Create and run machine learning pipelines using components with the Azure Machine Learning CLI

APPLIES TO: Azure CLI ml extension v2 (current)

In this article, you learn how to create and run machine learning pipelines by using the Azure CLI and components (for more, see What is an Azure Machine Learning component?). You can create pipelines without using components, but components offer the greatest amount of flexibility and reuse. Azure Machine Learning Pipelines may be defined in YAML and run from the CLI, authored in Python, or composed in Azure Machine Learning Studio Designer with a drag-and-drop UI. This document focuses on the CLI.


Suggested pre-reading

Create your first pipeline with component

Let's create your first pipeline with component using an example. This section aims to give you an initial impression of what pipeline and component look like in Azure Machine Learning with a concrete example.

From the cli/jobs/pipelines-with-components/basics directory of the azureml-examples repository, navigate to the 3b_pipeline_with_data subdirector. There are three types of files in this directory. Those are the files you'll need to create when building your own pipeline.

  • pipeline.yml: This YAML file defines the machine learning pipeline. This YAML file describes how to break a full machine learning task into a multistep workflow. For example, considering a simple machine learning task of using historical data to train a sales forecasting model, you may want to build a sequential workflow with data processing, model training, and model evaluation steps. Each step is a component that has well defined interface and can be developed, tested, and optimized independently. The pipeline YAML also defines how the child steps connect to other steps in the pipeline, for example the model training step generate a model file and the model file will pass to a model evaluation step.

  • component.yml: This YAML file defines the component. It packages following information:

    • Metadata: name, display name, version, description, type etc. The metadata helps to describe and manage the component.
    • Interface: inputs and outputs. For example, a model training component will take training data and number of epochs as input, and generate a trained model file as output. Once the interface is defined, different teams can develop and test the component independently.
    • Command, code & environment: the command, code and environment to run the component. Command is the shell command to execute the component. Code usually refers to a source code directory. Environment could be an Azure Machine Learning environment(curated or customer created), docker image or conda environment.
  • component_src: This is the source code directory for a specific component. It contains the source code that will be executed in the component. You can use your preferred language(Python, R...). The code must be executed by a shell command. The source code can take a few inputs from shell command line to control how this step is going to be executed. For example, a training step may take training data, learning rate, number of epochs to control the training process. The argument of a shell command is used to pass inputs and outputs to the code.

Now let's create a pipeline using the 3b_pipeline_with_data example. We'll explain the detailed meaning of each file in following sections.

First list your available compute resources with the following command:

az ml compute list

If you don't have it, create a cluster called cpu-cluster by running:

az ml compute create -n cpu-cluster --type amlcompute --min-instances 0 --max-instances 10

Now, create a pipeline job defined in the pipeline.yml file with the following command. The compute target will be referenced in the pipeline.yml file as azureml:cpu-cluster. If your compute target uses a different name, remember to update it in the pipeline.yml file.

az ml job create --file pipeline.yml

You should receive a JSON dictionary with information about the pipeline job, including:

Key Description
name The GUID-based name of the job.
experiment_name The name under which jobs will be organized in Studio.
services.Studio.endpoint A URL for monitoring and reviewing the pipeline job.
status The status of the job. This will likely be Preparing at this point.

Open the services.Studio.endpoint URL you'll see a graph visualization of the pipeline looks like below.

Screenshot of a graph visualization of the pipeline.

Understand the pipeline definition YAML

Let's take a look at the pipeline definition in the 3b_pipeline_with_data/pipeline.yml file.

type: pipeline

display_name: 3b_pipeline_with_data
description: Pipeline with 3 component jobs with data dependencies

  default_compute: azureml:cpu-cluster

    mode: rw_mount

    type: command
    component: ./componentA.yml
        type: uri_folder
        path: ./data

        mode: rw_mount
    type: command
    component: ./componentB.yml
      component_b_input: ${{}}
        mode: rw_mount
    type: command
    component: ./componentC.yml
      component_c_input: ${{}}
      component_c_output: ${{parent.outputs.final_pipeline_output}}
      #  mode: upload

Below table describes the most common used fields of pipeline YAML schema. See full pipeline YAML schema here.

key description
type Required. Job type, must be pipeline for pipeline jobs.
display_name Display name of the pipeline job in Studio UI. Editable in Studio UI. Doesn't have to be unique across all jobs in the workspace.
jobs 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. In this release, supported job types in pipeline are command and sweep
inputs 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.
outputs 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.

In the 3b_pipeline_with_data example, we've created a three steps pipeline.

  • The three steps are defined under jobs. All three step type is command job. Each step's definition is in corresponding component.yml file. You can see the component YAML files under 3b_pipeline_with_data directory. We'll explain the componentA.yml in next section.
  • This pipeline has data dependency, which is common in most real world pipelines. Component_a takes data input from local folder under ./data(line 17-20) and passes its output to componentB (line 29). Component_a's output can be referenced as ${{}}.
  • The compute defines the default compute for this pipeline. If a component under jobs defines a different compute for this component, the system will respect component specific setting.

Screenshot of the pipeline with data example above.

Read and write data in pipeline

One common scenario is to read and write data in your pipeline. In Azure Machine Learning, we use the same schema to read and write data for all type of jobs (pipeline job, command job, and sweep job). Below are pipeline job examples of using data for common scenarios.

Understand the component definition YAML

Now let's look at the componentA.yml as an example to understand component definition YAML.

type: command

name: component_a
display_name: componentA
version: 1

    type: uri_folder

    type: uri_folder

code: ./componentA_src

  image: python

command: >-
  python --componentA_input ${{inputs.component_a_input}} --componentA_output ${{outputs.component_a_output}}

The most common used schema of the component YAML is described in below table. See full component YAML schema here.

key description
name Required. Name of the component. Must be unique across the Azure Machine Learning workspace. Must start with lowercase letter. Allow lowercase letters, numbers and underscore(_). Maximum length is 255 characters.
display_name Display name of the component in the studio UI. Can be non-unique within the workspace.
command Required the command to execute
code Local path to the source code directory to be uploaded and used for the component.
environment Required. The environment that will be used to execute the component.
inputs Dictionary of component inputs. The key is a name for the input within the context of the component and the value is the component input definition. Inputs can be referenced in the command using the ${{ inputs.<input_name> }} expression.
outputs Dictionary of component outputs. The key is a name for the output within the context of the component and the value is the component output definition. Outputs can be referenced in the command using the ${{ outputs.<output_name> }} expression.
is_deterministic Whether to reuse the previous job's result if the component inputs did not change. Default value is true, also known as reuse by default. The common scenario when set as false is to force reload data from a cloud storage or URL.

For the example in 3b_pipeline_with_data/componentA.yml, componentA has one data input and one data output, which can be connected to other steps in the parent pipeline. All the files under code section in component YAML will be uploaded to Azure Machine Learning when submitting the pipeline job. In this example, files under ./componentA_src will be uploaded (line 16 in componentA.yml). You can see the uploaded source code in Studio UI: double select the ComponentA step and navigate to Snapshot tab, as shown in below screenshot. We can see it's a hello-world script just doing some simple printing, and write current datetime to the componentA_output path. The component takes input and output through command line argument, and it's handled in the using argparse.

Screenshot of pipeline with data example above showing componentA.

Input and output

Input and output define the interface of a component. Input and output could be either of a literal value(of type string,number,integer, or boolean) or an object containing input schema.

Object input (of type uri_file, uri_folder,mltable,mlflow_model,custom_model) can connect to other steps in the parent pipeline job and hence pass data/model to other steps. In pipeline graph, the object type input will render as a connection dot.

Literal value inputs (string,number,integer,boolean) are the parameters you can pass to the component at run time. You can add default value of literal inputs under default field. For number and integer type, you can also add minimum and maximum value of the accepted value using min and max fields. If the input value exceeds the min and max, pipeline will fail at validation. Validation happens before you submit a pipeline job to save your time. Validation works for CLI, Python SDK and designer UI. Below screenshot shows a validation example in designer UI. Similarly, you can define allowed values in enum field.

Screenshot of the input and output of the train linear regression model component.

If you want to add an input to a component, remember to edit three places: 1)inputs field in component YAML 2) command field in component YAML. 3) component source code to handle the command line input. It's marked in green box in above screenshot.


Environment defines the environment to execute the component. It could be an Azure Machine Learning environment(curated or custom registered), docker image or conda environment. See examples below.

Register component for reuse and sharing

While some components will be specific to a particular pipeline, the real benefit of components comes from reuse and sharing. Register a component in your Machine Learning workspace to make it available for reuse. Registered components support automatic versioning so you can update the component but assure that pipelines that require an older version will continue to work.

In the azureml-examples repository, navigate to the cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components directory.

To register a component, use the az ml component create command:

az ml component create --file train.yml
az ml component create --file score.yml
az ml component create --file eval.yml

After these commands run to completion, you can see the components in Studio, under Asset -> Components:

Screenshot of Studio showing the components that were just registered.

Select a component. You'll see detailed information for each version of the component.

Under Details tab, you'll see basic information of the component like name, created by, version etc. You'll see editable fields for Tags and Description. The tags can be used for adding rapidly searched keywords. The description field supports Markdown formatting and should be used to describe your component's functionality and basic use.

Under Jobs tab, you'll see the history of all jobs that use this component.

Screenshot of the component tab showing 3 components.

Use registered components in a pipeline job YAML file

Let's use 1b_e2e_registered_components to demo how to use registered component in pipeline YAML. Navigate to 1b_e2e_registered_components directory, open the pipeline.yml file. The keys and values in the inputs and outputs fields are similar to those already discussed. The only significant difference is the value of the component field in the jobs.<JOB_NAME>.component entries. The component value is of the form azureml:<COMPONENT_NAME>:<COMPONENT_VERSION>. The train-job definition, for instance, specifies the latest version of the registered component my_train should be used:

type: command
component: azureml:my_train@latest
    type: uri_folder 
    path: ./data      
  max_epocs: ${{parent.inputs.pipeline_job_training_max_epocs}}
  learning_rate: ${{parent.inputs.pipeline_job_training_learning_rate}}
  learning_rate_schedule: ${{parent.inputs.pipeline_job_learning_rate_schedule}}
  model_output: ${{parent.outputs.pipeline_job_trained_model}}

Manage components

You can check component details and manage the component using CLI (v2). Use az ml component -h to get detailed instructions on component command. Below table lists all available commands. See more examples in Azure CLI reference

commands description
az ml component create Create a component
az ml component list List components in a workspace
az ml component show Show details of a component
az ml component update Update a component. Only a few fields(description, display_name) support update
az ml component archive Archive a component container
az ml component restore Restore an archived component

Next steps