# mlflow.onnx

The mlflow.onnx module provides APIs for logging and loading ONNX models in the MLflow Model format. This module exports MLflow Models with the following flavors:

ONNX (native) format
This is the main flavor that can be loaded back as an ONNX model object.
mlflow.pyfunc
Produced for use by generic pyfunc-based deployment tools and batch inference.
mlflow.onnx.get_default_conda_env()

Note

Experimental: This method may change or be removed in a future release without warning.

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

Note

Experimental: This method may change or be removed in a future release without warning.

Load an ONNX 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://run-relative/path/to/model For more information about supported URI schemes, see the Artifacts Documentation. An ONNX model instance.
mlflow.onnx.log_model(onnx_model, artifact_path, conda_env=None)

Note

Experimental: This method may change or be removed in a future release without warning.

Log an ONNX model as an MLflow artifact for the current run.

Parameters: onnx_model – ONNX model 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 decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default get_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=3.6.0', 'onnx=1.4.1', 'onnxruntime=0.3.0' ] } 
mlflow.onnx.save_model(onnx_model, path, conda_env=None, mlflow_model=<mlflow.models.Model object>)

Note

Experimental: This method may change or be removed in a future release without warning.

Save an ONNX model to a path on the local file system.

Parameters: onnx_model – ONNX model 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 decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default get_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=3.6.0', 'onnx=1.4.1', 'onnxruntime=0.3.0' ] }  mlflow_model – mlflow.models.Model this flavor is being added to.