In this example, we train a Pytorch model to predict or classifcy garbage/trash.

To run the example via MLflow, navigate to the garbage-mlflow-tracking directory and run the command

mlflow run .

This will run train.py with the default set of parameters such as --max_epochs=5. You can see the default value in the MLproject file.

If you have the required modules for the file and would like to skip the creation of a conda environment, add the argument --env-manager=local.

mlflow run . --env-manager=local

Once the code is finished executing, you can view the run's metrics, parameters, and details by running the command

mlflow ui

and navigating to http://localhost:5000.

For more details on MLflow tracking, see the docs.

To configure MLflow to log to a custom (non-default) tracking location, set the MLFLOW_TRACKING_URI environment variable, e.g. via export MLFLOW_TRACKING_URI=http://localhost:5000/. For more details, see the docs.