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
()[source] Note
Experimental: This method may change or be removed in a future release without warning.
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
-
mlflow.fastai.
get_default_conda_env
(include_cloudpickle=False)[source] - Returns
The default Conda environment for MLflow Models produced by calls to
save_model()
andlog_model()
.
-
mlflow.fastai.
load_model
(model_uri)[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.
- Returns
A fastai model (an instance of fastai.Learner).
-
mlflow.fastai.
log_model
(fastai_learner, artifact_path, conda_env=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = None, await_registration_for=300, **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 minimum, it should specify the dependencies contained in
get_default_conda_env()
. IfNone
, the defaultget_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', 'fastai=1.0.60', ] }
registered_model_name – Note:: Experimental: 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 –
(Experimental)
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 – (Experimental) 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 will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.
kwargs – kwargs to pass to fastai.Learner.export method.
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.
-
mlflow.fastai.
save_model
(fastai_learner, path, conda_env=None, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = 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 minimum, it should specify the dependencies contained in
get_default_conda_env()
. IfNone
, the defaultget_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', 'fastai=1.0.60', ] }
mlflow_model – MLflow model config this flavor is being added to.
signature –
(Experimental)
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 – (Experimental) 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 will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.
kwargs – kwargs to pass to
Learner.save
method.