mlflow.keras

Note

Autologging is known to be compatible with the following package versions: 3.0.2 <= keras <= 3.12.0. Autologging may not succeed when used with package versions outside of this range.

Enable autologging for Keras.

This method configures the autologging for Keras workflow. Only Keras > 3 is supported. For usage of lower Keras version (also known as tf-keras), please refer to mlflow.tensorflow flavor. At a high level, calling this mlflow.keras.autolog() function will replace keras.Model.fit method with the custom fit method provided by MLflow, which logs metrics/params/info/model to MLflow at the corresponding time.

Autologging is compatible with all backends supported by Keras, including Tensorflow, PyTorch and JAX.

Please note that autologging works only when you are using model.fit() for training. If you are writing a custom training loop, then you need to use manual logging.

param log_every_epoch: If True, training metrics will be logged at the end of each epoch.
param log_every_n_steps: If set, training metrics will be logged every n training steps. log_every_n_steps must be None when log_every_epoch=True.
param log_models: If True, the Keras model will be logged to MLflow at the end of model.fit().
param log_model_signatures: If True, model signature will be automatically captured and logged.
param save_exported_model: If True, model will be saved as the exported format (compiled graph), which is suitable for serving and deployment. If False, model will be saved in .keras format, which contains model architecture and weights.
param log_datasets: If True, the dataset metadata will be logged to MLflow.
param log_input_examples: If True, input examples will be logged.
param disable: If True, disables the Keras autologging.
param 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 incompatible Keras versions.
param disable_for_unsupported_versions: If True, disable autologging for versions of keras that have not been tested against this version of the MLflow client or are incompatible.
param silent: If True, suppress all event logs and warnings from MLflow during Keras autologging. If True, show all events and warnings during Keras autologging.
param registered_model_name: If set, 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.
param save_model_kwargs: Extra kwargs passed to keras.Model.save().
param extra_tags: A dictionary of extra tags to set on each managed run created by autologging.

Example

import keras
import mlflow
import numpy as np

mlflow.keras.autolog()

# Prepare data for a 2-class classification.
data = np.random.uniform([8, 28, 28, 3])
label = np.random.randint(2, size=8)
model = keras.Sequential(
    [
        keras.Input([28, 28, 3]),
        keras.layers.Flatten(),
        keras.layers.Dense(2),
    ]
)
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(0.001),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
with mlflow.start_run() as run:
    model.fit(data, label, batch_size=4, epochs=2)

Keras 3 callback to log information to MLflow.

class mlflow.keras.callback.MlflowCallback(log_every_epoch=True, log_every_n_steps=None, model_id=None)[source]

Bases: keras.src.callbacks.callback.Callback

Callback for logging Keras metrics/params/model/… to MLflow.

This callback logs model metadata at training begins, and logs training metrics every epoch or every n steps (defined by the user) to MLflow.

Parameters

log_every_epoch – bool, defaults to True. If True, log metrics every epoch. If False, log metrics every n steps.
log_every_n_steps – int, defaults to None. If set, log metrics every n steps. If None, log metrics every epoch. Must be None if log_every_epoch=True.

Example

import keras
import mlflow
import numpy as np

# Prepare data for a 2-class classification.
data = np.random.uniform([8, 28, 28, 3])
label = np.random.randint(2, size=8)
model = keras.Sequential(
    [
        keras.Input([28, 28, 3]),
        keras.layers.Flatten(),
        keras.layers.Dense(2),
    ]
)
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(0.001),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
with mlflow.start_run() as run:
    model.fit(
        data,
        label,
        batch_size=4,
        epochs=2,
        callbacks=[mlflow.keras.MlflowCallback()],
    )

on_batch_end(batch, logs=None)[source]: Log metrics at the end of each batch with user specified frequency.

on_epoch_end(epoch, logs=None)[source]: Log metrics at the end of each epoch.

on_test_end(logs=None)[source]: Log validation metrics at validation end.

on_train_begin(logs=None)[source]: Log model architecture and optimizer configuration when training begins.

Functions for loading Keras models saved with MLflow.

class mlflow.keras.load.KerasModelWrapper(model, signature, save_exported_model=False)[source]

Bases: object

get_model_call_method()[source]

get_raw_model()[source]: Returns the underlying model.

predict(data, **kwargs)[source]

mlflow.keras.load.load_model(model_uri, dst_path=None, custom_objects=None, load_model_kwargs=None)[source]

Load Keras model from MLflow.

This method loads a saved Keras model from MLflow, and returns a Keras model instance.

Parameters

model_uri –
The URI of the saved Keras model in MLflow. 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. If unspecified, a local output path will be created.
custom_objects – The custom_objects arg in keras.saving.load_model.
load_model_kwargs – Extra args for keras.saving.load_model.

Example

import keras
import mlflow
import numpy as np

model = keras.Sequential(
    [
        keras.Input([28, 28, 3]),
        keras.layers.Flatten(),
        keras.layers.Dense(2),
    ]
)
with mlflow.start_run() as run:
    mlflow.keras.log_model(model)

model_url = f"runs:/{run.info.run_id}/{model_path}"
loaded_model = mlflow.keras.load_model(model_url)

# Test the loaded model produces the same output for the same input as the model.
test_input = np.random.uniform(size=[2, 28, 28, 3])
np.testing.assert_allclose(
    keras.ops.convert_to_numpy(model(test_input)),
    loaded_model.predict(test_input),
)

Returns: A Keras model instance.

Functions for saving Keras models to MLflow.

mlflow.keras.save.get_default_conda_env()[source]

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

mlflow.keras.save.get_default_pip_requirements()[source]

Returns: A list of default pip requirements for MLflow Models produced by Keras flavor. Calls to save_model() and log_model() produce a pip environment that, at minimum, contains these requirements.

mlflow.keras.save.log_model(model, artifact_path: str | None = None, save_exported_model=False, conda_env=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, registered_model_name=None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, save_model_kwargs=None, metadata=None, name: str | None = None, params: dict[str, typing.Any] | None = None, tags: dict[str, typing.Any] | None = None, model_type: str | None = None, step: int = 0, model_id: str | None = None)[source]

Log a Keras model along with metadata to MLflow.

This method saves a Keras model along with metadata such as model signature and conda environments to MLflow.

Parameters

model – an instance of keras.Model. The Keras model to be saved.
artifact_path – Deprecated. Use name instead.
save_exported_model – defaults to False. If True, save Keras model in exported model format, otherwise save in .keras format. For more information, please refer to Keras doc.
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 a 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=3.8.15",
        {
            "pip": [
                "keras==x.y.z"
            ],
        },
    ],
}
```
signature –
an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:
```
from mlflow.models import infer_signature

train = df.drop_column("target_label")
predictions = ...  # compute model predictions
signature = infer_signature(train, predictions)
```
input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then 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. When the signature parameter is None, the input example is used to infer a model signature.
registered_model_name – defaults to None. If set, create a model version under registered_model_name, also create a registered model if one with the given name does not exist.
await_registration_for – defaults to mlflow.tracking._model_registry.DEFAULT_AWAIT_MAX_SLEEP_SECONDS. 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. ["keras", "-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.
save_model_kwargs – defaults to None. 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.
name – Model name.
params – A dictionary of parameters to log with the model.
tags – A dictionary of tags to log with the model.
model_type – The type of the model.
step – The step at which to log the model outputs and metrics
model_id – The ID of the model.

Example

import keras
import mlflow

model = keras.Sequential(
    [
        keras.Input([28, 28, 3]),
        keras.layers.Flatten(),
        keras.layers.Dense(2),
    ]
)
with mlflow.start_run() as run:
    mlflow.keras.log_model(model, name="model")

mlflow.keras.save.save_model(model, path, save_exported_model=False, conda_env=None, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, pip_requirements=None, extra_pip_requirements=None, save_model_kwargs=None, metadata=None)[source]

Save a Keras model along with metadata.

This method saves a Keras model along with metadata such as model signature and conda environments to local file system. This method is called inside mlflow.keras.log_model().

Parameters

model – an instance of keras.Model. The Keras model to be saved.
path – local path where the MLflow model is to be saved.
save_exported_model – If True, save Keras model in exported model format, otherwise save in .keras format. For more information, please refer to https://keras.io/guides/serialization_and_saving/.
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 a 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=3.8.15",
        {
            "pip": [
                "keras==x.y.z"
            ],
        },
    ],
}
```
mlflow_model – an instance of mlflow.models.Model, defaults to None. MLflow model configuration to which to add the Keras model metadata. If None, a blank instance will be created.
signature –
an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:
```
from mlflow.models import infer_signature

train = df.drop_column("target_label")
predictions = ...  # compute model predictions
signature = infer_signature(train, predictions)
```
input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then 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. When the signature parameter is None, the input example is used to infer a model signature.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["keras", "-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.
save_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.

Example

import keras
import mlflow

model = keras.Sequential(
    [
        keras.Input([28, 28, 3]),
        keras.layers.Flatten(),
        keras.layers.Dense(2),
    ]
)
with mlflow.start_run() as run:
    mlflow.keras.save_model(model, "./model")