mlflow.tensorflow

The mlflow.tensorflow module provides an API for logging and loading TensorFlow models as mlflow.pyfunc models.

You must save your own saved_model and pass its path to log_saved_model(saved_model_dir). To load the model to predict on it, you call model = pyfunc.load_pyfunc(saved_model_dir) followed by prediction = model.predict(pandas DataFrame) to obtain a prediction in a pandas DataFrame.

The loaded mlflow.pyfunc model does not expose any APIs for model training.

mlflow.tensorflow.load_model(path, tf_sess, run_id=None)

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

This method must be called within a Tensorflow graph context!

Parameters:
  • path – The local filesystem path or run-relative artifact path to the model.
  • tf_sess – The Tensorflow session in which to the 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(path="model_path", 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)

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 the SavedModel, see the following 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: https://www.tensorflow.org/api_docs/python/tf/saved_model/builder/ SavedModelBuilder#add_meta_graph
  • tf_signature_def_key – A string identifying the input/output signature associated with the model. This is a key within the serialized SavedModel’s 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 mlflow.tensorflow.DEFAULT_CONDA_ENV. If None, the default mlflow.tensorflow.DEFAULT_CONDA_ENV environment will be 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)

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 the SavedModel, see the following 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: https://www.tensorflow.org/api_docs/python/tf/saved_model/builder/ savedmodelbuilder#add_meta_graph
  • tf_signature_def_key – A string identifying the input/output signature associated with the model. this is a key within the serialized savedmodel’s 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 this flavor will be added.
  • 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 mlflow.tensorflow.DEFAULT_CONDA_ENV. If None, the default mlflow.tensorflow.DEFAULT_CONDA_ENV environment will be 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'
    ]
}