Parallel Class

  • Reference

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

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

Inheritance
azure.ai.ml.entities._builders.base_node.BaseNode
Parallel
azure.ai.ml.entities._job.pipeline._io.mixin.NodeWithGroupInputMixin
Parallel

Constructor

Parallel(*, component: ParallelComponent | str, compute: str | None = None, inputs: Dict[str, NodeOutput | Input | str | bool | int | float | Enum] | None = None, outputs: Dict[str, str | Output] | None = None, retry_settings: RetrySettings | Dict[str, str] | None = None, logging_level: str | None = None, max_concurrency_per_instance: int | None = None, error_threshold: int | None = None, mini_batch_error_threshold: int | None = None, input_data: str | None = None, task: ParallelTask | RunFunction | Dict | None = None, partition_keys: List | None = None, mini_batch_size: int | str | None = None, resources: JobResourceConfiguration | None = None, environment_variables: Dict | None = None, identity: Dict | ManagedIdentityConfiguration | AmlTokenConfiguration | UserIdentityConfiguration | None = None, **kwargs: Any)

Parameters

Name Description
component
Required
<xref:azure.ai.ml.entities._component.parallel_component.parallelComponent>

Id or instance of the parallel component/job to be run for the step
name
Required
str

Name of the parallel
description
Required
str

Description of the commad
tags
Required
dict[str, str]

Tag dictionary. Tags can be added, removed, and updated
properties
Required
dict[str, str]

The job property dictionary
display_name
Required
str

Display name of the job
retry_settings
Required
BatchRetrySettings

Parallel job run failed retry
logging_level
Required
str

A string of the logging level name
max_concurrency_per_instance
Required
int

The max parallellism that each compute instance has
error_threshold
Required
int

The number of item processing failures should be ignored
mini_batch_error_threshold
Required
int

The number of mini batch processing failures should be ignored
task
Required
ParallelTask

The parallel task
mini_batch_size
Required
str

For FileDataset input, this field is the number of files a user script can process in one run() call. For TabularDataset input, this field is the approximate size of data the user script can process in one run() call. Example values are 1024, 1024KB, 10MB, and 1GB. (optional, default value is 10 files for FileDataset and 1MB for TabularDataset.) This value could be set through PipelineParameter
partition_keys
Required
List

The keys used to partition dataset into mini-batches. If specified, the data with the same key will be partitioned into the same mini-batch. If both partition_keys and mini_batch_size are specified, the partition keys will take effect. The input(s) must be partitioned dataset(s), and the partition_keys must be a subset of the keys of every input dataset for this to work.
input_data
Required
str

The input data
inputs
Required
dict

Inputs of the component/job
outputs
Required
dict

Outputs of the component/job

Keyword-Only Parameters

Name Description
identity
Optional[Union[ dict[str, str], ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]]

The identity that the command job will use while running on compute.
component
Required
compute
Required
inputs
Required
outputs
Required
retry_settings
Required
logging_level
Required
max_concurrency_per_instance
Required
error_threshold
Required
mini_batch_error_threshold
Required
input_data
Required
task
Required
partition_keys
Required
mini_batch_size
Required
resources
Required
environment_variables
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.
set_resources

Set the resources for the parallel job.
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.

Keyword-Only Parameters

Name Description
kwargs
dict

Additional arguments to pass to the YAML serializer.

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()

set_resources

Set the resources for the parallel job.

set_resources(*, instance_type: str | List[str] | None = None, instance_count: int | None = None, properties: Dict | None = None, docker_args: str | None = None, shm_size: str | None = None, **kwargs: Any) -> None

Keyword-Only Parameters

Name Description
instance_type
Union[str, List[str]]

The instance type or a list of instance types used as supported by the compute target.
instance_count
int

The number of instances or nodes used by the compute target.
properties
dict

The property dictionary for the resources.
docker_args
str

Extra arguments to pass to the Docker run command.
shm_size
str

Size of the Docker container's shared memory block.

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.

component

Get the component of the parallel job.

Returns

Type Description
str,
ParallelComponent

The component of the parallel job.

creation_context

The creation context of the resource.

Returns

Type Description
Optional[SystemData]

The creation metadata for the resource.

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 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 job will use while running on compute.

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

Get the resource configuration for the parallel job.

Returns

Type Description
JobResourceConfiguration

The resource configuration for the parallel job.

retry_settings

Get the retry settings for the parallel job.

Returns

Type Description
RetrySettings

The retry settings for the parallel 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.

task

Get the parallel task.

Returns

Type Description
ParallelTask

The parallel task.

type

The type of the job.

Returns

Type Description
Optional[str]

The type of the job.