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Repo Details

Environment Setup

  • environment_setup/requirements.txt : It consists of a list of python packages which are needed by the train.py to run successfully on host agent (locally).

  • environment_setup/install_requirements.sh : This script prepares the python environment i.e. install the Azure ML SDK and the packages specified in requirements.txt

  • environment_setup/iac-*.yml, arm-templates : Infrastructure as Code piplines to create and delete required resources along with corresponding arm-templates.

  • environment_setup/Dockerfile : Dockerfile of a build agent containing Python 3.6 and all required packages.

  • environment_setup/docker-image-pipeline.yml : An AzDo pipeline for building and pushing microsoft/mlopspython image.

Pipelines

  • .pipelines/azdo-base-pipeline.yml : a pipeline template used by ci-build-train pipeline and pr-build-train pipelines. It contains steps performing linting, data and unit testing.
  • .pipelines/azdo-ci-build-train.yml : a pipeline triggered when the code is merged into master. It performs linting, data integrity testing, unit testing, building and publishing an ML pipeline.
  • .pipelines/azdo-pr-build-train.yml : a pipeline triggered when a pull request to the master branch is created. It performs linting, data integrity testing and unit testing only.

ML Services

  • ml_service/pipelines/build_train_pipeline.py : builds and publishes an ML training pipeline. It uses Python on ML Compute.
  • ml_service/pipelines/build_train_pipeline_with_r.py : builds and publishes an ML training pipeline. It uses R on ML Compute.
  • ml_service/pipelines/build_train_pipeline_with_r_on_dbricks.py : builds and publishes an ML training pipeline. It uses R on Databricks Compute.
  • ml_service/pipelines/run_train_pipeline.py : invokes a published ML training pipeline (Python on ML Compute) via REST API.
  • ml_service/util : contains common utility functions used to build and publish an ML training pipeline.

Code

  • code/training/train.py : a training step of an ML training pipeline.
  • code/evaluate/evaluate_model.py : an evaluating step of an ML training pipeline which registers a new trained model if evaluation shows the new model is more performant than the previous one.
  • code/evaluate/register_model.py : (LEGACY) registers a new trained model if evaluation shows the new model is more performant than the previous one.
  • code/training/R/r_train.r : training a model with R basing on a sample dataset (weight_data.csv).
  • code/training/R/train_with_r.py : a python wrapper (ML Pipeline Step) invoking R training script on ML Compute
  • code/training/R/train_with_r_on_databricks.py : a python wrapper (ML Pipeline Step) invoking R training script on Databricks Compute
  • code/training/R/weight_data.csv : a sample dataset used by R script (r_train.r) to train a model

Scoring

  • code/scoring/score.py : a scoring script which is about to be packed into a Docker Image along with a model while being deployed to QA/Prod environment.
  • code/scoring/conda_dependencies.yml : contains a list of dependencies required by score.py to be installed in a deployable Docker Image
  • code/scoring/inference_config.yml, deployment_config_aci.yml, deployment_config_aks.yml : configuration files for the AML Model Deploy pipeline task for ACI and AKS deployment targets.