Input Class

Initialize an Input object.

Inheritance

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

Input

Constructor

Input(*, type: str, path: str | None = None, mode: str | None = None, optional: bool | None = None, description: str | None = None, **kwargs: Any)

Keyword-Only Parameters

Name	Description
type	str The type of the data input. Accepted values are 'uri_folder', 'uri_file', 'mltable', 'mlflow_model', 'custom_model', 'integer', 'number', 'string', and 'boolean'. Defaults to 'uri_folder'. default value: uri_folder
path	Optional[str] The path to the input data. Paths can be local paths, remote data uris, or a registered AzureML asset ID.
mode	Optional[str] The access mode of the data input. Accepted values are: 'ro_mount': Mount the data to the compute target as read-only, 'download': Download the data to the compute target, 'direct': Pass in the URI as a string to be accessed at runtime
default	Union[str, int, float, bool] The default value of the input. If a default is set, the input data will be optional.
min	Union[int, float] The minimum value for the input. If a value smaller than the minimum is passed to the job, the job execution will fail.
max	Union[int, float] The maximum value for the input. If a value larger than the maximum is passed to a job, the job execution will fail.
optional	Optional[bool] Specifies if the input is optional.
description	Optional[str] Description of the input
datastore	str The datastore to upload local files to.
intellectual_property	IntellectualProperty Intellectual property for the input.
enum Required

Examples

Creating a CommandJob with two inputs.

   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