CLI (v2) command job YAML schema

APPLIES TO: Azure CLI ml extension v2 (current)

The source JSON schema can be found at https://azuremlschemas.azureedge.net/latest/commandJob.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 job. command command
name string Name of the job. Must be unique across all jobs in the workspace. If omitted, Azure ML 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 ML will autogenerate a human-readable adjective-noun identifier for the display name.
experiment_name string Experiment name to organize the job under. Each job's run record will be organized under the corresponding experiment in the studio's "Experiments" tab. If omitted, Azure ML will default it to the name of the working directory where the job was created.
description string Description of the job.
tags object Dictionary of tags for the job.
command string Required (if not using component field). The command to execute.
code string Local path to the source code directory to be uploaded and used for the job.
environment string or object Required (if not using component field). The environment to use for the job. This can be either a reference to an existing versioned environment in the workspace or an inline environment specification.

To reference an existing environment use the azureml:<environment_name>:<environment_version> syntax or azureml:<environment_name>@latest (to reference the latest version of an environment).

To define an environment inline please follow the Environment schema. Exclude the name and version properties as they are not supported for inline environments.
environment_variables object Dictionary of environment variable key-value pairs to set on the process where the command is executed.
distribution object The distribution configuration for distributed training scenarios. One of MpiConfiguration, PyTorchConfiguration, or TensorFlowConfiguration.
compute string Name of the compute target to execute the job on. This can be either a reference to an existing compute in the workspace (using the azureml:<compute_name> syntax) or local to designate local execution. Note: jobs in pipeline didn't support local as compute local
resources.instance_count integer The number of nodes to use for the job. 1
resources.instance_type string The instance type to use for the job. Applicable for jobs running on Azure Arc-enabled Kubernetes compute (where the compute target specified in the compute field is of type: kubernentes). If omitted, this will default to the default instance type for the Kubernetes cluster. For more information, see Create and select Kubernetes instance types.
limits.timeout integer The maximum time in seconds the job is allowed to run. Once this limit is reached the system will cancel the job.
inputs object Dictionary of inputs to the job. The key is a name for the input within the context of the job and the value is the input value.

Inputs can be referenced in the command using the ${{ inputs.<input_name> }} expression.
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 job. The key is a name for the output within the context of the job and the value is the output configuration.

Outputs can be referenced in the command using the ${{ outputs.<output_name> }} expression.
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 ML will system-generate an output location for the output. 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 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.

Distribution configurations

MpiConfiguration

Key Type Description Allowed values
type const Required. Distribution type. mpi
process_count_per_instance integer Required. The number of processes per node to launch for the job.

PyTorchConfiguration

Key Type Description Allowed values Default value
type const Required. Distribution type. pytorch
process_count_per_instance integer The number of processes per node to launch for the job. 1

TensorFlowConfiguration

Key Type Description Allowed values Default value
type const Required. Distribution type. tensorflow
worker_count integer The number of workers to launch for the job. Defaults to resources.instance_count.
parameter_server_count integer The number of parameter servers to launch for the job. 0

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. uri_file, uri_folder, mlflow_model, custom_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 will get uploaded 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. See Core yaml syntax for more information on how to use the azureml:// URI format.

- An existing registered Azure ML 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 will be mounted as a folder and a file will be mounted as a file. Azure ML will resolve the input to the mount path.

For download mode the data will be downloaded to the compute target. Azure ML will resolve the input to the downloaded path.

If you only want the URL of the storage location of the data artifact(s) rather than mounting or downloading the data itself, you can use the direct mode. This will pass in the URL of the storage location as the job input. Note that in this case you are 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 type of job output. For the default uri_folder type, the output will correspond to a folder. uri_folder , mlflow_model, custom_model uri_folder
mode string Mode of how output file(s) will get delivered to the destination storage. For read-write mount mode (rw_mount) the output directory will be a mounted directory. For upload mode the file(s) written will get 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

The az ml job command can be used for managing Azure Machine Learning jobs.

Examples

Examples are available in the examples GitHub repository. Several are shown below.

YAML: hello world

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo "hello world"
environment:
  image: library/python:latest
compute: azureml:cpu-cluster

YAML: display name, experiment name, description, and tags

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo "hello world"
environment:
  image: library/python:latest
compute: azureml:cpu-cluster
tags:
  hello: world
display_name: hello-world-example
experiment_name: hello-world-example
description: |
  # Azure Machine Learning "hello world" job

  This is a "hello world" job running in the cloud via Azure Machine Learning!

  ## Description

  Markdown is supported in the studio for job descriptions! You can edit the description there or via CLI.

YAML: environment variables

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo $hello_env_var
environment:
  image: library/python:latest
compute: azureml:cpu-cluster
environment_variables:
  hello_env_var: "hello world"

YAML: source code

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: ls
code: src
environment:
  image: library/python:latest
compute: azureml:cpu-cluster

YAML: literal inputs

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  echo ${{inputs.hello_string}}
  echo ${{inputs.hello_number}}
environment:
  image: library/python:latest
inputs:
  hello_string: "hello world"
  hello_number: 42
compute: azureml:cpu-cluster

YAML: write to default outputs

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo "hello world" > ./outputs/helloworld.txt
environment:
  image: library/python:latest
compute: azureml:cpu-cluster

YAML: write to named data output

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo "hello world" > ${{outputs.hello_output}}/helloworld.txt
outputs:
  hello_output:
environment:
  image: python
compute: azureml:cpu-cluster

YAML: datastore URI file input

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  echo "--iris-csv: ${{inputs.iris_csv}}"
  python hello-iris.py --iris-csv ${{inputs.iris_csv}}
code: src
inputs:
  iris_csv:
    type: uri_file 
    path: azureml://datastores/workspaceblobstore/paths/example-data/iris.csv
environment: azureml:AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest
compute: azureml:cpu-cluster

YAML: datastore URI folder input

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  ls ${{inputs.data_dir}}
  echo "--iris-csv: ${{inputs.data_dir}}/iris.csv"
  python hello-iris.py --iris-csv ${{inputs.data_dir}}/iris.csv
code: src
inputs:
  data_dir:
    type: uri_folder 
    path: azureml://datastores/workspaceblobstore/paths/example-data/
environment: azureml:AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest
compute: azureml:cpu-cluster

YAML: URI file input

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  echo "--iris-csv: ${{inputs.iris_csv}}"
  python hello-iris.py --iris-csv ${{inputs.iris_csv}}
code: src
inputs:
  iris_csv:
    type: uri_file 
    path: https://azuremlexamples.blob.core.windows.net/datasets/iris.csv
environment: azureml:AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest
compute: azureml:cpu-cluster

YAML: URI folder input

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  ls ${{inputs.data_dir}}
  echo "--iris-csv: ${{inputs.data_dir}}/iris.csv"
  python hello-iris.py --iris-csv ${{inputs.data_dir}}/iris.csv
code: src
inputs:
  data_dir:
    type: uri_folder 
    path: wasbs://datasets@azuremlexamples.blob.core.windows.net/
environment: azureml:AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest
compute: azureml:cpu-cluster

YAML: Notebook via papermill

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  pip install ipykernel papermill
  papermill hello-notebook.ipynb outputs/out.ipynb -k python
code: src
environment:
  image: library/python:latest
compute: azureml:cpu-cluster

YAML: basic Python model training

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
code: src
command: >-
  python main.py 
  --iris-csv ${{inputs.iris_csv}}
  --C ${{inputs.C}}
  --kernel ${{inputs.kernel}}
  --coef0 ${{inputs.coef0}}
inputs:
  iris_csv: 
    type: uri_file
    path: wasbs://datasets@azuremlexamples.blob.core.windows.net/iris.csv
  C: 0.8
  kernel: "rbf"
  coef0: 0.1
environment: azureml:AzureML-sklearn-0.24-ubuntu18.04-py37-cpu@latest
compute: azureml:cpu-cluster
display_name: sklearn-iris-example
experiment_name: sklearn-iris-example
description: Train a scikit-learn SVM on the Iris dataset.

YAML: basic R model training with local Docker build context

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: >
  Rscript train.R 
  --data_folder ${{inputs.iris}}
code: src
inputs:
  iris: 
    type: uri_file
    path: https://azuremlexamples.blob.core.windows.net/datasets/iris.csv
environment:
  build:
    path: docker-context
compute: azureml:cpu-cluster
display_name: r-iris-example
experiment_name: r-iris-example
description: Train an R model on the Iris dataset.

YAML: distributed PyTorch

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
code: src
command: >-
  python train.py 
  --epochs ${{inputs.epochs}}
  --learning-rate ${{inputs.learning_rate}}
  --data-dir ${{inputs.cifar}}
inputs:
  epochs: 1
  learning_rate: 0.2
  cifar: 
     type: uri_folder
     path: azureml:cifar-10-example@latest
environment: azureml:AzureML-pytorch-1.9-ubuntu18.04-py37-cuda11-gpu@latest
compute: azureml:gpu-cluster
distribution:
  type: pytorch 
  process_count_per_instance: 1
resources:
  instance_count: 2
display_name: pytorch-cifar-distributed-example
experiment_name: pytorch-cifar-distributed-example
description: Train a basic convolutional neural network (CNN) with PyTorch on the CIFAR-10 dataset, distributed via PyTorch.

YAML: distributed TensorFlow

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
code: src
command: >-
  python train.py 
  --epochs ${{inputs.epochs}}
  --model-dir ${{inputs.model_dir}}
inputs:
  epochs: 1
  model_dir: outputs/keras-model
environment: azureml:AzureML-tensorflow-2.4-ubuntu18.04-py37-cuda11-gpu@latest
compute: azureml:gpu-cluster
resources:
  instance_count: 2
distribution:
  type: tensorflow
  worker_count: 2
display_name: tensorflow-mnist-distributed-example
experiment_name: tensorflow-mnist-distributed-example
description: Train a basic neural network with TensorFlow on the MNIST dataset, distributed via TensorFlow.

YAML: distributed MPI

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
code: src
command: >-
  python train.py
  --epochs ${{inputs.epochs}}
inputs:
  epochs: 1
environment: azureml:AzureML-tensorflow-2.7-ubuntu20.04-py38-cuda11-gpu@latest
compute: azureml:gpu-cluster
resources:
  instance_count: 2
distribution:
  type: mpi
  process_count_per_instance: 2
display_name: tensorflow-mnist-distributed-horovod-example
experiment_name: tensorflow-mnist-distributed-horovod-example
description: Train a basic neural network with TensorFlow on the MNIST dataset, distributed via Horovod.

Next steps