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)[source] Note
Autologging is known to be compatible with the following package versions:
2.3.0
<=tensorflow
<=2.12.0
. Autologging may not succeed when used with package versions outside of this range.Enables autologging for
tf.keras
andkeras
. Note that onlytensorflow>=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()
orfit_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
, etcfit()
orfit_generator()
parameters associated withEarlyStopping
:min_delta
,patience
,baseline
,restore_best_weights
, etc
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. IfFalse
, trained models are not logged.log_datasets – If
True
, dataset information is logged to MLflow Tracking. IfFalse
, dataset information is not logged.disable – If
True
, disables the TensorFlow autologging integration. IfFalse
, enables the TensorFlow integration autologging integration.exclusive – If
True
, autologged content is not logged to user-created fluent runs. IfFalse
, 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. IfFalse
, 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. IfFalse
, 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. IfFalse
, signatures are not logged. Note that logging TensorFlow models with signatures changes their pyfunc inference behavior when Pandas DataFrames are passed topredict()
. When a signature is present, annp.ndarray
(for single-output models) or a mapping fromstr
->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.
-
mlflow.tensorflow.
get_default_conda_env
()[source] - Returns
The default Conda environment for MLflow Models produced by calls to
save_model()
andlog_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()
andlog_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.
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] = 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:from mlflow.types.schema import Schema, TensorSpec from mlflow.models.signature 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 withmlflow.tensorflow.load_model()
andmlflow.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()
. IfNone
, a conda environment with pip requirements inferred bymlflow.models.infer_pip_requirements()
is added to the model. If the requirement inference fails, it falls back to usingget_default_pip_requirements()
. pip requirements fromconda_env
are written to a piprequirements.txt
file and the full conda environment is written toconda.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 –
ModelSignature
describes model input and outputSchema
. The model signature can beinferred
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. IfNone
, a default list of requirements is inferred bymlflow.models.infer_pip_requirements()
from the current software environment. If the requirement inference fails, it falls back to usingget_default_pip_requirements()
. Both requirements and constraints are automatically parsed and written torequirements.txt
andconstraints.txt
files, respectively, and stored as part of the model. Requirements are also written to thepip
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 torequirements.txt
andconstraints.txt
files, respectively, and stored as part of the model. Requirements are also written to thepip
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
andextra_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] = 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 themlflow.models.infer_signature()
API on features from the model’s test dataset. You can also manually create a model signature, for example:from mlflow.types.schema import Schema, TensorSpec from mlflow.models.signature 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 – {{ conda_env }}
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 withmlflow.tensorflow.load_model()
andmlflow.pyfunc.load_model()
.signature –
ModelSignature
describes model input and outputSchema
. The model signature can beinferred
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 – {{ pip_requirements }}
extra_pip_requirements – {{ 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.