examples/pytorch/torchscript/IrisClassification/README.md
This example demonstrates training a classification model on the Iris dataset, scripting the model with TorchScript, logging the
scripted model to MLflow using
mlflow.pytorch.log_model, and
loading it back for inference using
mlflow.pytorch.load_model
To run the example via MLflow, navigate to the mlflow/examples/pytorch/torchscript/IrisClassification directory and run the command
mlflow run .
This will run iris_classification.py with the default set of parameters such as --max_epochs=5. You can see the default value in the MLproject file.
In order to run the file with custom parameters, run the command
mlflow run . -P epochs=X
where X is your desired value for epochs.
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 server
and navigating to http://localhost:5000.
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