Output Class

Define an output.

Inheritance

azure.ai.ml.entities._inputs_outputs.base._InputOutputBase

Output

Constructor

Output(*, type: str, path: str | None = None, mode: str | None = None, description: str | None = None, **kwargs: Any)

Keyword-Only Parameters

Name	Description
type	str The type of the data output. Accepted values are 'uri_folder', 'uri_file', 'mltable', 'mlflow_model', 'custom_model', and user-defined types. Defaults to 'uri_folder'. Default value: uri_folder
path	Optional[str] The remote path where the output should be stored.
mode	Optional[str] The access mode of the data output. Accepted values are 'rw_mount': Read-write mount the data 'upload': Upload the data from the compute target 'direct': Pass in the URI as a string
path_on_compute	Optional[str] The access path of the data output for compute
description	Optional[str] The description of the output.
name	str The name to be used to register the output as a Data or Model asset. A name can be set without setting a version.
version	str The version used to register the output as a Data or Model asset. A version can be set only when name is set.
is_control	bool Determine if the output is a control output.
early_available	bool Mark the output for early node orchestration.
intellectual_property	Union[ IntellectualProperty, dict] Intellectual property associated with the output. It can be an instance of IntellectualProperty or a dictionary that will be used to create an instance.

Examples

Creating a CommandJob with a folder output.

   from azure.ai.ml import Input, Output
   from azure.ai.ml.entities import CommandJob, CommandJobLimits

   command_job = CommandJob(
       code="./src",
       command="python train.py --ss {search_space.ss}",
       inputs={
           "input1": Input(path="trial.csv", mode="ro_mount", description="trial input data"),
           "input_2": Input(
               path="azureml:list_data_v2_test:2", type="uri_folder", description="registered data asset"
           ),
       },
       outputs={"default": Output(path="./foo")},
       compute="trial",
       environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
       limits=CommandJobLimits(timeout=120),
   )

Methods

get
has_key
items
keys
update
values

get

get(key: Any, default: Any | None = None) -> Any

Parameters

Name	Description
key Required
default	Default value: None

has_key

has_key(k: Any) -> bool

Parameters

Name	Description
k Required

items

items() -> list

keys

keys() -> list

update

update(*args: Any, **kwargs: Any) -> None

values

values() -> list