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
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), followed by
prediction= model.predict(pandas DataFrame) in order to obtain a prediction in a pandas DataFrame.
Note that 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 tf model is saved.
- signature_def_key – Which signature definition to use when loading the model again. See https://www.tensorflow.org/serving/signature_defs for details.
- artifact_path – Path (within the artifact directory for the current run) to which artifacts of the model will be saved.