mlflow.tensorflow

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

TensorFlow (native) format

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

mlflow.pyfunc

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

mlflow.tensorflow.autolog(every_n_iter=1, log_models=True, log_datasets=True, disable=False, exclusive=False, disable_for_unsupported_versions=False, silent=False, registered_model_name=None, log_input_examples=False, log_model_signatures=True, saved_model_kwargs=None, keras_model_kwargs=None, extra_tags=None)[source]

Note

Autologging is known to be compatible with the following package versions: 2.6.5 <= tensorflow <= 2.14.0. Autologging may not succeed when used with package versions outside of this range.

Enables autologging for tf.keras and keras. Note that only tensorflow>=2.3 are supported. As an example, try running the Keras/TensorFlow example.

For each TensorFlow module, autologging captures the following information:

tf.keras

  • Metrics and Parameters

  • Training loss; validation loss; user-specified metrics

  • fit() or fit_generator() parameters; optimizer name; learning rate; epsilon

  • Artifacts

  • Model summary on training start

  • MLflow Model (Keras model)

  • TensorBoard logs on training end

tf.keras.callbacks.EarlyStopping

  • Metrics and Parameters

  • Metrics from the EarlyStopping callbacks: stopped_epoch, restored_epoch, restore_best_weight, etc

  • fit() or fit_generator() parameters associated with EarlyStopping: min_delta, patience, baseline, restore_best_weights, etc

Refer to the autologging tracking documentation for more information on TensorFlow workflows.

Note that autologging cannot be used together with explicit MLflow callback, i.e., mlflow.tensorflow.MLflowCallback, because it will cause the same metrics to be logged twice. If you want to include mlflow.tensorflow.MLflowCallback in the callback list, please turn off autologging by calling mlflow.tensorflow.autolog(disable=True).

Parameters

  • every_n_iter – The frequency with which metrics should be logged. For example, a value of 100 will log metrics at step 0, 100, 200, etc.

  • log_models – If True, trained models are logged as MLflow model artifacts. If False, trained models are not logged.

  • log_datasets – If True, dataset information is logged to MLflow Tracking. If False, dataset information is not logged.

  • disable – If True, disables the TensorFlow autologging integration. If False, enables the TensorFlow integration 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 tensorflow 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 TensorFlow autologging. If False, show all events and warnings during TensorFlow 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.

  • log_input_examples – If True, input examples from training datasets are collected and logged along with tf/keras model artifacts during training. If False, input examples are not logged.

  • log_model_signatures – If True, ModelSignatures describing model inputs and outputs are collected and logged along with tf/keras model artifacts during training. If False, signatures are not logged. Note that logging TensorFlow models with signatures changes their pyfunc inference behavior when Pandas DataFrames are passed to predict(). When a signature is present, an np.ndarray (for single-output models) or a mapping from str -> np.ndarray (for multi-output models) is returned; when a signature is not present, a Pandas DataFrame is returned.

  • saved_model_kwargs – a dict of kwargs to pass to tensorflow.saved_model.save method.

  • keras_model_kwargs – a dict of kwargs to pass to keras_model.save method.

  • extra_tags – A dictionary of extra tags to set on each managed run created by autologging.

mlflow.tensorflow.get_default_conda_env()[source]
Returns

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

mlflow.tensorflow.get_default_pip_requirements(include_cloudpickle=False)[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.tensorflow.load_model(model_uri, dst_path=None, saved_model_kwargs=None, keras_model_kwargs=None)[source]

Load an MLflow model that contains the TensorFlow flavor from the specified path.

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.

  • saved_model_kwargs – kwargs to pass to tensorflow.saved_model.load method. Only available when you are loading a tensorflow2 core model.

  • keras_model_kwargs – kwargs to pass to keras.models.load_model method. Only available when you are loading a Keras model.

Returns

A callable graph (tf.function) that takes inputs and returns inferences.

Example 
import mlflow
import tensorflow as tf

tf_graph = tf.Graph()
tf_sess = tf.Session(graph=tf_graph)
with tf_graph.as_default():
    signature_definition = mlflow.tensorflow.load_model(
        model_uri="model_uri", tf_sess=tf_sess
    )
    input_tensors = [
        tf_graph.get_tensor_by_name(input_signature.name)
        for _, input_signature in signature_definition.inputs.items()
    ]
    output_tensors = [
        tf_graph.get_tensor_by_name(output_signature.name)
        for _, output_signature in signature_definition.outputs.items()
    ]
mlflow.tensorflow.log_model(model, artifact_path, custom_objects=None, conda_env=None, code_paths=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, registered_model_name=None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, saved_model_kwargs=None, keras_model_kwargs=None, metadata=None)[source]

Log a TF2 core model (inheriting tf.Module) or a Keras model in MLflow Model format.

Note

If you log a Keras or TensorFlow model without a signature, inference with mlflow.pyfunc.spark_udf() will not work unless the model’s pyfunc representation accepts pandas DataFrames as inference inputs.

You can infer a model’s signature by calling the mlflow.models.infer_signature() API on features from the model’s test dataset. You can also manually create a model signature, for example:

Example of creating signature for saving TensorFlow and tf.Keras models 
from mlflow.types.schema import Schema, TensorSpec
from mlflow.models import ModelSignature
import numpy as np

input_schema = Schema(
    [
        TensorSpec(np.dtype(np.uint64), (-1, 5), "field1"),
        TensorSpec(np.dtype(np.float32), (-1, 3, 2), "field2"),
    ]
)
# Create the signature for a model that requires 2 inputs:
#  - Input with name "field1", shape (-1, 5), type "np.uint64"
#  - Input with name "field2", shape (-1, 3, 2), type "np.float32"
signature = ModelSignature(inputs=input_schema)
Parameters

  • model – The TF2 core model (inheriting tf.Module) or Keras model to be saved.

  • artifact_path – The run-relative path to which to log model artifacts.

  • 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.tensorflow.load_model() and mlflow.pyfunc.load_model().

  • 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=3.8.15",
        {
            "pip": [
                "tensorflow==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.

  • 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 –

    an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:

    from mlflow.models import infer_signature

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

  • input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then 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. If input example is a tuple, then the first element must be a valid model input, and the second element must be a valid params dictionary that could be used for model inference. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.

  • 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. ["tensorflow", "-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.

  • saved_model_kwargs – a dict of kwargs to pass to tensorflow.saved_model.save method.

  • keras_model_kwargs – a dict of kwargs to pass to keras_model.save method.

  • metadata –

    Custom metadata dictionary passed to the model and stored in the MLmodel file.

    Note

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

Returns

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

mlflow.tensorflow.save_model(model, path, conda_env=None, code_paths=None, mlflow_model=None, custom_objects=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, pip_requirements=None, extra_pip_requirements=None, saved_model_kwargs=None, keras_model_kwargs=None, metadata=None)[source]

Save a TF2 core model (inheriting tf.Module) or Keras model in MLflow Model format to a path on the local file system.

Note

If you save a Keras or TensorFlow model without a signature, inference with mlflow.pyfunc.spark_udf() will not work unless the model’s pyfunc representation accepts pandas DataFrames as inference inputs. You can infer a model’s signature by calling the mlflow.models.infer_signature() API on features from the model’s test dataset. You can also manually create a model signature, for example:

Example of creating signature for saving TensorFlow and tf.Keras models 
from mlflow.types.schema import Schema, TensorSpec
from mlflow.models import ModelSignature
import numpy as np

input_schema = Schema(
    [
        TensorSpec(np.dtype(np.uint64), (-1, 5), "field1"),
        TensorSpec(np.dtype(np.float32), (-1, 3, 2), "field2"),
    ]
)
# Create the signature for a model that requires 2 inputs:
#  - Input with name "field1", shape (-1, 5), type "np.uint64"
#  - Input with name "field2", shape (-1, 3, 2), type "np.float32"
signature = ModelSignature(inputs=input_schema)
Parameters

  • model – The Keras model or Tensorflow module to be saved.

  • path – Local path where the MLflow 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=3.8.15",
        {
            "pip": [
                "tensorflow==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 configuration to which to add the tensorflow flavor.

  • 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.tensorflow.load_model() and mlflow.pyfunc.load_model().

  • signature –

    an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:

    from mlflow.models import infer_signature

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

  • input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then 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. If input example is a tuple, then the first element must be a valid model input, and the second element must be a valid params dictionary that could be used for model inference. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["tensorflow", "-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.

  • saved_model_kwargs – a dict of kwargs to pass to tensorflow.saved_model.save method if the model to be saved is a Tensorflow module.

  • keras_model_kwargs – a dict of kwargs to pass to model.save method if the model to be saved is a keras model.

  • metadata –

    Custom metadata dictionary passed to the model and stored in the MLmodel file.

    Note

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