MLflow integration for TensorFlow.
Manages logging and loading TensorFlow models as Python Functions. You are expected to save your own
saved_models and pass their paths to
log_saved_model() so that MLflow can track the models.
In order to load the model to predict on it again, you can call
model = mlflow.pyfunc.load_pyfunc(saved_model_dir),
prediction = model.predict(pandas DataFrame) to obtain a prediction in a
The loaded PyFunc model does not expose any APIs for model training.
Load model stored in Python Function format. The loaded model object exposes a
predict(pandas DataFrame)method that returns a Pandas DataFrame containing the model’s inference output on an input DataFrame.
Parameters: saved_model_dir – Directory where the model is saved. Return type: Pyfunc format model with function
model.predict(pandas DataFrame) -> pandas DataFrame).
log_saved_model(saved_model_dir, signature_def_key, artifact_path)
Log a TensorFlow model as an MLflow artifact for the current run.
- saved_model_dir – Directory where the exported TensorFlow model is saved.
- signature_def_key – The signature definition to use when loading the model again. See SignatureDefs in SavedModel for TensorFlow Serving for details.
- artifact_path – Path (within the artifact directory for the current run) to which artifacts of the model will be saved.