mlflow.keras

The mlflow.keras module provides an API for logging and loading Keras models. This module exports Keras models with the following flavors:

Keras (native) format: This is the main flavor that can be loaded back into Keras.
mlflow.pyfunc: Produced for use by generic pyfunc-based deployment tools and batch inference.

mlflow.keras.get_default_conda_env()

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

mlflow.keras.load_model(model_uri, **kwargs)

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

Extra arguments are passed through to keras.load_model.

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 Keras model instance.

>>> # Load persisted model as a Keras model or as a PyFunc, call predict() on a pandas DataFrame
>>> keras_model = mlflow.keras.load_model("runs:/96771d893a5e46159d9f3b49bf9013e2" + "/models")
>>> predictions = keras_model.predict(x_test)

mlflow.keras.log_model(keras_model, artifact_path, conda_env=None, **kwargs)

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

Parameters:

keras_model – Keras model 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 decribes 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 mlflow.keras.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',
        'keras=2.2.4',
        'tensorflow=1.8.0'
    ]
}
```
kwargs – kwargs to pass to keras_model.save method.

>>> from keras import Dense, layers
>>> import mlflow
>>> # Build, compile, and train your model
>>> keras_model = ...
>>> keras_model.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
>>> results = keras_model.fit(
...     x_train, y_train, epochs=20, batch_size = 128, validation_data=(x_val, y_val))
>>> # Log metrics and log the model
>>> with mlflow.start_run() as run:
>>>   mlflow.keras.log_model(keras_model, "models")

mlflow.keras.save_model(keras_model, path, conda_env=None, mlflow_model=<mlflow.models.Model object>)

Save a Keras model to a path on the local file system.

Parameters:

keras_model – Keras model 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 decribes 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',
        'keras=2.2.4',
        'tensorflow=1.8.0'
    ]
}
```
mlflow_model – MLflow model config this flavor is being added to.

>>> import mlflow
>>> # Build, compile, and train your model
>>> keras_model = ...
>>> keras_model_path = ...
>>> keras_model.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
>>> results = keras_model.fit(
...     x_train, y_train, epochs=20, batch_size = 128, validation_data=(x_val, y_val))
... # Save the model as an MLflow Model
>>> mlflow.keras.save_model(keras_model, keras_model_path)