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AutoMLJobChannel

Source code in docs_src/capo_sagemaker/types/auto_ml_job_channel.py 
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class AutoMLJobChannel(TypedDict, closed=True):
    channel_type: NotRequired[
        "capo_sagemaker.types.auto_ml_channel_type.AutoMLChannelType"
    ]
    """<p>The type of channel. Defines whether the data are used for training or validation. The default value is <code>training</code>. Channels for <code>training</code> and <code>validation</code> must share the same <code>ContentType</code> </p> <note> <p>The type of channel defaults to <code>training</code> for the time-series forecasting problem type.</p> </note>"""
    content_type: NotRequired["capo_sagemaker.types.content_type.ContentType"]
    """<p>The content type of the data from the input source. The following are the allowed content types for different problems:</p> <ul> <li> <p>For tabular problem types: <code>text/csv;header=present</code> or <code>x-application/vnd.amazon+parquet</code>. The default value is <code>text/csv;header=present</code>.</p> </li> <li> <p>For image classification: <code>image/png</code>, <code>image/jpeg</code>, or <code>image/*</code>. The default value is <code>image/*</code>.</p> </li> <li> <p>For text classification: <code>text/csv;header=present</code> or <code>x-application/vnd.amazon+parquet</code>. The default value is <code>text/csv;header=present</code>.</p> </li> <li> <p>For time-series forecasting: <code>text/csv;header=present</code> or <code>x-application/vnd.amazon+parquet</code>. The default value is <code>text/csv;header=present</code>.</p> </li> <li> <p>For text generation (LLMs fine-tuning): <code>text/csv;header=present</code> or <code>x-application/vnd.amazon+parquet</code>. The default value is <code>text/csv;header=present</code>.</p> </li> </ul>"""
    compression_type: NotRequired[
        "capo_sagemaker.types.compression_type.CompressionType"
    ]
    """<p>The allowed compression types depend on the input format and problem type. We allow the compression type <code>Gzip</code> for <code>S3Prefix</code> inputs on tabular data only. For all other inputs, the compression type should be <code>None</code>. If no compression type is provided, we default to <code>None</code>.</p>"""
    data_source: NotRequired[
        "capo_sagemaker.types.auto_ml_data_source.AutoMLDataSource"
    ]
    """<p>The data source for an AutoML channel (Required).</p>"""

channel_type instance-attribute 

channel_type: NotRequired[AutoMLChannelType]

The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType

The type of channel defaults to training for the time-series forecasting problem type.

compression_type instance-attribute 

compression_type: NotRequired[CompressionType]

The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.

content_type instance-attribute 

content_type: NotRequired[ContentType]

The content type of the data from the input source. The following are the allowed content types for different problems:

  • For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

  • For image classification: image/png, image/jpeg, or image/*. The default value is image/*.

  • For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

  • For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

  • For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

data_source instance-attribute 

data_source: NotRequired[AutoMLDataSource]

The data source for an AutoML channel (Required).