CLI (v2) pipeline component YAML schema
APPLIES TO: 
Azure CLI ml extension v2 (current)
The source JSON schema can be found at https://azuremlschemas.azureedge.net/latest/pipelineComponent.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, including $schema at the top of your file enables you to invoke schema and resource completions. |
||
type |
const | The type of component. | pipeline |
pipeline |
name |
string | Required. Name of the component. Must start with lowercase letter. Allowed characters are lowercase letters, numbers, and underscore(_). Maximum length is 255 characters. | ||
version |
string | Version of the component. If omitted, Azure Machine Learning will autogenerate a version. | ||
display_name |
string | Display name of the component in the studio UI. It can be non-unique within the workspace. | ||
description |
string | Description of the component. | ||
tags |
object | Dictionary of tags for the component. | ||
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 is different from the unique job name of the child job. The value is the job specification, which can follow the command job schema or sweep job schema. Currently only command jobs and sweep jobs can be run in a pipeline. |
||
inputs |
object | Dictionary of inputs to the pipeline job. The key is a name for the input within the context of the job and the value is the input value. These pipeline inputs can be referenced by the inputs of an individual step job in the pipeline using the ${{ parent.inputs.<input_name> }} expression. For more information on how to bind the inputs of a pipeline step to the inputs of the top-level pipeline job, see the 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 component 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 and the value is the output configuration. These pipeline outputs can be referenced by the outputs of an individual step job in the pipeline using the ${{ parents.outputs.<output_name> }} expression. For more information on how to bind the inputs of a pipeline step to the inputs of the top-level pipeline job, see 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 which 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 the following template path: {settings.datastore}/azureml/{job-name}/{output-name}/. File(s) to the output directory will be written via read-write mount. If you want to specify a different mode for the output, provide an object containing the component output specification. |
Component input
| Key | Type | Description | Allowed values | Default value |
|---|---|---|---|---|
type |
string | Required. The type of component input. Learn more about data access | number, integer, boolean, string, uri_file, uri_folder, mltable, mlflow_model, custom_model |
|
description |
string | Description of the input. | ||
default |
number, integer, boolean, or string | The default value for the input. | ||
optional |
boolean | Whether the input is required. If set to true, you need use the command includes optional inputs with $[[]] |
false |
|
min |
integer or number | The minimum accepted value for the input. This field can only be specified if type field is number or integer. |
||
max |
integer or number | The maximum accepted value for the input. This field can only be specified if type field is number or integer. |
||
enum |
array | The list of allowed values for the input. Only applicable if type field is string. |
Component output
| Key | Type | Description | Allowed values | Default value |
|---|---|---|---|---|
type |
string | Required. The type of component output. | uri_file, uri_folder, mltable, mlflow_model, custom_model |
|
description |
string | Description of the output. |
Remarks
The az ml component commands can be used for managing Azure Machine Learning components.
Examples
Examples are available in the examples GitHub repository.