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SparkJob Class

A standalone Spark job.

Constructor

SparkJob(*, driver_cores: int | str | None = None, driver_memory: str | None = None, executor_cores: int | str | None = None, executor_memory: str | None = None, executor_instances: int | str | None = None, dynamic_allocation_enabled: bool | str | None = None, dynamic_allocation_min_executors: int | str | None = None, dynamic_allocation_max_executors: int | str | None = None, inputs: Dict[str, Input | str | bool | int | float] | None = None, outputs: Dict[str, Output] | None = None, compute: str | None = None, identity: Dict[str, str] | ManagedIdentityConfiguration | AmlTokenConfiguration | UserIdentityConfiguration | None = None, resources: Dict | SparkResourceConfiguration | None = None, **kwargs: Any)

Keyword-Only Parameters

Name Description
driver_cores
Optional[int]

The number of cores to use for the driver process, only in cluster mode.

Default value: None
driver_memory
Optional[str]

The amount of memory to use for the driver process, formatted as strings with a size unit suffix ("k", "m", "g" or "t") (e.g. "512m", "2g").

Default value: None
executor_cores
Optional[int]

The number of cores to use on each executor.

Default value: None
executor_memory
Optional[str]

The amount of memory to use per executor process, formatted as strings with a size unit suffix ("k", "m", "g" or "t") (e.g. "512m", "2g").

Default value: None
executor_instances
Optional[int]

The initial number of executors.

Default value: None
dynamic_allocation_enabled
Optional[bool]

Whether to use dynamic resource allocation, which scales the number of executors registered with this application up and down based on the workload.

Default value: None
dynamic_allocation_min_executors
Optional[int]

The lower bound for the number of executors if dynamic allocation is enabled.

Default value: None
dynamic_allocation_max_executors
Optional[int]

The upper bound for the number of executors if dynamic allocation is enabled.

Default value: None
inputs
Optional[dict[str, Input]]

The mapping of input data bindings used in the job.

Default value: None
outputs
Optional[dict[str, Output]]

The mapping of output data bindings used in the job.

Default value: None
compute
Optional[str]

The compute resource the job runs on.

Default value: None
identity
Optional[Union[dict[str, str], <xref:azure.ai.ml.ManagedIdentityConfiguration>, <xref:azure.ai.ml.AmlTokenConfiguration>, <xref:azure.ai.ml.UserIdentityConfiguration>]]

The identity that the Spark job will use while running on compute.

Default value: None
resources
Default value: None

Examples

Configuring a SparkJob.


   from azure.ai.ml import Input, Output
   from azure.ai.ml.entities import SparkJob

   spark_job = SparkJob(
       code="./sdk/ml/azure-ai-ml/tests/test_configs/dsl_pipeline/spark_job_in_pipeline/basic_src",
       entry={"file": "sampleword.py"},
       conf={
           "spark.driver.cores": 2,
           "spark.driver.memory": "1g",
           "spark.executor.cores": 1,
           "spark.executor.memory": "1g",
           "spark.executor.instances": 1,
       },
       environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
       inputs={
           "input1": Input(
               type="uri_file", path="azureml://datastores/workspaceblobstore/paths/python/data.csv", mode="direct"
           )
       },
       compute="synapsecompute",
       outputs={"component_out_path": Output(type="uri_folder")},
       args="--input1 ${{inputs.input1}} --output2 ${{outputs.output1}} --my_sample_rate ${{inputs.sample_rate}}",
   )

Methods

dump

Dumps the job content into a file in YAML format.
filter_conf_fields

Filters out the fields of the conf attribute that are not among the Spark configuration fields listed in ~azure.ai.ml._schema.job.parameterized_spark.CONF_KEY_MAP and returns them in their own dictionary.

dump

Dumps the job content into a file in YAML format.

dump(dest: str | PathLike | IO, **kwargs: Any) -> None

Parameters

Name Description
dest
Required
Union[<xref:PathLike>, str, IO[AnyStr]]

The local path or file stream to write the YAML content to. If dest is a file path, a new file will be created. If dest is an open file, the file will be written to directly.

Exceptions

Type Description
FileExistsError

Raised if dest is a file path and the file already exists.
IOError

Raised if dest is an open file and the file is not writable.

filter_conf_fields

Filters out the fields of the conf attribute that are not among the Spark configuration fields listed in ~azure.ai.ml._schema.job.parameterized_spark.CONF_KEY_MAP and returns them in their own dictionary.

filter_conf_fields() -> Dict[str, str]

Returns

Type Description
dict[str, str]

A dictionary of the conf fields that are not Spark configuration fields.

Attributes

base_path

The base path of the resource.

Returns

Type Description
str

The base path of the resource.

creation_context

The creation context of the resource.

Returns

Type Description
Optional[SystemData]

The creation metadata for the resource.

entry

environment

The Azure ML environment to run the Spark component or job in.

Returns

Type Description
Optional[Union[str, Environment]]

The Azure ML environment to run the Spark component or job in.

id

The resource ID.

Returns

Type Description
Optional[str]

The global ID of the resource, an Azure Resource Manager (ARM) ID.

identity

The identity that the Spark job will use while running on compute.

Returns

Type Description
Optional[Union[<xref:azure.ai.ml.ManagedIdentityConfiguration>, <xref:azure.ai.ml.AmlTokenConfiguration>, <xref:azure.ai.ml.UserIdentityConfiguration>]]

The identity that the Spark job will use while running on compute.

inputs

log_files

Job output files.

Returns

Type Description
Optional[Dict[str, str]]

The dictionary of log names and URLs.

outputs

resources

The compute resource configuration for the job.

Returns

Type Description
Optional[SparkResourceConfiguration]

The compute resource configuration for the job.

status

The status of the job.

Common values returned include "Running", "Completed", and "Failed". All possible values are:

  • NotStarted - This is a temporary state that client-side Run objects are in before cloud submission.

  • Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.

  • Provisioning - On-demand compute is being created for a given job submission.

  • Preparing - The run environment is being prepared and is in one of two stages:

    • Docker image build

    • conda environment setup

  • Queued - The job is queued on the compute target. For example, in BatchAI, the job is in a queued state

    while waiting for all the requested nodes to be ready.

  • Running - The job has started to run on the compute target.

  • Finalizing - User code execution has completed, and the run is in post-processing stages.

  • CancelRequested - Cancellation has been requested for the job.

  • Completed - The run has completed successfully. This includes both the user code execution and run

    post-processing stages.

  • Failed - The run failed. Usually the Error property on a run will provide details as to why.

  • Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.

  • NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.

Returns

Type Description
Optional[str]

Status of the job.

studio_url

Azure ML studio endpoint.

Returns

Type Description
Optional[str]

The URL to the job details page.

type

The type of the job.

Returns

Type Description
Optional[str]

The type of the job.

CODE_ID_RE_PATTERN 

CODE_ID_RE_PATTERN = re.compile('\\/subscriptions\\/(?P<subscription>[\\w,-]+)\\/resourceGroups\\/(?P<resource_group>[\\w,-]+)\\/providers\\/Microsoft\\.MachineLearningServices\\/workspaces\\/(?P<workspace>[\\w,-]+)\\/codes\\/(?P<co)