Share via

Input Class

  • Reference

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
path_on_compute
Optional[str]

The access path of the data input for compute
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