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Azure Machine Learning CLI (v2) release notes

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APPLIES TO: Azure CLI ml extension v2 (current)

In this article, learn about Azure Machine Learning CLI (v2) releases.

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2023-10-18

Azure Machine Learning CLI (v2) v2.21.1

2023-05-09

Azure Machine Learning CLI (v2) v2.16.0

  • az ml job connect-ssh

    • This command is marked as GA.

  • az ml job show-services

    • This command is marked as GA.

  • az ml model download

    • Fixed issue where, when downloading a model from a registry via the --registry-name argument, workspace_name was mandatory.

  • az ml model create

    • Add --stage(-s) flag to add the stage of the model.

  • az ml model update

    • Add --stage(-s) flag to update the stage of the model.

  • az ml model list

    • Add --stage(-s) flag to list by the stage of the model.

  • az ml workspace delete

    • Add --purge(-p) flag to force to purge instead of soft delete.

  • az ml workspace create

    • Add --enable-data-isolation(-e) flag to determine if a workspace has data isolation enabled.

2023-03-21

Azure Machine Learning CLI (v2) v2.15.0

  • az ml compute

    • Added --tags to create and update for Azure Machine Learning compute.

  • az ml data import

    • Support create a data asset version by first importing data from database and file_system to Azure cloud storage.

  • az ml data list-materialization-status

    • Support list status of data import materialization jobs that create data asset versions of <asset_name> via --name argument.

  • az ml online-deployment update

    • Added --skip-script-validation to create for Azure Machine Learning online deployment.

  • az ml workspace provision-network

    • Support to provision managed network for workspace

2023-02-03

Azure Machine Learning CLI (v2) v2.14.0

  • az ml compute

    • Added --location to create for Azure Machine Learning compute.
    • Added --enable-node-public-ip to create for Compute.

  • az ml data

    • Minor edits to help text

  • az ml data list

    • Added support for listing data assets in a registry via the --registry-name argument

  • az ml data show

    • Added support for showing a data asset in a registry via the --registry-name argument

  • az ml data create

    • Added support for creating a data asset in a registry via the --registry-name argument
    • Added support for promoting a data asset from a workspace to a registry

  • az ml workspace create

    • Added support for creating a workspace with a managed network with --managed-network argument

  • az ml workspace update

    • Added support for updating a workspace with a managed network with --managed-network argument

  • az ml compute connect-ssh

    • Added support for connecting to a compute instance via SSH

  • az ml workspace outbound-rule

    • Added support for listing a managed network's outbound rules for a workspace az ml workspace outbound-rule list
    • Added support for showing a managed network's outbound rules for a workspace az ml workspace outbound-rule show
    • Added support for removing a managed network's outbound rules for a workspace az ml workspace outbound-rule remove
    • Added support for creating or updating a managed network's outbound rules for a workspace az ml workspace outbound-rule set

2022-12-06

Azure Machine Learning CLI (v2) v2.12.0

  • Improved error message for az ml commands that are registry enabled, when neither workspace nor registry name is passed.
  • az ml compute
    • Fixed issue caused by no-wait parameter.

2022-11-08

Azure Machine Learning CLI (v2) v2.11.0

  • The CLI is depending on azure-ai-ml 1.1.0.
  • az ml registry
    • Added ml registry delete command.
    • Adjusted registry experimental tags and imports to avoid warning printouts for unrelated operations.
  • az ml environment
    • Prevented registering an already existing environment that references conda file.

2022-10-10

Azure Machine Learning CLI (v2) v2.10.0

  • The CLI is depending on GA version of azure-ai-ml.
  • Dropped support for Python 3.6.
  • az ml registry
    • New command group added to manage ML asset registries.
  • az ml job
    • Added az ml job show-services command.
    • Added model sweeping and hyperparameter tuning to AutoML NLP jobs.
  • az ml schedule
    • Added month_days property in recurrence schedule.
  • az ml compute
    • Added custom setup scripts support for compute instances.

2022-09-22

Azure Machine Learning CLI (v2) v2.8.0

  • az ml job
    • Added spark job support.
    • Added shm_size and docker_args to job.
  • az ml compute
    • Compute instance supports managed identity.
    • Added idle shutdown time support for compute instance.
  • az ml online-deployment
    • Added support for data collection for eventhub and data storage.
    • Added syntax validation for scoring script.
  • az ml batch-deployment
    • Added syntax validation for scoring script.

2022-08-10

Azure Machine Learning CLI (v2) v2.7.0

  • az ml component
    • Added AutoML component.
  • az ml dataset
    • Deprecated command group (Use az ml data instead).

2022-07-16

Azure Machine Learning CLI (v2) v2.6.0

  • Added MoonCake cloud support.
  • az ml job
    • Allow Git repo URLs to be used as code.
    • AutoML jobs use the same input schema as other job types.
    • Pipeline jobs now support registry assets.
  • az ml component
    • Allow Git repo URLs to be used as code.
  • az ml online-endpoint
    • MIR now supports registry assets.

2022-05-24

Azure Machine Learning CLI (v2) v2.4.0

  • The Azure Machine Learning CLI (v2) is now GA.
  • az ml job
    • The command group is marked as GA.
    • Added AutoML job type in public preview.
    • Added schedules property to pipeline job in public preview.
    • Added an option to list only archived jobs.
    • Improved reliability of az ml job download command.
  • az ml data
    • The command group is marked as GA.
    • Added MLTable data type in public preview.
    • Added an option to list only archived data assets.
  • az ml environment
    • Added an option to list only archived environments.
  • az ml model
    • The command group is marked as GA.
    • Allow models to be created from job outputs.
    • Added an option to list only archived models.
  • az ml online-deployment
    • The command group is marked as GA.
    • Removed timeout waiting for deployment creation.
    • Improved online deployment list view.
  • az ml online-endpoint
    • The command group is marked as GA.
    • Added mirror_traffic property to online endpoints in public preview.
    • Improved online endpoint list view.
  • az ml batch-deployment
    • The command group is marked as GA.
    • Added support for uri_file and uri_folder as invocation input.
    • Fixed a bug in batch deployment update.
    • Fixed a bug in batch deployment list-jobs output.
  • az ml batch-endpoint
    • The command group is marked as GA.
    • Added support for uri_file and uri_folder as invocation input.
    • Fixed a bug in batch endpoint update.
    • Fixed a bug in batch endpoint list-jobs output.
  • az ml component
    • The command group is marked as GA.
    • Added an option to list only archived components.
  • az ml code
    • This command group is removed.

2022-03-14

Azure Machine Learning CLI (v2) v2.2.1

  • az ml job
    • For all job types, flattened the code section of the YAML schema. Instead of code.local_path to specify the path to the source code directory, it's now just code
    • For all job types, changed the schema for defining data inputs to the job in the job YAML. Instead of specifying the data path using either the file or folder fields, use the path field to specify either a local path, a URI to a cloud path containing the data, or a reference to an existing registered Azure Machine Learning data asset via path: azureml:<data_name>:<data_version>. Also specify the type field to clarify whether the data source is a single file (uri_file) or a folder (uri_folder). If type field is omitted, it defaults to type: uri_folder. For more information, see the section of any of the job YAML references that discuss the schema for specifying input data.
    • In the sweep job YAML schema, changed the sampling_algorithm field from a string to an object in order to support more configurations for the random sampling algorithm type
    • Removed the component job YAML schema. With this release, if you want to run a command job inside a pipeline that uses a component, just specify the component to the component field of the command job YAML definition.
    • For all job types, added support for referencing the latest version of a nested asset in the job YAML configuration. When referencing a registered environment or data asset to use as input in a job, you can alias by latest version rather than having to explicitly specify the version. For example: environment: azureml:AzureML-Minimal@latest
    • For pipeline jobs, introduced the ${{ parent }} context for binding inputs and outputs between steps in a pipeline. For more information, see Expression syntax for binding inputs and outputs between steps in a pipeline job.
    • Added support for downloading named outputs of job via the --output-name argument for the az ml job download command
  • az ml data
    • Deprecated the az ml dataset subgroup, now using az ml data instead
    • There are two types of data that can now be created, either from a single file source (type: uri_file) or a folder (type: uri_folder). When creating the data asset, you can either specify the data source from a local file / folder or from a URI to a cloud path location. See the data YAML schema for the full schema
  • az ml environment
    • In the environment YAML schema, renamed the build.local_path field to build.path
    • Removed the build.context_uri field, the URI of the uploaded build context location will be accessible via build.path when the environment is returned
  • az ml model
    • In the model YAML schema, model_uri and local_path fields removed and consolidated to one path field that can take either a local path or a cloud path URI. model_format field renamed to type; the default type is custom_model, but you can specify one of the other types (mlflow_model, triton_model) to use the model in no-code deployment scenarios
    • For az ml model create, --model-uri and --local-path arguments removed and consolidated to one --path argument that can take either a local path or a cloud path URI
    • Added the az ml model download command to download a model's artifact files
  • az ml online-deployment
    • In the online deployment YAML schema, flattened the code section of the code_configuration field. Instead of code_configuration.code.local_path to specify the path to the source code directory containing the scoring files, it's now just code_configuration.code
    • Added an environment_variables field to the online deployment YAML schema to support configuring environment variables for an online deployment
  • az ml batch-deployment
    • In the batch deployment YAML schema, flattened the code section of the code_configuration field. Instead of code_configuration.code.local_path to specify the path to the source code directory containing the scoring files, it's now just code_configuration.code
  • az ml component
    • Flattened the code section of the command component YAML schema. Instead of code.local_path to specify the path to the source code directory, it's now just code
    • Added support for referencing the latest version of a registered environment to use in the component YAML configuration. When referencing a registered environment, you can alias by latest version rather than having to explicitly specify the version. For example: environment: azureml:AzureML-Minimal@latest
    • Renamed the component input and output type value from path to uri_folder for the type field when defining a component input or output
  • Removed the delete commands for assets (model, component, data, environment). The existing delete functionality is only a soft delete, so the delete commands will be reintroduced in a later release once hard delete is supported
  • Added support for archiving and restoring assets (model, component, data, environment) and jobs, for example, az ml model archive and az ml model restore. You can now archive assets and jobs, which will hide the archived entity from list queries (for example, az ml model list).

2021-10-04

Azure Machine Learning CLI (v2) v2.0.2

  • az ml workspace
  • az ml compute
    • Updated YAML schemas for AmlCompute and Compute Instance
    • Removed support for legacy AKS attach via az ml compute attach. Azure Arc-enabled Kubernetes attach will be supported in the next release
  • az ml datastore
  • az ml job
  • az ml environment
  • az ml model
    • Updated model YAML schema
    • Added new model_format property to Model for no-code deployment scenarios
  • az ml dataset
    • Renamed az ml data subgroup to az ml dataset
    • Updated dataset YAML schema
  • az ml component
    • Added the az ml component commands for managing Azure Machine Learning components
    • Added support for command components (command component YAML schema)
  • az ml online-endpoint
    • az ml endpoint subgroup split into two separate groups: az ml online-endpoint and az ml batch-endpoint
    • Updated online endpoint YAML schema
    • Added support for local endpoints for dev/test scenarios
    • Added interactive VS Code debugging support for local endpoints (added the --vscode-debug flag to az ml batch-endpoint create/update)
  • az ml online-deployment
    • az ml deployment subgroup split into two separate groups: az ml online-deployment and az ml batch-deployment
    • Updated managed online deployment YAML schema
    • Added autoscaling support via integration with Azure Monitor Autoscale
    • Added support for updating multiple online deployment properties in the same update operation
    • Added support for performing concurrent operations on deployments under the same endpoint
  • az ml batch-endpoint
    • az ml endpoint subgroup split into two separate groups: az ml online-endpoint and az ml batch-endpoint
    • Updated batch endpoint YAML schema
    • Removed traffic property; replaced with a configurable default deployment property
    • Added support for input data URIs for az ml batch-endpoint invoke
    • Added support for VNet ingress (private link)
  • az ml batch-deployment
    • az ml deployment subgroup split into two separate groups: az ml online-deployment and az ml batch-deployment
    • Updated batch deployment YAML schema

2021-05-25

Announcing the CLI (v2) for Azure Machine Learning

The ml extension to the Azure CLI is the next-generation interface for Azure Machine Learning. It enables you to train and deploy models from the command line, with features that accelerate scaling data science up and out while tracking the model lifecycle. Install and get started.