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)[source]

Note

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

Note

Autologging is known to be compatible with the following package versions: 2.0.0 <= tensorflow <= 2.6.0. 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.

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)[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.

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, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = 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.

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"
            ],
        },
    ],
}
```
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.
param 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.

mlflow.tensorflow.save_model(tf_saved_model_dir, tf_meta_graph_tags, tf_signature_def_key, path, mlflow_model=None, conda_env=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = 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"
            ],
        },
    ],
}
```
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.