diff --git a/Python/azureml-pipeline-databricks/.amlignore b/Python/azureml-pipeline-databricks/.amlignore new file mode 100644 index 0000000..616345c --- /dev/null +++ b/Python/azureml-pipeline-databricks/.amlignore @@ -0,0 +1,7 @@ +.ipynb_checkpoints +azureml-logs +.azureml +.git +outputs +azureml-setup +docs diff --git a/Python/azureml-pipeline-databricks/.gitignore b/Python/azureml-pipeline-databricks/.gitignore new file mode 100644 index 0000000..8047d30 --- /dev/null +++ b/Python/azureml-pipeline-databricks/.gitignore @@ -0,0 +1,122 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST +venv/ + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ +*-testresults.xml +test-output.xml + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +*.tmp +.terraform* +*.tfstate* +test-output.xml +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +*.vscode + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +.DS_Store + +# Azure ML +.azureml + +# Terraform +.terraform* +*.tfstate* +*.tmp diff --git a/Python/azureml-pipeline-databricks/README.md b/Python/azureml-pipeline-databricks/README.md new file mode 100644 index 0000000..2e8f1cf --- /dev/null +++ b/Python/azureml-pipeline-databricks/README.md @@ -0,0 +1,185 @@ +# Integrating Databricks into Azure ML Pipelines with Terraform + +This sample automates the provisioning of an ML execution environment using Terraform, and the provisioning and execution of an [Azure ML Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines) that runs a Databricks notebook doing data engineering. + +This sample demonstrates: +* Deployment of Azure ML and Databricks infrastructure using Terraform (based on the [Terraform Azure DevOps starter sample](https://github.com/microsoft/terraform-azure-devops-starter)). +* Provisioning of Databricks accounts and notebooks with Azure AD authentication, using the [databricks-client](https://pypi.org/project/databricks-client/) module. +* Unit testing of Databricks notebooks with PySpark, using the [databricks-test](https://pypi.org/project/databricks-test/) module. +* Integrating a [Databricks step](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline#databricks) into an Azure ML pipeline. + +This sample is meant to be combined with [the MLOpsPython repository](https://github.com/microsoft/MLOpsPython) +in order to add ETL / feature engineering to an ML training pipeline. The MLOpsPython repository +contains templates for subsequent steps of an MLOps pipeline, such as ML model building, +validation and scoring image deployment. + +In this sample, Databricks is used for feature engineering previously to building an ML model. A Databricks step +can also be used for model training and model batch scoring. + +## Contents + +| File/folder | Description | +|-------------------------|-------------------------------------------------------------| +| `README.md` | This README file. | +| `azure-pipelines.yml` | The Azure ML build and integration pipeline. | +| `ci_dependencies.yml` | The image definition for the CI environment. | +| `code` | The Databricks feature engineering notebook and unit tests. | +| `docs` | Images for this README file. | +| `environment_setup` | Pipelines and configuration for building the environment. | +| `ml_service` | Python script for provisioning the Azure environment. | +| `tox.ini` | Linting and unit test configuration. | + +## About the sample + +The sample contains [Terraform configuration](environment_setup/terraform) to deploy an entire environment for creating and executing a data engineering pipeline in Azure ML Pipelines. + +The [Databricks notebook](code/prepare/feature_engineering.py) performs very basic feature engineering by +removing lines with NA values from an initial dataset [diabetes.csv](./environment_setup/terraform/training-data/diabetes.csv). + +A [unit test for the notebook](code/tests/feature_engineering_test.py) is provided and runs in CI using the [databricks-test](https://pypi.org/project/databricks-test/) module. + +A [Python script](ml_service/build_ml_pipeline.py) uses the [Azure ML Python SDK](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/?view=azure-ml-py) to provision the notebook and a cluster pool into the Databricks environment, and programmatically define the structure of the Azure ML Pipeline, and submit the pipeline into the Azure ML workspace. The [CI/CD pipeline](azure-pipelines.yml) then proceeds to execute the Azure ML Pipeline. + +## Running the sample + +### Getting the code + +Fork this repository within GitHub, or clone it into your Azure DevOps project. + +### Create an Azure DevOps organization + +We use Azure DevOps for running our MLOps pipeline with build (CI), ML training and scoring service release +(CD) stages. If you don't already have an Azure DevOps organization, create one by +following the instructions [here](https://docs.microsoft.com/en-us/azure/devops/organizations/accounts/create-organization?view=azure-devops). + +If you already have an Azure DevOps organization, create a [new project](https://docs.microsoft.com/en-us/azure/devops/organizations/projects/create-project?view=azure-devops). + +### Install Azure DevOps extensions + +Install the [Terraform extension for Azure DevOps](https://marketplace.visualstudio.com/items?itemName=ms-devlabs.custom-terraform-tasks) from the Azure DevOps marketplace into your Azure DevOps organization. + +Also install the [Azure Machine Learning extension](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml). + +### Create an ARM service connection for Terraform + +The `DataOpsML ARM Connection` service connection is used by the [Azure DevOps pipeline](environment_setup/terraform-init-template.yml) to create the Azure ML workspace and associated resources through Terraform. The pipeline requires an **Azure Resource Manager** +[service connection](https://docs.microsoft.com/en-us/azure/devops/pipelines/library/service-endpoints?view=azure-devops&tabs=yaml#create-a-service-connection) at the subscription level. + +Leave the **``Resource Group``** field empty. + +**Note:** Creating the ARM service connection scope requires 'Owner' or 'User Access Administrator' permissions on the subscription. +You must also have sufficient permissions to register an application with +your Azure AD tenant, or receive the ID and secret of a service principal +from your Azure AD Administrator. That principal must have 'Contributor' +permissions on the subscription. + +### Create a storage account for the Terraform state + +[Create an Azure storage account](https://docs.microsoft.com/en-us/azure/storage/common/storage-account-create) with an arbitrary name. In the storage account, create a storage container named `terraformstate`. + +### Create a Variable Group for your Pipeline + +We make use of a variable group inside Azure DevOps to store variables and their +values that we want to make available across multiple pipelines or pipeline stages. You can either +store the values directly in [Azure DevOps](https://docs.microsoft.com/en-us/azure/devops/pipelines/library/variable-groups?view=azure-devops&tabs=designer#create-a-variable-group) +or connect to an Azure Key Vault in your subscription. Please refer to the +documentation [here](https://docs.microsoft.com/en-us/azure/devops/pipelines/library/variable-groups?view=azure-devops&tabs=designer#create-a-variable-group) to +learn more about how to create a variable group and +[link](https://docs.microsoft.com/en-us/azure/devops/pipelines/library/variable-groups?view=azure-devops&tabs=designer#use-a-variable-group) it to your pipeline. +Click on **Library** in the **Pipelines** section as indicated below: + +Create a variable group named **``terraform``**. The YAML pipeline definitions in this repository refer to this variable group by name. + +The variable group should contain the following required variables: + +| Variable Name | Suggested Value | +| ------------------------- | -------------------------------------------------------- | +| BASE_NAME | mydataops | +| LOCATION | [The [region of your Azure DevOps organization](https://docs.microsoft.com/en-us/azure/devops/organizations/accounts/change-organization-location?view=azure-devops), e.g. `westus2`] | +| TERRAFORM_BACKEND_STORAGE | [The name of the storage account you created for the Terraform state] | +| TERRAFORM_BACKEND_RG | [The resource group of the Terraform state storage account] | + +**Note:** + +The **BASE_NAME** parameter is used throughout the solution for naming +Azure resources. When the solution is used in a shared subscription, there can +be naming collisions with resources that require unique names like azure blob +storage and registry DNS naming. Make sure to give a unique value to the +BASE_NAME variable (e.g. mydataops), so that the created resources will have +unique names. The length of +the BASE_NAME value should not exceed 10 characters and it should contain numbers and lowercase letters only. + +Make sure to select the **Allow access to all pipelines** checkbox in the +variable group configuration. + +### Run the Terraform pipeline + +In your [Azure DevOps](https://dev.azure.com) project create a new build +pipeline referring to the +[environment_setup/terraform-pipeline.yml](environment_setup/terraform-pipeline.yml) +pipeline definition in your forked repository. + +Save and run the pipeline. This will deploy the environment using Terraform, creating a resource group named `rg-[BASE_NAME]-test-main` containing the following resources: + +* A Machine Learning workspace named `aml-[BASE_NAME]-test` for managing the AML pipeline +* A Container Registry named `acr[BASE_NAME]test`, required to provision the Azure Machine Learning workspace +* A Key Vault named `kv-[BASE_NAME]-test`, required to provision the Azure Machine Learning workspace +* A Storage account named `st[BASE_NAME]test`, required to provision the Azure Machine Learning workspace, used for storing the output of the AML pipeline +* An Application Insights instance named `appinsights-[BASE_NAME]-test`, required to provision the Azure Machine Learning workspace +* An Azure Databricks workspace named `dbricks[BASE_NAME]test`, used for running the data engineering notebook +* A Storage account named `st[BASE_NAME]trtest`, where Terraform has copied a training dataset file [diabetes.csv](./environment_setup/terraform/training-data/diabetes.csv). + +**Note:** + +The Terraform pipeline only runs Terraform if it is run on the `master` branch. +If running from another branch, set the variable `RUN_FLAG_TERRAFORM` to the +value `true` at queue time. + +### Create a Registry Service Connection + +[Create a service connection](https://docs.microsoft.com/en-us/azure/devops/pipelines/library/service-endpoints?view=azure-devops&tabs=yaml#sep-docreg) to your Azure Container Registry: +- As *Connection type*, select *Docker Registry* +- As *Registry type*, select *Azure Container Registry* +- As *Azure container registry*, select your Container registry instance (deployed by Terraform) +- As *Service connection name*, enter `DataOpsML Azure Container Registry` + +### Create a container build pipeline + +In your [Azure DevOps](https://dev.azure.com) project create a new build +pipeline referring to the +[docker-image-pipeline.yml](environment_setup/docker-image-pipeline.yml) +pipeline definition in your forked repository. + +Save and run the pipeline. This will build and push a container image to your Azure Container Registry. + +### Create an Azure DevOps Azure ML Workspace Service Connection + +Create a service connection to your ML workspace via the [Azure DevOps Azure ML task instructions](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml) to be able to execute the Azure ML training pipeline. Name the connection `DataOpsML Azure ML Workspace` (this name is used in the variable `WORKSPACE_SVC_CONNECTION` in [azure-pipelines.yml](azure-pipelines.yml)). + +**Note:** Creating service connection with Azure Machine Learning workspace scope requires 'Owner' or 'User Access Administrator' permissions on the Workspace. +You must also have sufficient permissions to register an application with +your Azure AD tenant, or receive the ID and secret of a service principal +from your Azure AD Administrator. That principal must have Contributor +permissions on the Azure ML Workspace. + +### Set up MLOps Pipeline + +Now that you have all the required resources created from the IaC pipeline, +you can set up the Azure DeveOps pipeline that will run the Azure ML pipeline. + +### Set up the Pipeline + +In your [Azure DevOps](https://dev.azure.com) project create and run a new build +pipeline referring to the +[azure-pipelines.yml](azure-pipelines.yml) +pipeline definition in your forked repository. + +After the pipeline has run, you can navigate in the Azure Portal to the Azure ML Workspace +and visualize the Azure ML pipeline result. + +![ML lifecycle](docs/images/azureml-pipeline.png) + +The pipeline output shows a reference to the storage account location where the output +data is stored. You can navigate to that dataset in the Azure Portal. + +![ML lifecycle](docs/images/output-dataset.png) diff --git a/Python/azureml-pipeline-databricks/azure-pipelines.yml b/Python/azureml-pipeline-databricks/azure-pipelines.yml new file mode 100644 index 0000000..a2931bc --- /dev/null +++ b/Python/azureml-pipeline-databricks/azure-pipelines.yml @@ -0,0 +1,115 @@ +name: ML-Pipeline + +resources: + containers: + - container: mlops + endpoint: DataOpsML Azure Container Registry + image: azureml-pipeline-databricks + +pr: none +trigger: + branches: + include: + - master + paths: + include: + - Python/azureml-pipeline-databricks/ + +variables: +- group: terraform +- template: variables.yml + +pool: + vmImage: ubuntu-latest + +stages: + +- stage: AML_Pipeline_CI + displayName: Azure ML CI + jobs: + - job: ReadTerraform + displayName: Use Terraform outputs + steps: + + - template: environment_setup/terraform-init-template.yml + parameters: + TerraformDirectory: Python/azureml-pipeline-databricks/environment_setup/terraform + environment: test + + - template: environment_setup/terraform-outputs-template.yml + parameters: + TerraformDirectory: Python/azureml-pipeline-databricks/environment_setup/terraform + + - job: Build_Pipeline + dependsOn: ReadTerraform + displayName: Build ML Training Pipeline + container: mlops + timeoutInMinutes: 0 + variables: + SUBSCRIPTION_ID: $[ dependencies.ReadTerraform.outputs['Outputs.subscription_id'] ] + RESOURCE_GROUP: $[ dependencies.ReadTerraform.outputs['Outputs.resource_group_name'] ] + AML_WORKSPACE_NAME: $[ dependencies.ReadTerraform.outputs['Outputs.aml_workspace_name'] ] + DATABRICKS_WORKSPACE_NAME: $[ dependencies.ReadTerraform.outputs['Outputs.databricks_workspace_name'] ] + TRAINING_DATA_ACCOUNT_NAME: $[ dependencies.ReadTerraform.outputs['Outputs.training_data_account_name'] ] + + steps: + - script: | + flake8 --output-file=lint-testresults.xml --format junit-xml + displayName: 'Run lint tests' + workingDirectory: Python/azureml-pipeline-databricks + + - script: | + python -m pytest . --cov=code --cov-report=html --cov-report=xml --junitxml=unit-testresults.xml + condition: succeededOrFailed() + displayName: 'Run unit tests' + workingDirectory: Python/azureml-pipeline-databricks + + - task: PublishTestResults@2 + condition: succeededOrFailed() + inputs: + testResultsFiles: '**/*-testresults.xml' + testRunTitle: 'Linting & Unit tests' + failTaskOnFailedTests: true + displayName: 'Publish test results' + + - task: PublishCodeCoverageResults@1 + displayName: 'Publish coverage report' + condition: succeededOrFailed() + inputs: + codeCoverageTool: Cobertura + summaryFileLocation: '**/coverage.xml' + reportDirectory: '**/htmlcov' + failIfCoverageEmpty: true + + - task: AzureCLI@1 + name: TrainPipeline + inputs: + azureSubscription: '$(ARM_SVC_CONNECTION)' + scriptLocation: inlineScript + workingDirectory: Python/azureml-pipeline-databricks + inlineScript: | + set -e # fail on error + + # Set up environment and build AML pipeline. + # Also sets the AMLPIPELINE_ID job variable. + python -m ml_service.build_ml_pipeline + + displayName: 'Publish Azure Machine Learning Pipeline' + + - job: "Run_ML_Pipeline" + dependsOn: Build_Pipeline + displayName: "Trigger ML Training Pipeline" + pool: server + variables: + SUBSCRIPTION_ID: $[ dependencies.ReadTerraform.outputs['Outputs.subscription_id'] ] + RESOURCE_GROUP: $[ dependencies.ReadTerraform.outputs['Outputs.resource_group_name'] ] + AML_WORKSPACE_NAME: $[ dependencies.ReadTerraform.outputs['Outputs.aml_workspace_name'] ] + DATABRICKS_WORKSPACE_NAME: $[ dependencies.ReadTerraform.outputs['Outputs.databricks_workspace_name'] ] + AMLPIPELINE_ID: $[ dependencies.Build_Pipeline.outputs['TrainPipeline.AMLPIPELINE_ID'] ] + steps: + - task: ms-air-aiagility.vss-services-azureml.azureml-restApi-task.MLPublishedPipelineRestAPITask@0 + displayName: 'Invoke ML pipeline' + inputs: + azureSubscription: '$(WORKSPACE_SVC_CONNECTION)' + PipelineId: '$(AMLPIPELINE_ID)' + ExperimentName: '$(EXPERIMENT_NAME)' diff --git a/Python/azureml-pipeline-databricks/ci_dependencies.yml b/Python/azureml-pipeline-databricks/ci_dependencies.yml new file mode 100644 index 0000000..d40271b --- /dev/null +++ b/Python/azureml-pipeline-databricks/ci_dependencies.yml @@ -0,0 +1,33 @@ +name: dataops_ci + +dependencies: + + # The python interpreter version. +- python=3.7.5 + +- psutil=5.6 #latest + +- pip=20.0.2 +- pip: + + # dependencies shared with other environment .yml files. + - azureml-sdk==1.0.85 + + # Additional pip dependencies for the CI environment. + - databricks-client==0.0.2 + - databricks-test==0.0.1 + - pytest==5.3.1 + - pytest-mock==1.13.0 + - pytest-cov==2.8.1 + - requests==2.22.0 + - python-dotenv==0.10.3 + - flake8==3.7.9 + - flake8_formatter_junit_xml==0.0.6 + - azure-cli==2.1.0 + - tox==3.14.3 + # required for azure.common.credentials.get_azure_cli_credentials + - azure-core==1.2.2 + # Runtime modules for Spark - match Databricks runtime 6.2 + - pyspark==2.4.4 + - pandas==0.24.2 + - pyarrow==0.13.0 diff --git a/Python/azureml-pipeline-databricks/code/prepare/feature_engineering.py b/Python/azureml-pipeline-databricks/code/prepare/feature_engineering.py new file mode 100644 index 0000000..63acda3 --- /dev/null +++ b/Python/azureml-pipeline-databricks/code/prepare/feature_engineering.py @@ -0,0 +1,95 @@ +# Databricks notebook source +# This notebook processed the training dataset (imported by Data Factory) +# and computes a cleaned dataset with additional feature + +# COMMAND ---------- + +# Instrument for unit tests. This is only executed in local unit tests, not in Databricks. +if 'dbutils' not in locals(): + import databricks_test + databricks_test.inject_variables() + +# COMMAND ---------- + +# Widgets for interactive development. Values are automatically passed when run in Azure ML. +# In interactive development, use Databricks CLI to provision the proper secrets. + +# Data input: training data + +dbutils.widgets.text("secretscope", "amlscope") +dbutils.widgets.text( + "training", "wasbs://trainingdata@stmyappdataopstrstage.blob.core.windows.net/" +) +dbutils.widgets.text( + "training_blob_config", + "fs.azure.account.key.stmyappdataopstrstage.blob.core.windows.net", +) +dbutils.widgets.text( + "training_blob_secretname", "trainingdata@stmyappdataopstrstage" +) + +# Data output: feature engineered data + +dbutils.widgets.text( + "feature_engineered", "wasbs://feature_engineereddata@stmyappdataopstrstage.blob.core.windows.net/" +) +dbutils.widgets.text( + "feature_engineered_blob_config", + "fs.azure.account.key.MYACCOUNT.blob.core.windows.net" +) +dbutils.widgets.text( + "feature_engineered_blob_secretname", "MYCONTAINER@MYACCOUNT" +) + +# COMMAND ---------- + +# Connect to Azure ML +dbutils.library.installPyPI( + "azureml-sdk", + version="1.0.85", + extras="databricks") +from azureml.core import Run +# In an Azure ML run, settings get imported from passed --AZUREML_* parameters +run = Run.get_context(allow_offline=True) + +# COMMAND ---------- + +# Set up storage credentials + +spark.conf.set( + dbutils.widgets.get("training_blob_config"), + dbutils.secrets.get( + scope=dbutils.widgets.get("secretscope"), + key=dbutils.widgets.get("training_blob_secretname") + ), +) + +spark.conf.set( + dbutils.widgets.get("feature_engineered_blob_config"), + dbutils.secrets.get( + scope=dbutils.widgets.get("secretscope"), + key=dbutils.widgets.get("feature_engineered_blob_secretname") + ), +) + +# COMMAND ---------- + +rawdata = ( + spark.read.format("csv") + .options(header="true", mode="FAILFAST") + .load(dbutils.widgets.get('training')) +) + +# COMMAND ---------- + +# Basic data cleanup - remove rows with missing data +cleandata = rawdata.na.drop() + +# COMMAND ---------- + +# Write data to CSV + +dest_path_csv = dbutils.widgets.get('feature_engineered') +cleandata.toPandas().to_csv("/tmp/output.csv", index=False) +csv_file = "%s/engineered.csv" % (dest_path_csv) +assert dbutils.fs.cp("file:/tmp/output.csv", csv_file) diff --git a/Python/azureml-pipeline-databricks/code/tests/diabetes_missing_values.csv b/Python/azureml-pipeline-databricks/code/tests/diabetes_missing_values.csv new file mode 100644 index 0000000..287fac0 --- /dev/null +++ b/Python/azureml-pipeline-databricks/code/tests/diabetes_missing_values.csv @@ -0,0 +1,443 @@ +AGE,SEX,BMI,BP,S1,S2,S3,S4,S5,S6,Y +0.0380759064334241,,0.0616962065186885,0.0218723549949558,-0.0442234984244464,-0.0348207628376986,-0.0434008456520269,-0.00259226199818282,0.0199084208763183,-0.0176461251598052,151.0 +,-0.044641636506989,-0.0514740612388061,-0.0263278347173518,-0.00844872411121698,-0.019163339748222,0.0744115640787594,-0.0394933828740919,-0.0683297436244215,-0.09220404962683,75.0 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+0.0417084448844436,0.0506801187398187,-0.0159062628007364,0.0172818607481171,-0.0373437341334407,-0.0138398158977999,-0.0249926566315915,-0.0110795197996419,-0.0468794828442166,0.0154907301588724,132.0 +-0.0454724779400257,-0.044641636506989,0.0390621529671896,0.00121513083253827,0.0163184273364034,0.0152829910486266,-0.0286742944356786,0.0265596234937854,0.0445283740214053,-0.0259303389894746,220.0 +-0.0454724779400257,-0.044641636506989,-0.0730303027164241,-0.081413765817132,0.0837401173882587,0.0278089295202079,0.17381578478911,-0.0394933828740919,-0.00421985970694603,0.00306440941436832,57.0 diff --git a/Python/azureml-pipeline-databricks/code/tests/feature_engineering_test.py b/Python/azureml-pipeline-databricks/code/tests/feature_engineering_test.py new file mode 100644 index 0000000..ddf753e --- /dev/null +++ b/Python/azureml-pipeline-databricks/code/tests/feature_engineering_test.py @@ -0,0 +1,42 @@ +import pandas as pd +from tempfile import TemporaryDirectory +import databricks_test +from pandas.testing import assert_frame_equal + + +def test_feature_engineering(mocker): + mocker.patch("azureml.core.Run") + + with databricks_test.session() as dbrickstest, \ + TemporaryDirectory() as out_dir: + + # Provide input and output location as widgets to notebook + switcher = { + "training": "code/tests/diabetes_missing_values.csv", + "feature_engineered": out_dir, + } + dbrickstest.dbutils.widgets.get = lambda x: switcher.get(x, "") + + capture_files = {} + + def mock_cp(src, dst, capture_files=capture_files): + prefix = "file:" + assert src.startswith(prefix) + capture_files[dst] = pd.read_csv(src) + return True + + dbrickstest.dbutils.fs.cp.side_effect = mock_cp + + # Run notebook + dbrickstest.run_notebook("./code/prepare", "feature_engineering") + + expected_name = "engineered.csv" + expected_file = "%s/%s" % (out_dir, expected_name) + assert expected_file in capture_files + resultDF = capture_files[expected_file] + + # Compare produced and expected CSV files + expectedDF = pd.read_csv( + "code/tests/feature_engineering_expected.csv") + assert_frame_equal( + expectedDF, resultDF, check_dtype=False, check_categorical=False) diff --git a/Python/azureml-pipeline-databricks/docs/images/azureml-pipeline.png b/Python/azureml-pipeline-databricks/docs/images/azureml-pipeline.png new file mode 100644 index 0000000..1dac3e8 Binary files /dev/null and b/Python/azureml-pipeline-databricks/docs/images/azureml-pipeline.png differ diff --git a/Python/azureml-pipeline-databricks/docs/images/output-dataset.png b/Python/azureml-pipeline-databricks/docs/images/output-dataset.png new file mode 100644 index 0000000..2d38d7e Binary files /dev/null and b/Python/azureml-pipeline-databricks/docs/images/output-dataset.png differ diff --git a/Python/azureml-pipeline-databricks/environment_setup/Dockerfile b/Python/azureml-pipeline-databricks/environment_setup/Dockerfile new file mode 100644 index 0000000..87ae90a --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/Dockerfile @@ -0,0 +1,28 @@ +FROM conda/miniconda3 + +ARG SPARK_VERSION=2.4.3 +ARG HADOOP_VERSION=2.7 + +# Install Spark runtime +WORKDIR / +RUN apt-get update \ + && apt-get install -y \ + wget \ + apt-transport-https \ + openjdk-8-jdk \ + && wget -q https://archive.apache.org/dist/spark/spark-${SPARK_VERSION}/spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz \ + && tar -zxf spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz \ + && rm spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz + +ENV SPARK_HOME /spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION} + +COPY ci_dependencies.yml /setup/ + +# activate Conda environment +ENV PATH /usr/local/envs/dataops_ci/bin:$PATH + +RUN conda update -n base -c defaults conda && \ + conda env create -f /setup/ci_dependencies.yml && \ + /bin/bash -c "source activate dataops_ci" && \ + az --version && \ + chmod -R 777 /usr/local/envs/dataops_ci/lib diff --git a/Python/azureml-pipeline-databricks/environment_setup/docker-image-pipeline.yml b/Python/azureml-pipeline-databricks/environment_setup/docker-image-pipeline.yml new file mode 100644 index 0000000..b194a13 --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/docker-image-pipeline.yml @@ -0,0 +1,33 @@ +name: docker-image + +# Pipeline that builds and pushes the microsoft/mlopspython image. +resources: +- repo: self + +pool: + vmImage: 'ubuntu-latest' + +trigger: + branches: + include: + - master + + paths: + include: + - Python/azureml-pipeline-databricks/environment_setup/Dockerfile + +variables: +- template: ../variables.yml + +steps: + - task: Docker@2 + displayName: Build and Push + inputs: + command: buildAndPush + containerRegistry: '$(REGISTRY_SVC_CONNECTION)' + repository: '$(AGENT_IMAGE_NAME)' + tags: | + $(Build.BuildNumber) + latest + buildContext: Python/azureml-pipeline-databricks + dockerFile: Python/azureml-pipeline-databricks/environment_setup/Dockerfile diff --git a/Python/azureml-pipeline-databricks/environment_setup/install_requirements.sh b/Python/azureml-pipeline-databricks/environment_setup/install_requirements.sh new file mode 100755 index 0000000..fc037f6 --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/install_requirements.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +set -eux + +conda env create -f diabetes_regression/ci_dependencies.yml + +conda activate mlopspython_ci diff --git a/Python/azureml-pipeline-databricks/environment_setup/terraform-init-template.yml b/Python/azureml-pipeline-databricks/environment_setup/terraform-init-template.yml new file mode 100644 index 0000000..f685a06 --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/terraform-init-template.yml @@ -0,0 +1,52 @@ +parameters: + environment: test + TerraformVersion: 0.12.21 + TerraformDirectory: + TerraformBackendServiceConnection: $(ARM_SVC_CONNECTION) + TerraformEnvironmentServiceConnection: $(ARM_SVC_CONNECTION) + TerraformBackendResourceGroup: $(TERRAFORM_BACKEND_RG) + TerraformBackendStorageAccount: $(TERRAFORM_BACKEND_STORAGE) + TerraformBackendStorageContainer: terraformstate + +steps: + +- task: ms-devlabs.custom-terraform-tasks.custom-terraform-installer-task.TerraformInstaller@0 + displayName: Install Terraform + inputs: + terraformVersion: ${{ parameters.TerraformVersion }} + +- task: AzureCLI@1 + displayName: Terraform credentials + inputs: + azureSubscription: ${{ parameters.TerraformEnvironmentServiceConnection }} + scriptLocation: inlineScript + inlineScript: | + set -eu + subscriptionId=$(az account show --query id -o tsv) + echo "##vso[task.setvariable variable=ARM_CLIENT_ID]$servicePrincipalId" + echo "##vso[task.setvariable variable=ARM_CLIENT_SECRET;issecret=true]$servicePrincipalKey" + echo "##vso[task.setvariable variable=ARM_SUBSCRIPTION_ID]$subscriptionId" + echo "##vso[task.setvariable variable=ARM_TENANT_ID]$tenantId" + addSpnToEnvironment: true + +# Using bash instead of Terraform extension because of following issue: +# - https://github.com/microsoft/azure-pipelines-extensions/issues/738 +- task: AzureCLI@1 + displayName: Terraform init + inputs: + azureSubscription: ${{ parameters.TerraformBackendServiceConnection }} + scriptLocation: inlineScript + inlineScript: | + set -eux # fail on error + subscriptionId=$(az account show --query id -o tsv) + terraform init \ + -backend-config=storage_account_name=${{ parameters.TerraformBackendStorageAccount }} \ + -backend-config=container_name=${{ parameters.TerraformBackendStorageContainer }} \ + -backend-config=key=${{ parameters.environment }}.tfstate \ + -backend-config=resource_group_name=${{ parameters.TerraformBackendResourceGroup }} \ + -backend-config=subscription_id=$subscriptionId \ + -backend-config=tenant_id=$tenantId \ + -backend-config=client_id=$servicePrincipalId \ + -backend-config=client_secret="$servicePrincipalKey" + workingDirectory: ${{ parameters.TerraformDirectory }} + addSpnToEnvironment: true diff --git a/Python/azureml-pipeline-databricks/environment_setup/terraform-outputs-template.yml b/Python/azureml-pipeline-databricks/environment_setup/terraform-outputs-template.yml new file mode 100644 index 0000000..e9b8121 --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/terraform-outputs-template.yml @@ -0,0 +1,29 @@ +parameters: + TerraformDirectory: + +steps: +- bash: | + set -eu + + echo "Setting job variables from Terraform outputs:" + terraform output -json | jq -r ' + . as $in + | keys[] + | ["- " + .] + | @tsv' + + terraform output -json | jq -r ' + . as $in + | keys[] + | ($in[.].value | tostring) as $value + | ($in[.].sensitive | tostring) as $sensitive + | [ + "- " + . + ": " + if $in[.].sensitive then "(sensitive)" else $value end, # output name to console + "##vso[task.setvariable variable=" + . + ";isSecret=" + $sensitive + "]" + $value, # set as ADO task variable + "##vso[task.setvariable variable=" + . + ";isOutput=true;isSecret=" + $sensitive + "]" + $value # also set as ADO job variable + ] + | .[]' + + name: Outputs + displayName: Read Terraform outputs + workingDirectory: ${{ parameters.TerraformDirectory }} diff --git a/Python/azureml-pipeline-databricks/environment_setup/terraform-pipeline.yml b/Python/azureml-pipeline-databricks/environment_setup/terraform-pipeline.yml new file mode 100644 index 0000000..463a703 --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/terraform-pipeline.yml @@ -0,0 +1,20 @@ +name: terraform + +pr: none +trigger: none + +variables: +- group: terraform +- template: ../variables.yml + +pool: + vmImage: ubuntu-latest + +stages: + +- template: terraform-stages-template.yml + parameters: + environment: test + TerraformEnvVariables: + TF_VAR_appname: $(BASE_NAME) + TF_VAR_location: $(LOCATION) diff --git a/Python/azureml-pipeline-databricks/environment_setup/terraform-stages-template.yml b/Python/azureml-pipeline-databricks/environment_setup/terraform-stages-template.yml new file mode 100644 index 0000000..0be70f9 --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/terraform-stages-template.yml @@ -0,0 +1,34 @@ +parameters: + environment: test + TerraformArguments: '' + TerraformEnvVariables: + +stages: +- stage: Terraform + pool: + vmImage: ubuntu-latest + jobs: + + - job: Terraform + displayName: Terraform + # Avoid concurrent Terraform runs on PRs, which would result in failures due to exclusive lock on remote state file. + condition: and(succeeded(), or(eq(variables['Build.SourceBranch'], 'refs/heads/master'), variables['RUN_FLAG_TERRAFORM'])) + steps: + + - template: terraform-init-template.yml + parameters: + TerraformDirectory: Python/azureml-pipeline-databricks/environment_setup/terraform + environment: ${{ parameters.environment }} + + # Using bash instead of Terraform extension because of following issues: + # - https://github.com/microsoft/azure-pipelines-extensions/issues/748 + # - https://github.com/microsoft/azure-pipelines-extensions/issues/725 + # - https://github.com/microsoft/azure-pipelines-extensions/issues/747 + - bash: | + set -eu + export ARM_CLIENT_SECRET=$(ARM_CLIENT_SECRET) + terraform apply -input=false -auto-approve -var environment=${{ parameters.environment }} ${{ parameters.TerraformArguments }} + displayName: Terraform apply + workingDirectory: Python/azureml-pipeline-databricks/environment_setup/terraform + env: + ${{ parameters.TerraformEnvVariables }} diff --git a/Python/azureml-pipeline-databricks/environment_setup/terraform/.artifactignore b/Python/azureml-pipeline-databricks/environment_setup/terraform/.artifactignore new file mode 100644 index 0000000..1944fd6 --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/terraform/.artifactignore @@ -0,0 +1 @@ +*.tmp diff --git a/Python/azureml-pipeline-databricks/environment_setup/terraform/azureml/main.tf b/Python/azureml-pipeline-databricks/environment_setup/terraform/azureml/main.tf new file mode 100644 index 0000000..78c4fae --- /dev/null +++ b/Python/azureml-pipeline-databricks/environment_setup/terraform/azureml/main.tf @@ -0,0 +1,113 @@ +# Storage Account + +resource "azurerm_storage_account" "aml" { + name = "st${var.appname}${var.environment}" + resource_group_name = var.resource_group_name + location = var.location + account_tier = "Standard" + account_replication_type = "LRS" +} + +# Key Vault + +resource "azurerm_key_vault" "aml" { + name = "kv-${var.appname}-${var.environment}" + location = var.location + resource_group_name = var.resource_group_name + tenant_id = var.tenant_id + + sku_name = "standard" +} + +# Application Insights + +resource "azurerm_application_insights" "aml" { + name = "appinsights-${var.appname}-${var.environment}" + resource_group_name = var.resource_group_name + location = var.location + application_type = "web" +} + +# Container Registry + +resource "azurerm_container_registry" "aml" { + name = "acr${var.appname}${var.environment}" + resource_group_name = var.resource_group_name + location = var.location + sku = "Standard" + admin_enabled = true +} + + +# Azure ML Workspace + +resource "azurerm_template_deployment" "aml" { + name = "aml-${var.appname}-${var.environment}-deploy" + resource_group_name = var.resource_group_name + + template_body = <