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(log_models=True, disable=False, exclusive=False, disable_for_unsupported_versions=False, silent=False, registered_model_name=None)[source]

Note

Autologging is known to be compatible with the following package versions: 2.3.0 <= keras <= 2.10.0. Autologging may not succeed when used with package versions outside of this range.

Enables (or disables) and configures autologging from Keras to MLflow. Autologging captures the following information:

Metrics and Parameters

Training loss; validation loss; user-specified metrics
Metrics associated with the EarlyStopping callbacks: stopped_epoch, restored_epoch, restore_best_weight, last_epoch, etc
fit() or fit_generator() parameters; optimizer name; learning rate; epsilon
fit() or fit_generator() parameters associated with EarlyStopping: min_delta, patience, baseline, restore_best_weights, etc

Artifacts

Model summary on training start
MLflow Model (Keras model) on training end

Example

import mlflow
import mlflow.keras
# Build, compile, enable autologging, and train your model
keras_model = ...
keras_model.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
# autolog your metrics, parameters, and model
mlflow.keras.autolog()
results = keras_model.fit(
    x_train, y_train, epochs=20, batch_size=128, validation_data=(x_val, y_val))

EarlyStopping Integration with Keras AutoLogging

MLflow will detect if an EarlyStopping callback is used in a fit() or fit_generator() call, and if the restore_best_weights parameter is set to be True, 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 metric restored_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 be False, then MLflow will not log an additional step.

Regardless of restore_best_weights, MLflow will also log stopped_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 as 0.

MLflow will also log the parameters of the EarlyStopping callback, excluding mode and verbose.

Parameters

log_models – If True, trained models are logged as MLflow model artifacts. If False, trained models are not logged.
disable – If True, disables the Keras autologging integration. If False, enables the Keras autologging integration.
exclusive – If True, autologged content is not logged to user-created fluent runs. If False, autologged content is logged to the active fluent run, which may be user-created.
disable_for_unsupported_versions – If True, disable autologging for versions of keras that have not been tested against this version of the MLflow client or are incompatible.
silent – If True, suppress all event logs and warnings from MLflow during Keras autologging. If False, show all events and warnings during Keras autologging.
registered_model_name – If given, each time a model is trained, it is registered as a new model version of the registered model with this name. The registered model is created if it does not already exist.

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() and log_model().

mlflow.keras.get_default_pip_requirements(include_cloudpickle=False, keras_module=None)[source]

Returns: A list of default pip requirements for MLflow Models produced by this flavor. Calls to save_model() and log_model() produce a pip environment that, at minimum, contains these requirements.

mlflow.keras.load_model(model_uri, dst_path=None, **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.
dst_path – The local filesystem path to which to download the model artifact. This directory must already exist. If unspecified, a local output path will be created.

Returns

A Keras model instance.

Example

# 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, code_paths=None, custom_objects=None, keras_module=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=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(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.7.0",
        {
            "pip": [
                "keras==x.y.z"
            ],
        },
    ],
}
```
code_paths – A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded.
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 with mlflow.keras.load_model() and mlflow.pyfunc.load_model().
keras_module – Keras module to be used to save / load the model (keras or tf.keras). If not provided, MLflow will attempt to infer the Keras module based on the given model.
registered_model_name – 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 –
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 – 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 can be a Pandas DataFrame where the given example will be serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded.
await_registration_for – Number of seconds to wait for the model version to finish being created and is in READY status. By default, the function waits for five minutes. Specify 0 or None to skip waiting.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["keras", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
kwargs – kwargs to pass to keras_model.save method.

Returns

A ModelInfo instance that contains the metadata of the logged model.

Example

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, code_paths=None, mlflow_model=None, custom_objects=None, keras_module=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix] = None, pip_requirements=None, extra_pip_requirements=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 describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.7.0",
        {
            "pip": [
                "keras==x.y.z"
            ],
        },
    ],
}
```
code_paths – A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded.
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 with mlflow.keras.load_model() and mlflow.pyfunc.load_model().
keras_module – Keras module to be used to save / load the model (keras or tf.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.
signature –
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 – 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 can be a Pandas DataFrame where the given example will be serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["keras", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.

Example

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)