g-training-pipeline 0.1.0

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Training Pipeline

Check out this Medium article for more details about this module.

Install for Development

~/energy-forecasting                   $ cd training-pipeline && rm poetry.lock
~/energy-forecasting/training-pipeline $ bash ../scripts/devops/virtual_environment/poetry_install.sh
~/energy-forecasting/training-pipeline $ source .venv/bin/activate

Check the Set Up Additional Tools and Usage sections to see how to set up the additional tools and credentials you need to run this project.

Create Environment File

~/energy-forecasting/training-pipeline $ cp .env.default .env

The command cp .env.default .env is used to create a copy of the .env.default file and name it .env. In many projects, the .env file is used to store environment variables that the application needs to run. The .env.default file is usually a template that includes all the environment variables that the application expects, but with default values. By copying it to .env, you can customize these values for your own environment.

Set Up the ML_PIPELINE_ROOT_DIR Variable

~/energy-forecasting/training-pipeline $ export ML_PIPELINE_ROOT_DIR=$(pwd)

The command export ML_PIPELINE_ROOT_DIR=$(pwd) is setting the value of the ML_PIPELINE_ROOT_DIR environment variable to the current directory. In this context, $(pwd) is a command substitution that gets replaced with the output of the pwd command, which prints the path of the current directory. The export command then makes this variable available to child processes of the current shell.

In essence, ML_PIPELINE_ROOT_DIR is an environment variable that is set to the path of the current directory. This can be useful for scripts or programs that need to reference the root directory of the ML pipeline, as they can simply refer to ML_PIPELINE_ROOT_DIR instead of needing to know the exact path.

Usage for Development

Run the scripts in the following order:

1. Start the hyperparameter tuning script:

   ~/energy-forecasting/training-pipeline $ python -m training_pipeline.hyperparameter_tuning
   

2. Upload the best config based on the previous hyperparameter tuning step:

   ~/energy-forecasting/training-pipeline $ python -m training_pipeline.best_config
   

3. Start the training script using the best configuration uploaded one step before:

   ~/energy-forecasting/training-pipeline $ python -m training_pipeline.train
   

NOTE: Be careful to set the ML_PIPELINE_ROOT_DIR variable as explain in this section.