Please be aware that the local environment also needs access to the Azure subscription so you have to have Contributor access on the Azure ML Workspace.
In order to configure the project locally, create a copy of .env.example in the root directory and name it .env. Fill out all missing values and adjust the existing ones to suit your requirements.
Install the Azure CLI. The Azure CLI will be used to log you in interactively.
Create a virtual environment using venv, conda or pyenv-virtualenv.
Here is an example for setting up and activating a venv environment with Python 3:
python3 -mvenv .venv
source .venv/bin/activate
Install the required Python modules in your virtual environment.
pip install -r environment_setup/requirements.txt
To run your local ML pipeline code on Azure ML, run a command such as the following (in bash, all on one line):
export BUILD_BUILDID=$(uuidgen); python ml_service/pipelines/build_train_pipeline.py && python ml_service/pipelines/run_train_pipeline.py
BUILD_BUILDID is a variable used to uniquely identify the ML pipeline between the
build_train_pipeline.py and run_train_pipeline.py scripts. In Azure DevOps it is
set to the current build number. In a local environment, we can use a command such as
uuidgen so set a different random identifier on each run, ensuring there are
no collisions.