az ml online-endpoint
Note
This reference is part of the ml extension for the Azure CLI (version 2.15.0 or higher). The extension will automatically install the first time you run an az ml online-endpoint command. Learn more about extensions.
Manage Azure ML online endpoints.
Azure ML endpoints provide a simple interface for creating and managing model deployments. Each endpoint can have one or more deployments, enabling the traffic from a single scoring endpoint to be served to multiple deployments if needed. This is useful for scenarios such as controlled rollout.
Azure ML supports two types of endpoints: online and batch. Online endpoints support real-time inference, while batch endpoints are used for offline batch scoring.
Name | Description | Type | Status |
---|---|---|---|
az ml online-endpoint create |
Create an endpoint. |
Extension | GA |
az ml online-endpoint delete |
Delete an endpoint. |
Extension | GA |
az ml online-endpoint get-credentials |
List the token/keys for an online endpoint. |
Extension | GA |
az ml online-endpoint invoke |
Invoke an endpoint. |
Extension | GA |
az ml online-endpoint list |
List endpoints in a workspace. |
Extension | GA |
az ml online-endpoint regenerate-keys |
Regenerate the keys for an online endpoint. |
Extension | GA |
az ml online-endpoint show |
Show details for an endpoint. |
Extension | GA |
az ml online-endpoint update |
Update an endpoint. |
Extension | GA |
Create an endpoint.
To create an endpoint, provide a YAML file with online endpoint configuration. If the endpoint already exists, it will fail. If you want to update existing endpoint, use az ml online-endpoint update.
az ml online-endpoint create --resource-group
--workspace-name
[--auth-mode]
[--file]
[--local {false, true}]
[--name]
[--no-wait]
[--set]
[--web]
Create an endpoint from a YAML specification file
az ml online-endpoint create --file endpoint.yml --resource-group my-resource-group --workspace-name my-workspace
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Authentication method for the endpoint. Allowed values: key, aml_token. Default: key.
Local path to the YAML file containing the Azure ML online-endpoint specification. The YAML reference docs for online-endpoint can be found at: https://aka.ms/ml-cli-v2-endpoint-online-yaml-reference.
Create endpoint locally. Note: traffic and auth is not supported locally. You can use 'az ml online-deployment create --local' directly. It will create an endpoint if one doesn't exist.
Name of the online endpoint.
Do not wait for the long-running-operation to finish. Default is False.
Update an object by specifying a property path and value to set. Example: --set property1.property2=.
Show the endpoint's details in Azure ML studio in a web browser.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
Delete an endpoint.
az ml online-endpoint delete --name
--resource-group
--workspace-name
[--local {false, true}]
[--no-wait]
[--yes]
Delete an online endpoint, including all its deployments
az ml online-endpoint delete --name my-online-endpoint --resource-group my-resource-group --workspace-name my-workspace
Name of the online endpoint.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Delete local endpoint.
Do not wait for the long-running-operation to finish. Default is False.
Do not prompt for confirmation.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
List the token/keys for an online endpoint.
az ml online-endpoint get-credentials --name
--resource-group
--workspace-name
List the keys for an online endpoint
az ml online-endpoint get-credentials --name my-online-endpoint --resource-group my-resource-group --workspace-name my-workspace
Name of the online endpoint.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
Invoke an endpoint.
You can invoke an online endpoint with some request data. This will be real-time inference, and the scoring results will be returned immediately.
az ml online-endpoint invoke --name
--resource-group
--workspace-name
[--deployment-name]
[--local {false, true}]
[--request-file]
Invoke an online endpoint with some request data
az ml online-endpoint invoke --name my-online-endpoint --request-file sample_request.json --resource-group my-resource-group --workspace-name my-workspace
Invoke an online endpoint, targeting a specific deployment
az ml online-endpoint invoke --name my-online-endpoint --deployment my-deployment --request-file sample_request.json --resource-group my-resource-group --workspace-name my-workspace
Name of the online endpoint.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Name of the deployment to target.
Invoke local endpoint. This will only work if a local deployment has been created for this endpoint.
Local path to the JSON file containing the request data.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
List endpoints in a workspace.
az ml online-endpoint list --resource-group
--workspace-name
[--local {false, true}]
List all the online endpoints in a workspace
az ml online-endpoint list --resource-group my-resource-group --workspace-name my-workspace
List all the batch endpoints in a workspace
az ml online-endpoint list --resource-group my-resource-group --workspace-name my-workspace
List all the online endpoints in a workspace using --query argument to execute a JMESPath query on the results of commands.
az ml online-endpoint list --query "[].{Name:name}" --output table --resource-group my-resource-group --workspace-name my-workspace
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
List all local endpoints.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
Regenerate the keys for an online endpoint.
az ml online-endpoint regenerate-keys --name
--resource-group
--workspace-name
[--key-type]
[--no-wait]
Regenerate the keys for an online endpoint
az ml online-endpoint regenerate-keys --name my-online-endpoint --resource-group my-resource-group --workspace-name my-workspace
Name of the online endpoint.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
The type of key to regenerate. Allowed values: primary, secondary.
Do not wait for the long-running-operation to finish. Default is False.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
Show details for an endpoint.
az ml online-endpoint show --name
--resource-group
--workspace-name
[--local {false, true}]
[--web]
Show the details for a batch endpoint
az ml online-endpoint show --name my-online-endpoint --resource-group my-resource-group --workspace-name my-workspace
Show the provisioning state of an endpoint using --query argument to execute a JMESPath query on the results of commands.
az ml online-endpoint show -n my-endpoint --query "{Name:name,State:provisioning_state}" --output table --resource-group my-resource-group --workspace-name my-workspace
Name of the online endpoint.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Show local endpoint.
Show the endpoint's details in Azure ML studio in a web browser.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
Update an endpoint.
The 'description', 'tags', and 'traffic' properties of an endpoint can be updated. In addition, new deployments can be added to an endpoint, and existing deployments can be updated.
az ml online-endpoint update --resource-group
--workspace-name
[--add]
[--file]
[--force-string]
[--local {false, true}]
[--mirror-traffic]
[--name]
[--no-wait]
[--remove]
[--set]
[--traffic]
[--web]
Update an endpoint from a YAML specification file
az ml online-endpoint update --file updated_endpoint.yml --resource-group my-resource-group --workspace-name my-workspace
Update the traffic settings for an endpoint
az ml online-endpoint update --name my-online-endpoint --traffic "my-new-deployment=100" --resource-group my-resource-group --workspace-name my-workspace
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Add an object to a list of objects by specifying a path and key value pairs. Example: --add property.listProperty <key=value, string or JSON string>
.
Local path to the YAML file containing the Azure ML online-endpoint specification. The YAML reference docs for online-endpoint can be found at: https://aka.ms/ml-cli-v2-endpoint-online-yaml-reference.
When using 'set' or 'add', preserve string literals instead of attempting to convert to JSON.
Update local endpoint.
Directs a duplicate percentage of live traffic to a train a deployment.
Name of the online endpoint.
Do not wait for the long-running-operation to finish. Default is False.
Remove a property or an element from a list. Example: --remove property.list <indexToRemove>
OR --remove propertyToRemove
.
Update an object by specifying a property path and value to set. Example: --set property1.property2=<value>
.
Space-separated key-value pairs, in quotes, for the traffic settings for the endpoint.
Show the endpoint's details in Azure ML studio in a web browser.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
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