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Spark Class

  • Reference

Base class for spark node, used for spark component version consumption.

You should not instantiate this class directly. Instead, you should create it from the builder function: spark.

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Inheritance
azure.ai.ml.entities._builders.base_node.BaseNode
Spark
azure.ai.ml.entities._job.spark_job_entry_mixin.SparkJobEntryMixin
Spark

Constructor

Spark(*, component: str | SparkComponent, identity: Dict | ManagedIdentityConfiguration | AmlTokenConfiguration | UserIdentityConfiguration | None = None, 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, conf: Dict[str, str] | None = None, inputs: Dict[str, NodeOutput | Input | str | bool | int | float | Enum] | None = None, outputs: Dict[str, str | Output] | None = None, compute: str | None = None, resources: Dict | SparkResourceConfiguration | None = None, entry: Dict[str, str] | SparkJobEntry | None = None, py_files: List[str] | None = None, jars: List[str] | None = None, files: List[str] | None = None, archives: List[str] | None = None, args: str | None = None, **kwargs: Any)

Parameters

Name Description
component
Required
Union[str, SparkComponent]

The ID or instance of the Spark component or job to be run during the step.
identity
Required
Union[Dict[str, str], ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration

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

The number of cores to use for the driver process, only in cluster mode.
driver_memory
Required
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").
executor_cores
Required
int

The number of cores to use on each executor.
executor_memory
Required
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").
executor_instances
Required
int

The initial number of executors.
dynamic_allocation_enabled
Required
bool

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

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

The upper bound for the number of executors if dynamic allocation is enabled.
conf
Required
Dict[str, str]

A dictionary with pre-defined Spark configurations key and values.
inputs
Required
Dict[str, Union[ str, bool, int, float, <xref:Enum>, <xref:azure.ai.ml.entities._job.pipeline._io.NodeOutput>, Input

A mapping of input names to input data sources used in the job.
outputs
Required
Dict[str, Union[str, Output]]

A mapping of output names to output data sources used in the job.
args
Required
str

The arguments for the job.
compute
Required
str

The compute resource the job runs on.
resources
Required
Union[Dict, SparkResourceConfiguration]

The compute resource configuration for the job.
entry
Required
Dict[str, str]

The file or class entry point.
py_files
Required
List[str]

The list of .zip, .egg or .py files to place on the PYTHONPATH for Python apps.
jars
Required
List[str]

The list of .JAR files to include on the driver and executor classpaths.
files
Required
List[str]

The list of files to be placed in the working directory of each executor.
archives
Required
List[str]

The list of archives to be extracted into the working directory of each executor.

Keyword-Only Parameters

Name Description
component
Required
identity
Required
driver_cores
Required
driver_memory
Required
executor_cores
Required
executor_memory
Required
executor_instances
Required
dynamic_allocation_enabled
Required
dynamic_allocation_min_executors
Required
dynamic_allocation_max_executors
Required
conf
Required
inputs
Required
outputs
Required
compute
Required
resources
Required
entry
Required
py_files
Required
jars
Required
files
Required
archives
Required
args
Required

Methods

clear
copy
dump

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

Create a new dictionary with keys from iterable and values set to value.
get

Return the value for key if key is in the dictionary, else default.
items
keys
pop

If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.
setdefault

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.
update

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values

clear 

clear() -> None.  Remove all items from D.

copy 

copy() -> a shallow copy of D

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.

fromkeys

Create a new dictionary with keys from iterable and values set to value.

fromkeys(value=None, /)

Positional-Only Parameters

Name Description
iterable
Required
value
Default value: None

Parameters

Name Description
type
Required

get

Return the value for key if key is in the dictionary, else default.

get(key, default=None, /)

Positional-Only Parameters

Name Description
key
Required
default
Default value: None

items 

items() -> a set-like object providing a view on D's items

keys 

keys() -> a set-like object providing a view on D's keys

pop

If the key is not found, return the default if given; otherwise, raise a KeyError.

pop(k, [d]) -> v, remove specified key and return the corresponding value.

popitem

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

popitem()

setdefault

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

setdefault(key, default=None, /)

Positional-Only Parameters

Name Description
key
Required
default
Default value: None

update

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

update([E], **F) -> None.  Update D from dict/iterable E and F.

values 

values() -> an object providing a view on D's values

Attributes

base_path

The base path of the resource.

Returns

Type Description
str

The base path of the resource.

code

The local or remote path pointing at source code.

Returns

Type Description
Union[str, <xref:PathLike>]

component

The ID or instance of the Spark component or job to be run during the step.

Returns

Type Description
SparkComponent

creation_context

The creation context of the resource.

Returns

Type Description
Optional[SystemData]

The creation metadata for the resource.

entry

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
Union[ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]

inputs

Get the inputs for the object.

Returns

Type Description
Dict[str, Union[Input, str, bool, int, float]]

A dictionary containing the inputs for the object.

log_files

Job output files.

Returns

Type Description
Optional[Dict[str, str]]

The dictionary of log names and URLs.

name

Get the name of the node.

Returns

Type Description
str

The name of the node.

outputs

Get the outputs of the object.

Returns

Type Description
Dict[str, Union[str, Output]]

A dictionary containing the outputs for the object.

resources

The compute resource configuration for the job.

Returns

Type Description
SparkResourceConfiguration

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)