reference-yaml-deployment-batch.md

title	titleSuffix	description	services	ms.service	ms.subservice	ms.topic	ms.custom	author	ms.author	ms.date	ms.reviewer
CLI (v2) batch deployment YAML schema	Azure Machine Learning	Reference documentation for the CLI (v2) batch deployment YAML schema.	machine-learning	machine-learning	core	reference	cliv2, event-tier1-build-2022	blackmist	larryfr	03/31/2022	nibaccam

CLI (v2) batch deployment YAML schema

[!INCLUDE cli v2]

The source JSON schema can be found at https://azuremlschemas.azureedge.net/latest/batchDeployment.schema.json.

[!INCLUDE schema note]

YAML syntax

Key	Type	Description	Allowed values	Default value
$schema	string	The YAML schema. If you use the Azure Machine Learning VS Code extension to author the YAML file, including $schema at the top of your file enables you to invoke schema and resource completions.
name	string	Required. Name of the deployment.
description	string	Description of the deployment.
tags	object	Dictionary of tags for the deployment.
endpoint_name	string	Required. Name of the endpoint to create the deployment under.
model	string or object	Required. The model to use for the deployment. This value can be either a reference to an existing versioned model in the workspace or an inline model specification. To reference an existing model, use the azureml:<model-name>:<model-version> syntax. To define a model inline, follow the Model schema. As a best practice for production scenarios, you should create the model separately and reference it here.
code_configuration	object	Configuration for the scoring code logic. This property is not required if your model is in MLflow format.
code_configuration.code	string	The local directory that contains all the Python source code to score the model.
code_configuration.scoring_script	string	The Python file in the above directory. This file must have an init() function and a run() function. Use the init() function for any costly or common preparation (for example, load the model in memory). init() will be called only once at beginning of process. Use run(mini_batch) to score each entry; the value of mini_batch is a list of file paths. The run() function should return a pandas DataFrame or an array. Each returned element indicates one successful run of input element in the mini_batch. For more information on how to author scoring script, see Understanding the scoring script.
environment	string or object	The environment to use for the deployment. This value can be either a reference to an existing versioned environment in the workspace or an inline environment specification. This property is not required if your model is in MLflow format. To reference an existing environment, use the azureml:<environment-name>:<environment-version> syntax. To define an environment inline, follow the Environment schema. As a best practice for production scenarios, you should create the environment separately and reference it here.
compute	string	Required. Name of the compute target to execute the batch scoring jobs on. This value should be a reference to an existing compute in the workspace using the azureml:<compute-name> syntax.
resources.instance_count	integer	The number of nodes to use for each batch scoring job.		1
max_concurrency_per_instance	integer	The maximum number of parallel scoring_script runs per instance.		1
error_threshold	integer	The number of file failures that should be ignored. If the error count for the entire input goes above this value, the batch scoring job will be terminated. error_threshold is for the entire input and not for individual mini batches. If omitted, any number of file failures will be allowed without terminating the job.		-1
logging_level	string	The log verbosity level.	warning, info, debug	info
mini_batch_size	integer	The number of files the code_configuration.scoring_script can process in one run() call.		10
retry_settings	object	Retry settings for scoring each mini batch.
retry_settings.max_retries	integer	The maximum number of retries for a failed or timed-out mini batch.		3
retry_settings.timeout	integer	The timeout in seconds for scoring a mini batch.		30
output_action	string	Indicates how the output should be organized in the output file.	append_row, summary_only	append_row
output_file_name	string	Name of the batch scoring output file.		predictions.csv
environment_variables	object	Dictionary of environment variable key-value pairs to set for each batch scoring job.

Remarks

The az ml batch-deployment commands can be used for managing Azure Machine Learning batch deployments.

Examples

Examples are available in the examples GitHub repository. Several are shown below.

YAML: basic (MLflow)

:::code language="yaml" source="~/azureml-examples-main/cli/endpoints/batch/mlflow-deployment.yml":::

YAML: custom model and scoring code

:::code language="yaml" source="~/azureml-examples-main/cli/endpoints/batch/nonmlflow-deployment.yml":::

Next steps

• Install and use the CLI (v2)