Dela via

Pipeline Class

Base class for pipeline node, used for pipeline component version consumption. You should not instantiate this class directly. Instead, you should use @pipeline decorator to create a pipeline node.

Constructor

Pipeline(*, component: Component | str, inputs: Dict[str, Input | str | bool | int | float | Enum] | None = None, outputs: Dict[str, str | Output] | None = None, settings: PipelineJobSettings | None = None, **kwargs: Any)

Parameters

Name Description
component
Required
Union[Component, str]

Id or instance of the pipeline component/job to be run for the step.
inputs
Required
Optional[Dict[str, Union[ <xref:azure.ai.ml.entities.Input>, str, bool, int, float, <xref:Enum>, <xref:"Input">]]]<xref:./>

Inputs of the pipeline node.
outputs
Required
Optional[Dict[str, Union[str, <xref:azure.ai.ml.entities.Output>, <xref:"Output">]]]

Outputs of the pipeline node.
settings
Required
Optional[PipelineJobSettings]

Setting of pipeline node, only taking effect for root pipeline job.

Keyword-Only Parameters

Name Description
component
Required
inputs
Default value: None
outputs
Default value: None
settings
Default value: None

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

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.

component

Id or instance of the pipeline component/job to be run for the step.

Returns

Type Description
Union[str, Component]

Id or instance of the pipeline component/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.

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.

settings

Settings of the pipeline.

Note: settings is available only when create node as a job. i.e. ml_client.jobs.create_or_update(node).

Returns

Type Description
PipelineJobSettings

Settings of the pipeline.

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.