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.
autolog
()[source] Note
Experimental: This method may change or be removed in a future release without warning.
Enable automatic logging from Keras 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.
EarlyStopping Integration with Keras Automatic Logging
MLflow will detect if an
EarlyStopping
callback is used in afit()
/fit_generator()
call, and if therestore_best_weights
parameter is set to beTrue
, then MLflow will log the metrics associated with the restored model as a final, extra step. The epoch of the restored model will also be logged as the metricrestored_epoch
. This allows for easy comparison between the actual metrics of the restored model and the metrics of other models.If
restore_best_weights
is set to beFalse
, then MLflow will not log an additional step.Regardless of
restore_best_weights
, MLflow will also logstopped_epoch
, which indicates the epoch at which training stopped due to early stopping.If training does not end due to early stopping, then
stopped_epoch
will be logged as0
.MLflow will also log the parameters of the EarlyStopping callback, excluding
mode
andverbose
.
-
mlflow.keras.
get_default_conda_env
(include_cloudpickle=False, keras_module=None)[source] - Returns
The default Conda environment for MLflow Models produced by calls to
save_model()
andlog_model()
.
-
mlflow.keras.
load_model
(model_uri, **kwargs)[source] 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
models:/<model_name>/<model_version>
models:/<model_name>/<stage>
For more information about supported URI schemes, see Referencing Artifacts.
- Returns
A Keras model instance.
-
mlflow.keras.
log_model
(keras_model, artifact_path, conda_env=None, custom_objects=None, keras_module=None, registered_model_name=None, **kwargs)[source] 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 describes the environment this model should be run in. At minimum, it should specify the dependencies contained in
get_default_conda_env()
. IfNone
, the defaultmlflow.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' ] }
custom_objects – A Keras
custom_objects
dictionary mapping names (strings) to custom classes or functions associated with the Keras model. MLflow saves these custom layers using CloudPickle and restores them automatically when the model is loaded withmlflow.keras.load_model()
andmlflow.pyfunc.load_model()
.keras_module – Keras module to be used to save / load the model (
keras
ortf.keras
). If not provided, MLflow will attempt to infer the Keras module based on the given model.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.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>, custom_objects=None, keras_module=None, **kwargs)[source] 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 decsribes the environment this model should be run in. At minimum, it should specify the dependencies contained in
get_default_conda_env()
. IfNone
, the defaultget_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.
custom_objects – A Keras
custom_objects
dictionary mapping names (strings) to custom classes or functions associated with the Keras model. MLflow saves these custom layers using CloudPickle and restores them automatically when the model is loaded withmlflow.keras.load_model()
andmlflow.pyfunc.load_model()
.keras_module – Keras module to be used to save / load the model (
keras
ortf.keras
). If not provided, MLflow will attempt to infer the Keras module based on the given model.kwargs – kwargs to pass to
keras_model.save
method.
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)