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=100)

Note

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

Enable automatic logging from TensorFlow to MLflow. If applicable, model checkpoints are logged as artifacts to a ‘models’ directory, along with any TensorBoard log data.

Refer to the tracking documentation for information on what is logged with different TensorFlow workflows.

Parameters:	every_n_iter – The frequency with which metrics should be logged. Defaults to 100. Ex: a value of 100 will log metrics at step 0, 100, 200, etc.

mlflow.tensorflow.get_default_conda_env()

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

mlflow.tensorflow.load_model(model_uri, tf_sess)

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

This method must be called within a TensorFlow graph context.

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` For more information about supported URI schemes, see Referencing Artifacts. tf_sess – The TensorFlow session in which to load the model.
Returns:	A TensorFlow signature definition of type: `tensorflow.core.protobuf.meta_graph_pb2.SignatureDef`. This defines the input and output tensors for model inference.

>>> 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_def.inputs.items()]
>>>     output_tensors = [tf_graph.get_tensor_by_name(output_signature.name)
>>>                       for _, output_signature in signature_def.outputs.items()]

mlflow.tensorflow.log_model(tf_saved_model_dir, tf_meta_graph_tags, tf_signature_def_key, artifact_path, conda_env=None)

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.

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 decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default 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',
        'tensorflow=1.8.0'
    ]
}
```

mlflow.tensorflow.save_model(tf_saved_model_dir, tf_meta_graph_tags, tf_signature_def_key, path, mlflow_model=<mlflow.models.Model object>, conda_env=None)

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 decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default 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',
        'tensorflow=1.8.0'
    ]
}
```