytext | ||
|
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| # <create_variables> | ||
| SUBSCRIPTION_ID=$(az account show --query id -o tsv) | ||
| LOCATION=$(az ml workspace show --query location -o tsv) | ||
| RESOURCE_GROUP=$(az group show --query name -o tsv) | ||
| AML_WORKSPACE_NAME=$(az configure -l --query "[?name=='workspace'].value" -o tsv) | ||
| OUTPUT_COMMAND="print" | ||
| FEATURE_STORAGE_ACCOUNT_NAME=${RESOURCE_GROUP}fs | ||
| USER_ID="36b5b70a-a2b2-45e6-a496-df3c2ffde085" | ||
|
|
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| # </create_variables> | ||
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| # <convert_notebook_to_py> | ||
| NOTEBOOK_1="notebooks/sdk_only/1. Develop a feature set and register with managed feature store" | ||
| NOTEBOOK_2="notebooks/sdk_only/2. Enable materialization and backfill feature data" | ||
| NOTEBOOK_3="notebooks/sdk_only/3. Experiment and train models using features" | ||
| NOTEBOOK_4="notebooks/sdk_only/4. Enable recurrent materialization and run batch inference" | ||
| jupytext --to py "${NOTEBOOK_1}.ipynb" | ||
| jupytext --to py "${NOTEBOOK_2}.ipynb" | ||
| jupytext --to py "${NOTEBOOK_3}.ipynb" | ||
| jupytext --to py "${NOTEBOOK_4}.ipynb" | ||
| # <convert_notebook_to_py> | ||
|
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| #<replace_template_values> | ||
| sed -i "s/<SUBSCRIPTION_ID>/$SUBSCRIPTION_ID/g; | ||
| s/<RESOURCE_GROUP>/$RESOURCE_GROUP/g; | ||
| s/<AML_WORKSPACE_NAME>/$AML_WORKSPACE_NAME/g;" $1 | ||
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| #<replace_template_values> | ||
| sed -i "s/display/$OUTPUT_COMMAND/g;s/.\/Users\/<your_user_alias>\/featurestore_sample/.\//g;" "${NOTEBOOK_1}.py" | ||
| sed -i "s/display/$OUTPUT_COMMAND/g;s/.\/Users\/<your_user_alias>\/featurestore_sample/.\//g; | ||
| s/<FEATURE_STORAGE_ACCOUNT_NAME>/$FEATURE_STORAGE_ACCOUNT_NAME/g; | ||
| s/<USER_AAD_OBJECTID>/$USER_ID/g;" "${NOTEBOOK_2}.py" | ||
| sed -i "s/display/$OUTPUT_COMMAND/g;s/.\/Users\/<your_user_alias>\/featurestore_sample/.\//g;" "${NOTEBOOK_3}.py" | ||
| sed -i "s/display/$OUTPUT_COMMAND/g;s/.\/Users\/<your_user_alias>\/featurestore_sample/.\//g;" "${NOTEBOOK_4}.py" |
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sdk/python/featurestore_sample/test_featurestore_sdk_samples.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Use a serverless Spark compute" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "You should have an attached Synapse Spark pool available in your workspace. Please see documentation page: [Attach and manage a Synapse Spark pool in Azure Machine Learning (preview)](https://learn.microsoft.com/azure/machine-learning/how-to-manage-synapse-spark-pool) for more details.\n", | ||
| "\n", | ||
| "**Note** - To ensure successful execution of Spark job, the identity being used for the Spark job should be assigned **Contributor** and **Storage Blob Data Contributor** roles on the Azure storage account used for data input and output." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from azure.ai.ml import MLClient, spark, Input, Output\n", | ||
| "from azure.identity import DefaultAzureCredential\n", | ||
| "from azure.ai.ml.entities import Environment\n", | ||
| "\n", | ||
| "subscription_id = \"<SUBSCRIPTION_ID>\"\n", | ||
| "resource_group = \"<RESOURCE_GROUP>\"\n", | ||
| "workspace = \"<AML_WORKSPACE_NAME>\"\n", | ||
| "ml_client = MLClient(\n", | ||
| " DefaultAzureCredential(), subscription_id, resource_group, workspace\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "spark_job = spark(\n", | ||
| " display_name=\"featurestore_sample_test\",\n", | ||
| " code=\"./\",\n", | ||
| " entry={\"file\": \"featurestore_sdk_job.py\"},\n", | ||
| " driver_cores=1,\n", | ||
| " driver_memory=\"1g\",\n", | ||
| " executor_cores=1,\n", | ||
| " executor_memory=\"1g\",\n", | ||
| " executor_instances=1,\n", | ||
| " resources={\n", | ||
| " \"instance_type\": \"Standard_E8S_V3\",\n", | ||
| " \"runtime_version\": \"3.2.0\",\n", | ||
| " },\n", | ||
| " environment=Environment(conda_file=\"project/env/conda.yml\"),\n", | ||
| ")\n", | ||
| "\n", | ||
| "returned_spark_job = ml_client.jobs.create_or_update(spark_job)\n", | ||
| "\n", | ||
| "print(returned_spark_job.id)\n", | ||
| "# Wait until the job completes\n", | ||
| "ml_client.jobs.stream(returned_spark_job.name)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3.10 - SDK V2", | ||
| "language": "python", | ||
| "name": "python310-sdkv2" | ||
| }, | ||
| "language_info": { | ||
| "name": "python", | ||
| "version": "3.7.10" | ||
| }, | ||
| "orig_nbformat": 4, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "6aeff17a1aa7735c2f7cb3a6d691fe1b4d4c3b8d2d650f644ad0f24e1b8e3f3f" | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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