mlflow.fastai

The mlflow.fastai module provides an API for logging and loading fast.ai models. This module exports fast.ai models with the following flavors:

fastai (native) format

This is the main flavor that can be loaded back into fastai.

mlflow.pyfunc

Produced for use by generic pyfunc-based deployment tools and batch inference.

mlflow.fastai.autolog()[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Enable automatic logging from Fastai to MLflow. Logs loss and any other metrics specified in the fit function, and optimizer data as parameters. Model checkpoints are logged as artifacts to a ‘models’ directory.

MLflow will also log the parameters of the EarlyStopping and OneCycleScheduler callbacks

mlflow.fastai.get_default_conda_env(include_cloudpickle=False)[source]
Returns

The default Conda environment for MLflow Models produced by calls to save_model() and log_model().

mlflow.fastai.load_model(model_uri)[source]

Load a fastai model from a local file or a run.

Parameters

model_uri

The location, in URI format, of the MLflow model. For example:

  • /Users/me/path/to/local/model

  • relative/path/to/local/model

  • s3://my_bucket/path/to/model

  • runs:/<mlflow_run_id>/run-relative/path/to/model

For more information about supported URI schemes, see Referencing Artifacts.

Returns

A fastai model (an instance of fastai.Learner).

mlflow.fastai.log_model(fastai_learner, artifact_path, conda_env=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = None, **kwargs)[source]

Log a fastai model as an MLflow artifact for the current run.

Parameters
  • fastai_learner – Fastai model (an instance of fastai.Learner) to be saved.

  • artifact_path – Run-relative artifact path.

  • conda_env

    Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default get_default_conda_env() environment is added to the model. The following is an example dictionary representation of a Conda environment:

    {
        'name': 'mlflow-env',
        'channels': ['defaults'],
        'dependencies': [
            'python=3.7.0',
            'fastai=1.0.60',
        ]
    }
    

  • registered_model_name – Note:: Experimental: This argument may change or be removed in a future release without warning. If given, create a model version under registered_model_name, also creating a registered model if one with the given name does not exist.

  • signature

    (Experimental) ModelSignature describes model input and output Schema. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:

    from mlflow.models.signature import infer_signature
    train = df.drop_column("target_label")
    predictions = ... # compute model predictions
    signature = infer_signature(train, predictions)
    

  • input_example – (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.

  • kwargs – kwargs to pass to fastai.Learner.export method.

mlflow.fastai.save_model(fastai_learner, path, conda_env=None, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = None, **kwargs)[source]

Save a fastai Learner to a path on the local file system.

Parameters
  • fastai_learner – fastai Learner to be saved.

  • path – Local path where the model is to be saved.

  • conda_env

    Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default get_default_conda_env() environment is added to the model. The following is an example dictionary representation of a Conda environment:

    {
        'name': 'mlflow-env',
        'channels': ['defaults'],
        'dependencies': [
            'python=3.7.0',
            'fastai=1.0.60',
        ]
    }
    

  • mlflow_model – MLflow model config this flavor is being added to.

  • signature

    (Experimental) ModelSignature describes model input and output Schema. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:

    from mlflow.models.signature import infer_signature
    train = df.drop_column("target_label")
    predictions = ... # compute model predictions
    signature = infer_signature(train, predictions)
    

  • input_example – (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.

  • kwargs – kwargs to pass to Learner.save method.