mlflow.sagemaker

mlflow.sagemaker.build_image(name='mlflow_sage', mlflow_home=None)

This function builds an MLflow Docker image. The image is built locally and it requires Docker to run.

Parameters:name – image name

mlflow.sagemaker.deploy(app_name, model_path, execution_role_arn, bucket, run_id=None, image='mlflow_sage', region_name='us-west-2')

Deploy model on Sagemaker. Current active AWS account needs to have correct permissions setup.

Parameters:

• app_name – Name of the deployed app.
• path – Path to the model. Either local if no run_id or MLflow-relative if run_id is specified)
• execution_role_arn – Amazon execution role with sagemaker rights
• bucket – S3 bucket where model artifacts are gonna be stored
• run_id – MLflow run id.
• image – name of the Docker image to be used.
• region_name – Name of the AWS region to deploy to.

mlflow.sagemaker.push_image_to_ecr(image='mlflow_sage')

Push local Docker image to ECR.

The image is pushed under current active aws account and to current active AWS region.

Parameters:image – image name

mlflow.sagemaker.run_local(model_path, run_id=None, port=5000, image='mlflow_sage')

Serve model locally in a SageMaker compatible Docker container. :param model_path: Path to the model. Either local if no run_id or MLflow-relative if run_id is specified) :param run_id: MLflow RUN-ID. :param port: local port :param image: name of the Docker image to be used.