28627903b22c8d365295d6057be392ab7b03a6a2,mlflow/sagemaker/__init__.py,,_update_sagemaker_endpoint,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,691

Before Change

                                                 image_url=image_url,
                                                 execution_role=role,
                                                 sage_client=sage_client)
    eprint("Created new model with arn: %s" % new_model_response["ModelArn"])

    if mode == DEPLOYMENT_MODE_ADD:
        new_model_weight = 0
        production_variants = deployed_production_variants

After Change

                                                 image_url=image_url,
                                                 execution_role=role,
                                                 sage_client=sage_client)
    _logger.info("Created new model with arn: %s", new_model_response["ModelArn"])

    if mode == DEPLOYMENT_MODE_ADD:
        new_model_weight = 0
        production_variants = deployed_production_variants
    elif mode == DEPLOYMENT_MODE_REPLACE:
        new_model_weight = 1
        production_variants = []

    new_production_variant = {
        "VariantName": new_model_name,
        "ModelName": new_model_name,
        "InitialInstanceCount": instance_count,
        "InstanceType": instance_type,
        "InitialVariantWeight": new_model_weight
    }
    production_variants.append(new_production_variant)

    // Create the new endpoint configuration and update the endpoint
    // to adopt the new configuration
    new_config_name = _get_sagemaker_config_name(endpoint_name)
    endpoint_config_response = sage_client.create_endpoint_config(
        EndpointConfigName=new_config_name,
        ProductionVariants=production_variants,
        Tags=[
            {
                "Key": "app_name",
                "Value": endpoint_name,
            },
        ],
    )
    _logger.info("Created new endpoint configuration with arn: %s",
                 endpoint_config_response["EndpointConfigArn"])

    sage_client.update_endpoint(EndpointName=endpoint_name,
                                EndpointConfigName=new_config_name)
    _logger.info("Updated endpoint with new configuration!")

    // If applicable, clean up unused models and old configurations
    if not archive:
        _logger.info("Cleaning up unused resources...")
        if mode == DEPLOYMENT_MODE_REPLACE:
            s3_client = boto3.client("s3")
            for pv in deployed_production_variants:
                deployed_model_arn = _delete_sagemaker_model(model_name=pv["ModelName"],
                                                             sage_client=sage_client,
                                                             s3_client=s3_client)
                _logger.info("Deleted model with arn: %s", deployed_model_arn)

        sage_client.delete_endpoint_config(
            EndpointConfigName=deployed_config_name)
        _logger.info("Deleted endpoint configuration with arn: %s", deployed_config_arn)


def _create_sagemaker_model(model_name, model_s3_path, flavor, vpc_config, run_id, image_url,
                            execution_role, sage_client):

Instances