Muokkaa

Jaa

CLI (v2) pipeline job YAML schema

  • Artikkeli

APPLIES TO: Azure CLI ml extension v2 (current)

You can find the source JSON schema at https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json.

Note

The YAML syntax detailed in this document is based on the JSON schema for the latest version of the ML CLI v2 extension. This syntax is guaranteed only to work with the latest version of the ML CLI v2 extension. You can find the schemas for older extension versions at https://azuremlschemasprod.azureedge.net/.

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, you can invoke schema and resource completions if you include $schema at the top of your file.
type const Required. The type of job. pipeline
name string Name of the job. Must be unique across all jobs in the workspace. If omitted, Azure Machine Learning will autogenerate a GUID for the name.
display_name string Display name of the job in the studio UI. Can be non-unique within the workspace. If omitted, Azure Machine Learning autogenerates a human-readable adjective-noun identifier for the display name.
experiment_name string Organize the job under the experiment name. The run record of each job is organized under the corresponding experiment in the "Experiments" tab of the studio. If omitted, Azure Machine Learning defaults experiment_name to the name of the working directory where the job was created.
tags object Dictionary of tags for the job.
settings object Default settings for the pipeline job. Visit Attributes of the settings key for the set of configurable properties.
jobs object Required. Dictionary of the set of individual jobs to run as steps within the pipeline. These jobs are considered child jobs of the parent pipeline job.

The key is the name of the step within the context of the pipeline job. This name differs from the unique job name of the child job. The value is the job specification, which can follow the command job schema or the sweep job schema. Currently, only command jobs and sweep jobs can be run in a pipeline. Later releases will have support for other job types.
inputs object Dictionary of inputs to the pipeline job. The key is a name for the input within the context of the job. The value is the input value.

The inputs of an individual step job in the pipeline can reference these pipeline inputs with the ${{ parent.inputs.<input_name> }} expression. For more information about binding the inputs of a pipeline step to the inputs of the top-level pipeline job, visit Expression syntax for binding inputs and outputs between steps in a pipeline job.
inputs.<input_name> number, integer, boolean, string or object One of a literal value (of type number, integer, boolean, or string) or an object containing a job input data specification.
outputs object Dictionary of output configurations of the pipeline job. The key is a name for the output within the context of the job. The value is the output configuration.

The outputs of an individual step job in the pipeline can reference these pipeline outputs with the ${{ parents.outputs.<output_name> }} expression. For more information about binding the outputs of a pipeline step to the outputs of the top-level pipeline job, visit the Expression syntax for binding inputs and outputs between steps in a pipeline job.
outputs.<output_name> object You can leave the object empty. In this case, by default, the output will be of type uri_folder, and Azure Machine Learning will system-generate an output location for the output based on this templatized path: {settings.datastore}/azureml/{job-name}/{output-name}/. File(s) to the output directory will be written via a read-write mount. To specify a different output mode, provide an object that contains the job output specification.
identity object The identity is used for data accessing. It can be UserIdentityConfiguration, ManagedIdentityConfiguration or None. If it's UserIdentityConfiguration, the identity of job submitter will be used to access input data and write result to output folder, otherwise the managed identity of the compute target will be used.

Attributes of the settings key

Key Type Description Default value
default_datastore string Name of the datastore to use as the default datastore for the pipeline job. This value must be a reference to an existing datastore in the workspace, using the azureml:<datastore-name> syntax. Any outputs defined in the outputs property of the parent pipeline job or child step jobs are stored in this datastore. If omitted, outputs are stored in the workspace blob datastore.
default_compute string Name of the compute target to use as the default compute for all the steps in the pipeline. Compute defined at the step level overrides this default compute for that specific step. The default_compute value must be a reference to an existing compute in the workspace, using the azureml:<compute-name> syntax.
continue_on_step_failure boolean This setting determines what happens if a step in the pipeline fails. By default, the pipeline will continue to run even if one step fails. This means that any steps that don't depend on the failed step will still execute. However, if you change this setting to False, the entire pipeline stops running and any currently running steps will be canceled if one step fails. True
force_rerun boolean Whether to force rerun the whole pipeline. The default value is False. This means that by default, the pipeline tries to reuse the output of the previous job if it meets reuse criteria. If set as True, all steps in the pipeline will rerun. False

Job inputs

Key Type Description Allowed values Default value
type string The type of job input. Specify uri_file for input data that points to a single file source, or uri_folder for input data that points to a folder source. For more information, visit Learn more about data access. uri_file, uri_folder, mltable, mlflow_model uri_folder
path string The path to the data to use as input. This can be specified in a few ways:

- A local path to the data source file or folder, e.g. path: ./iris.csv. The data uploads during job submission.

- A URI of a cloud path to the file or folder to use as the input. Supported URI types are azureml, https, wasbs, abfss, adl. For more information about the use of the azureml:// URI format, visit Core yaml syntax.

- An existing registered Azure Machine Learning data asset to use as the input. To reference a registered data asset, use the azureml:<data_name>:<data_version> syntax or azureml:<data_name>@latest (to reference the latest version of that data asset), e.g. path: azureml:cifar10-data:1 or path: azureml:cifar10-data@latest.
mode string Mode of how the data should be delivered to the compute target.

For read-only mount (ro_mount), the data will be consumed as a mount path. A folder is mounted as a folder and a file is mounted as a file. Azure Machine Learning resolves the input to the mount path.

For download mode, the data is downloaded to the compute target. Azure Machine Learning resolves the input to the downloaded path.

For just the URL of the storage location of the data artifact or artifacts, instead of mounting or downloading the data itself, use the direct mode. This passes in the URL of the storage location as the job input. In this case, you're fully responsible for handling credentials to access the storage.		 ro_mount, download, direct ro_mount

Job outputs

Key Type Description Allowed values Default value
type string The job output type. For the default uri_folder type, the output corresponds to a folder. uri_file, uri_folder, mltable, mlflow_model uri_folder
mode string Mode of the delivery of the output file or files to the destination storage. For read-write mount mode (rw_mount), the output directory will be a mounted directory. For the upload mode, the file(s) written are uploaded at the end of the job. rw_mount, upload rw_mount

Identity configurations

UserIdentityConfiguration

Key Type Description Allowed values
type const Required. Identity type. user_identity

ManagedIdentityConfiguration

Key Type Description Allowed values
type const Required. Identity type. managed or managed_identity

Remarks

You can use the az ml job command to manage Azure Machine Learning jobs.

Examples

Visit the examples GitHub repository for examples. Several are shown here:

YAML: hello pipeline

$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline
display_name: hello_pipeline
jobs:
  hello_job:
    command: echo "hello"
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
    compute: azureml:cpu-cluster
  world_job:
    command: echo "world"
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
    compute: azureml:cpu-cluster

YAML: input/output dependency

$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline
display_name: hello_pipeline_io
jobs:
  hello_job:
    command: echo "hello" && echo "world" > ${{outputs.world_output}}/world.txt
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
    compute: azureml:cpu-cluster
    outputs:
      world_output:
  world_job:
    command: cat ${{inputs.world_input}}/world.txt
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
    compute: azureml:cpu-cluster
    inputs:
      world_input: ${{parent.jobs.hello_job.outputs.world_output}}

YAML: common pipeline job settings

$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline
display_name: hello_pipeline_settings

settings:
  default_datastore: azureml:workspaceblobstore
  default_compute: azureml:cpu-cluster
jobs:
  hello_job:
    command: echo 202204190 & echo "hello"
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
  world_job:
    command: echo 202204190 & echo "hello"
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest

YAML: top-level input and overriding common pipeline job settings

$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline
display_name: hello_pipeline_abc
settings:
    default_compute: azureml:cpu-cluster
  
inputs:
  hello_string_top_level_input: "hello world"
jobs:
  a:
    command: echo hello ${{inputs.hello_string}}
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
    inputs:
      hello_string: ${{parent.inputs.hello_string_top_level_input}}
  b:
    command: echo "world" >> ${{outputs.world_output}}/world.txt
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
    outputs:
      world_output:
  c:
    command: echo ${{inputs.world_input}}/world.txt
    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
    inputs:
      world_input: ${{parent.jobs.b.outputs.world_output}}

Next steps