DataTransferImport Class

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Base class for data transfer import node.

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

Inheritance

azure.ai.ml.entities._builders.data_transfer.DataTransfer

DataTransferImport

Constructor

DataTransferImport(*, component: str | DataTransferImportComponent, compute: str | None = None, source: Dict | Database | FileSystem | None = None, outputs: Dict[str, str | Output] | None = None, **kwargs: Any)

Parameters

Name	Description
component Required	str Id of the data transfer built in component to be run for the step
source Required	Union[Dict, Database, FileSystem] The data source of file system or database
outputs Required	Dict[str, Union[str, Output, dict]] Mapping of output data bindings used in the job.
name Required	str Name of the data transfer.
description Required	str Description of the data transfer.
tags Required	dict[str, str] Tag dictionary. Tags can be added, removed, and updated.
display_name Required	str Display name of the job.
experiment_name Required	str Name of the experiment the job will be created under, if None is provided, default will be set to current directory name.
compute Required	str The compute target the job runs on.

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.

component

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.

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.