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CLI (v2) Spark job YAML schema

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APPLIES TO: Azure CLI ml extension v2 (current)

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 Required. The type of job. spark
name string Name of the job. Must be unique across all jobs in the workspace. If omitted, Azure Machine Learning autogenerates a GUID for the name.
display_name string Display name of the job in the studio UI. Can be nonunique within the workspace. If omitted, Azure Machine Learning autogenerates a human-readable adjective-noun identifier for the display name.
experiment_name string Experiment name to organize the job under. 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 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.
code string Local path to the source code directory to be uploaded and used for the job.
code string Required. The location of the folder that contains source code and scripts for this job.
entry object Required. The entry point for the job. It could define a file.
entry.file string The location of the folder that contains source code and scripts for this job.
py_files object A list of .zip, .egg, or .py files, to be placed in the PYTHONPATH, for successful execution of the job.
jars object A list of .jar files to include on the Spark driver, and the executor CLASSPATH, for successful execution of the job.
files object A list of files that should be copied to the working directory of each executor, for successful job execution.
archives object A list of archives that should be extracted into the working directory of each executor, for successful job execution.
conf object The Spark driver and executor properties. See Attributes of the conf key
environment string or object The environment to use for the job. The environment 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, follow the Environment schema. Exclude the name and version properties, because inline environments don't support them.
args string The command line arguments that should be passed to the job entry point Python script. These arguments may contain the input data paths, the location to write the output, for example "--input_data ${{inputs.<input_name>}} --output_path ${{outputs.<output_name>}}"
resources object The resources to be used by an Azure Machine Learning serverless Spark compute. One of the compute or resources should be defined.
resources.instance_type string The compute instance type to be used for Spark pool. standard_e4s_v3, standard_e8s_v3, standard_e16s_v3, standard_e32s_v3, standard_e64s_v3.
resources.runtime_version string The Spark runtime version. 3.2, 3.3
compute string Name of the attached Synapse Spark pool to execute the job on. One of the compute or resources should be defined.
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 args 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 args using the ${{ outputs.<output_name> }} expression.
outputs.<output_name> object The Spark job output. Output for a Spark job can be written to either a file or a folder location by providing an object containing the job output specification.
identity object The identity is used for data accessing. It can be UserIdentityConfiguration, ManagedIdentityConfiguration or None. For UserIdentityConfiguration, the identity of job submitter is used to access the input data and write the result to the output folder. Otherwise, the appropriate identity is based on the Spark compute type.

Attributes of the conf key

Key Type Description Default value
spark.driver.cores integer The number of cores for the Spark driver.
spark.driver.memory string Allocated memory for the Spark driver, in gigabytes (GB); for example, 2g.
spark.executor.cores integer The number of cores for the Spark executor.
spark.executor.memory string Allocated memory for the Spark executor, in gigabytes (GB); for example, 2g.
spark.dynamicAllocation.enabled boolean Whether or not executors should be dynamically allocated, as a True or False value. If this property is set True, define spark.dynamicAllocation.minExecutors and spark.dynamicAllocation.maxExecutors. If this property is set to False, define spark.executor.instances. False
spark.dynamicAllocation.minExecutors integer The minimum number of Spark executors instances, for dynamic allocation.
spark.dynamicAllocation.maxExecutors integer The maximum number of Spark executors instances, for dynamic allocation.
spark.executor.instances integer The number of Spark executor instances.

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. Learn more about data access. uri_file, uri_folder
path string The path to the data to use as input. The URI of the input data, such as azureml://, abfss://, or wasbs:// can be used. For more information about using the azureml:// URI format, see Core yaml syntax.
mode string Mode of how the data should be delivered to the compute target. The direct mode passes in the storage location URL as the job input. You have full responsibility to handle storage access credentials. direct

Job outputs

Key Type Description Allowed values Default value
type string The type of job output. uri_file, uri_folder
path string The URI of the input data, such as azureml://, abfss://, or wasbs://.
mode string Mode of output file(s) delivery to the destination storage resource. direct

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

Remarks

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

Examples

See examples at examples GitHub repository. Several are shown next.

YAML: A standalone Spark job using attached Synapse Spark pool and managed identity

# attached-spark-standalone-managed-identity.yaml
$schema: https://azuremlschemas.azureedge.net/latest/sparkJob.schema.json
type: spark

code: ./src
entry:
  file: titanic.py

conf:
  spark.driver.cores: 1
  spark.driver.memory: 2g
  spark.executor.cores: 2
  spark.executor.memory: 2g
  spark.executor.instances: 2

inputs:
  titanic_data:
    type: uri_file
    path: azureml://datastores/workspaceblobstore/paths/data/titanic.csv
    mode: direct

outputs:
  wrangled_data:
    type: uri_folder
    path: azureml://datastores/workspaceblobstore/paths/data/wrangled/
    mode: direct

args: >-
  --titanic_data ${{inputs.titanic_data}}
  --wrangled_data ${{outputs.wrangled_data}}

identity:
  type: managed

compute: <ATTACHED_SPARK_POOL_NAME>

YAML: A standalone Spark job using serverless Spark compute and user identity

Next steps