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, disable=False, exclusive=False, disable_for_unsupported_versions=False, silent=False, registered_model_name=None, log_input_examples=False, log_model_signatures=False)[source]

Note

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

Enables automatic logging from TensorFlow to MLflow. Note that autologging for tf.keras is handled by mlflow.tensorflow.autolog(), not mlflow.keras.autolog(). As an example, try running the TensorFlow examples.

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

tf.estimator
  • Metrics and Parameters

  • TensorBoard metrics: average_loss, loss, etc

  • Parameters steps and max_steps

  • Artifacts

  • MLflow Model (TF saved model) on call to tf.estimator.export_saved_model

TensorFlow Core
  • Metrics

  • All tf.summary.scalar calls

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

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.

  • 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. False by default because 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.

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()[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)[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.

Returns

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

Example
import mlflow.tensorflow
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(tf_saved_model_dir, tf_meta_graph_tags, tf_signature_def_key, artifact_path, 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] = None, registered_model_name=None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None)[source]

Note

This method requires all argument be specified by keyword.

Log a serialized collection of TensorFlow graphs and variables as an MLflow model for the current run. This method operates on TensorFlow variables and graphs that have been serialized in TensorFlow’s SavedModel format. For more information about SavedModel format, see the TensorFlow documentation: https://www.tensorflow.org/guide/saved_model#save_and_restore_models.

This method saves a model with both python_function and tensorflow flavors. If loaded back using the python_function flavor, the model can be used to predict on pandas DataFrames, producing a pandas DataFrame whose output columns correspond to the TensorFlow model’s outputs. The python_function model will flatten outputs that are length-one, one-dimensional tensors of a single scalar value (e.g. {"predictions": [[1.0], [2.0], [3.0]]}) into the scalar values (e.g. {"predictions": [1, 2, 3]}), so that the resulting output column is a column of scalars rather than lists of length one. All other model output types are included as-is in the output DataFrame.

Note that this method should not be used to log a tf.keras model. Use mlflow.keras.log_model() instead.

Parameters
  • tf_saved_model_dir – Path to the directory containing serialized TensorFlow variables and graphs in SavedModel format.

  • tf_meta_graph_tags – A list of tags identifying the model’s metagraph within the serialized SavedModel object. For more information, see the tags parameter of the tf.saved_model.builder.SavedModelBuilder method.

  • tf_signature_def_key – A string identifying the input/output signature associated with the model. This is a key within the serialized SavedModel signature definition mapping. For more information, see the signature_def_map parameter of the tf.saved_model.builder.SavedModelBuilder method.

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

  • 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": [
                    "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

    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. ["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.

Returns

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

mlflow.tensorflow.save_model(tf_saved_model_dir, tf_meta_graph_tags, tf_signature_def_key, path, mlflow_model=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] = None, pip_requirements=None, extra_pip_requirements=None)[source]

Note

This method requires all argument be specified by keyword.

Save a serialized collection of TensorFlow graphs and variables as an MLflow model to a local path. This method operates on TensorFlow variables and graphs that have been serialized in TensorFlow’s SavedModel format. For more information about SavedModel format, see the TensorFlow documentation: https://www.tensorflow.org/guide/saved_model#save_and_restore_models.

Parameters
  • tf_saved_model_dir – Path to the directory containing serialized TensorFlow variables and graphs in SavedModel format.

  • tf_meta_graph_tags – A list of tags identifying the model’s metagraph within the serialized SavedModel object. For more information, see the tags parameter of the tf.saved_model.builder.savedmodelbuilder method.

  • tf_signature_def_key – A string identifying the input/output signature associated with the model. This is a key within the serialized savedmodel signature definition mapping. For more information, see the signature_def_map parameter of the tf.saved_model.builder.savedmodelbuilder method.

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

  • mlflow_model – MLflow model configuration to which to add the tensorflow flavor.

  • 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": [
                    "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.

  • 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. ["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.