reference-yaml-deployment-managed-online.md

title	titleSuffix	description	services	ms.service	ms.subservice	ms.topic	ms.custom	author	ms.author	ms.date	ms.reviewer
CLI (v2) managed online deployment YAML schema	Azure Machine Learning	Reference documentation for the CLI (v2) managed online deployment YAML schema.	machine-learning	machine-learning	mlops	reference	cliv2, event-tier1-build-2022	rsethur	seramasu	04/26/2022	larryfr

CLI (v2) managed online deployment YAML schema

[!INCLUDE cli v2]

The source JSON schema can be found at https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.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. Naming rules are defined here.
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	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. This field is optional for custom container deployment scenarios.
model_mount_path	string	The path to mount the model in a custom container. Applicable only for custom container deployment scenarios. If the model field is specified, it is mounted on this path in the container.
code_configuration	object	Configuration for the scoring code logic. This field is optional for custom container deployment scenarios.
code_configuration.code	string	Local path to the source code directory for scoring the model.
code_configuration.scoring_script	string	Relative path to the scoring file in the source code directory.
environment_variables	object	Dictionary of environment variable key-value pairs to set in the deployment container. You can access these environment variables from your scoring scripts.
environment	string or object	Required. 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. 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.
instance_type	string	Required. The VM size to use for the deployment. For the list of supported sizes, see Managed online endpoints SKU list.
instance_count	integer	Required. The number of instances to use for the deployment. Specify the value based on the workload you expect. For high availability, Microsoft recommends you set it to at least 3. instance_count can be updated after deployment creation using az ml online-deployment update command. We reserve an extra 20% for performing upgrades. For more information, see managed online endpoint quotas.
app_insights_enabled	boolean	Whether to enable integration with the Azure Application Insights instance associated with your workspace.		false
scale_settings	object	The scale settings for the deployment. Currently only the default scale type is supported, so you do not need to specify this property. With this default scale type, you can either manually scale the instance count up and down after deployment creation by updating the instance_count property, or create an autoscaling policy.
scale_settings.type	string	The scale type.	default	default
request_settings	object	Scoring request settings for the deployment. See RequestSettings for the set of configurable properties.
liveness_probe	object	Liveness probe settings for monitoring the health of the container regularly. See ProbeSettings for the set of configurable properties.
readiness_probe	object	Readiness probe settings for validating if the container is ready to serve traffic. See ProbeSettings for the set of configurable properties.
egress_public_network_access	string	This flag secures the deployment by restricting communication between the deployment and the Azure resources used by it. Set to disabled to ensure that the download of the model, code, and images needed by your deployment are secured with a private endpoint. This flag is applicable only for managed online endpoints.	enabled, disabled	enabled

RequestSettings

Key	Type	Description	Default value
request_timeout_ms	integer	The scoring timeout in milliseconds.	5000
max_concurrent_requests_per_instance	integer	The maximum number of concurrent requests per instance allowed for the deployment. Do not change this setting from the default value unless instructed by Microsoft Technical Support or a member of the Azure ML team.	1
max_queue_wait_ms	integer	The maximum amount of time in milliseconds a request will stay in the queue.	500

ProbeSettings

Key	Type	Description	Default value
period	integer	How often (in seconds) to perform the probe.	10
initial_delay	integer	The number of seconds after the container has started before the probe is initiated. Minimum value is 1.	10
timeout	integer	The number of seconds after which the probe times out. Minimum value is 1.	2
success_threshold	integer	The minimum consecutive successes for the probe to be considered successful after having failed. Minimum value is 1.	1
failure_threshold	integer	When a probe fails, the system will try failure_threshold times before giving up. Giving up in the case of a liveness probe means the container will be restarted. In the case of a readiness probe the container will be marked Unready. Minimum value is 1.	30

Remarks

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

Examples

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

YAML: basic

:::code language="yaml" source="~/azureml-examples-main/cli/endpoints/online/managed/sample/blue-deployment.yml":::

:::code language="yaml" source="~/azureml-examples-main/cli/endpoints/online/managed/sample/green-deployment.yml":::

YAML: system-assigned identity

:::code language="yaml" source="~/azureml-examples-main/cli/endpoints/online/managed/managed-identities/2-sai-deployment.yml":::

YAML: user-assigned identity

:::code language="yaml" source="~/azureml-examples-main/cli/endpoints/online/managed/managed-identities/2-uai-deployment.yml":::

Next steps

• Install and use the CLI (v2)