mlflow.fastai

The mlflow.fastai module provides an API for logging and loading fast.ai models. This module exports fast.ai models with the following flavors:

fastai (native) format

This is the main flavor that can be loaded back into fastai.

mlflow.pyfunc

Produced for use by generic pyfunc-based deployment tools and batch inference.

mlflow.fastai.autolog(log_models=True, log_datasets=True, disable=False, exclusive=False, disable_for_unsupported_versions=False, silent=False, registered_model_name=None, extra_tags=None)[source]

Note

Autologging is known to be compatible with the following package versions: 2.4.1 <= fastai <= 2.7.17. Autologging may not succeed when used with package versions outside of this range.

Enable automatic logging from Fastai to MLflow.

Logs loss and any other metrics specified in the fit function, and optimizer data as parameters. Model checkpoints are logged as artifacts to a ‘models’ directory.

MLflow will also log the parameters of the EarlyStoppingCallback and OneCycleScheduler callbacks

Parameters

  • log_models – If True, trained models are logged as MLflow model artifacts. If False, trained models are not logged.

  • log_datasets – If True, dataset information is logged to MLflow Tracking. If False, dataset information is not logged.

  • disable – If True, disables the Fastai autologging integration. If False, enables the Fastai 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 fastai 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 Fastai autologging. If False, show all events and warnings during Fastai 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.

  • extra_tags – A dictionary of extra tags to set on each managed run created by autologging.

Example 
# This is a modified example from
# https://github.com/mlflow/mlflow/tree/master/examples/fastai
# demonstrating autolog capabilities.

import fastai.vision as vis
import mlflow.fastai
from mlflow import MlflowClient


def print_auto_logged_info(r):
    tags = {k: v for k, v in r.data.tags.items() if not k.startswith("mlflow.")}
    artifacts = [f.path for f in MlflowClient().list_artifacts(r.info.run_id, "model")]
    print(f"run_id: {r.info.run_id}")
    print(f"artifacts: {artifacts}")
    print(f"params: {r.data.params}")
    print(f"metrics: {r.data.metrics}")
    print(f"tags: {tags}")


def main(epochs=5, learning_rate=0.01):
    # Download and untar the MNIST data set
    path = vis.untar_data(vis.URLs.MNIST_SAMPLE)

    # Prepare, transform, and normalize the data
    data = vis.ImageDataBunch.from_folder(
        path, ds_tfms=(vis.rand_pad(2, 28), []), bs=64
    )
    data.normalize(vis.imagenet_stats)

    # Create CNN the Learner model
    model = vis.cnn_learner(data, vis.models.resnet18, metrics=vis.accuracy)

    # Enable auto logging
    mlflow.fastai.autolog()

    # Start MLflow session
    with mlflow.start_run() as run:
        model.fit(epochs, learning_rate)

    # fetch the auto logged parameters, metrics, and artifacts
    print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id))


main()
output 
run_id: 5a23dcbcaa334637814dbce7a00b2f6a
artifacts: ['model/MLmodel', 'model/conda.yaml', 'model/model.fastai']
params: {'wd': 'None',
         'bn_wd': 'True',
         'opt_func': 'Adam',
         'epochs': '5', '
         train_bn': 'True',
         'num_layers': '60',
         'lr': '0.01',
         'true_wd': 'True'}
metrics: {'train_loss': 0.024,
          'accuracy': 0.99214,
          'valid_loss': 0.021}
# Tags model summary omitted too long
tags: {...}
../_images/fastai_autolog.png

Fastai autologged MLflow entities

mlflow.fastai.get_default_conda_env(include_cloudpickle=False)[source]
Returns

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

mlflow.fastai.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() and log_model() produce a pip environment that, at minimum, contains these requirements.

mlflow.fastai.load_model(model_uri, dst_path=None)[source]

Load a fastai model from a local file or a run.

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.

  • 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.

Returns

A fastai model (an instance of fastai.Learner).

Example 
import mlflow.fastai

# Define the Learner model
model = ...

# log the fastai Leaner model
with mlflow.start_run() as run:
    model.fit(epochs, learning_rate)
    mlflow.fastai.log_model(model, "model")

# Load the model for scoring
model_uri = f"runs:/{run.info.run_id}/model"
loaded_model = mlflow.fastai.load_model(model_uri)
results = loaded_model.predict(predict_data)
mlflow.fastai.log_model(fastai_learner, artifact_path, conda_env=None, code_paths=None, registered_model_name=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, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, metadata=None, **kwargs)[source]

Log a fastai model as an MLflow artifact for the current run.

Parameters

  • fastai_learner – Fastai model (an instance of fastai.Learner) to be saved.

  • artifact_path – Run-relative artifact path.

  • 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": [
                "fastai==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 – This argument may change or be removed in a future release without warning. 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 –

    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 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.

  • 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. ["fastai", "-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.

  • metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

  • kwargs – kwargs to pass to fastai.Learner.export method.

Returns

A ModelInfo instance that contains the metadata of the logged model.

Example 
import fastai.vision as vis
import mlflow.fastai
from mlflow import MlflowClient


def main(epochs=5, learning_rate=0.01):
    # Download and untar the MNIST data set
    path = vis.untar_data(vis.URLs.MNIST_SAMPLE)
    # Prepare, transform, and normalize the data
    data = vis.ImageDataBunch.from_folder(
        path, ds_tfms=(vis.rand_pad(2, 28), []), bs=64
    )
    data.normalize(vis.imagenet_stats)
    # Create the CNN Learner model
    model = vis.cnn_learner(data, vis.models.resnet18, metrics=vis.accuracy)
    # Start MLflow session and log model
    with mlflow.start_run() as run:
        model.fit(epochs, learning_rate)
        mlflow.fastai.log_model(model, "model")
    # fetch the logged model artifacts
    artifacts = [
        f.path for f in MlflowClient().list_artifacts(run.info.run_id, "model")
    ]
    print(f"artifacts: {artifacts}")


main()
Output 
artifacts: ['model/MLmodel', 'model/conda.yaml', 'model/model.fastai']
mlflow.fastai.save_model(fastai_learner, path, conda_env=None, code_paths=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, metadata=None, **kwargs)[source]

Save a fastai Learner to a path on the local file system.

Parameters

  • fastai_learner – fastai Learner to be saved.

  • path – Local path where the model is to be saved.

  • 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": [
                "fastai==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.

  • mlflow_model – MLflow model config this flavor is being added to.

  • signature –

    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 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. ["fastai", "-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.

  • metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

  • kwargs – kwargs to pass to Learner.save method.

Example 
import os

import mlflow.fastai

# Create a fastai Learner model
model = ...

# Start MLflow session and save model to current working directory
with mlflow.start_run():
    model.fit(epochs, learning_rate)
    mlflow.fastai.save_model(model, "model")

# Load saved model for inference
model_uri = "{}/{}".format(os.getcwd(), "model")
loaded_model = mlflow.fastai.load_model(model_uri)
results = loaded_model.predict(predict_data)