software.amazon.awssdk.services.sagemaker.model

Interface InputConfig.Builder

All Superinterfaces:

Buildable, CopyableBuilder<InputConfig.Builder,InputConfig>, SdkBuilder<InputConfig.Builder,InputConfig>, SdkPojo

Enclosing class:

InputConfig

public static interface InputConfig.Builder
extends SdkPojo, CopyableBuilder<InputConfig.Builder,InputConfig>

Method Summary

Modifier and Type	Method and Description
InputConfig.Builder	dataInputConfig(String dataInputConfig) Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.
InputConfig.Builder	framework(Framework framework) Identifies the framework in which the model was trained.
InputConfig.Builder	framework(String framework) Identifies the framework in which the model was trained.
InputConfig.Builder	s3Uri(String s3Uri) The S3 path where the model artifacts, which result from model training, are stored.

Methods inherited from interface software.amazon.awssdk.core.SdkPojo

equalsBySdkFields, sdkFields

Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder

copy

Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder

applyMutation, build

Method Detail

s3Uri

InputConfig.Builder s3Uri(String s3Uri)

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

Parameters:

s3Uri - The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

Returns:

Returns a reference to this object so that method calls can be chained together.

dataInputConfig

InputConfig.Builder dataInputConfig(String dataInputConfig)

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.

• TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

  • Examples for one input:

    • If using the console, {"input":[1,1024,1024,3]}

    • If using the CLI, {\"input\":[1,1024,1024,3]}

  • Examples for two inputs:

    • If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}

    • If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}

• MXNET/ONNX: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

  • Examples for one input:

    • If using the console, {"data":[1,3,1024,1024]}

    • If using the CLI, {\"data\":[1,3,1024,1024]}

  • Examples for two inputs:

    • If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}

    • If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}

• PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

  • Examples for one input in dictionary format:

    • If using the console, {"input0":[1,3,224,224]}

    • If using the CLI, {\"input0\":[1,3,224,224]}

  • Example for one input in list format: [[1,3,224,224]]

  • Examples for two inputs in dictionary format:

    • If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}

    • If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}

  • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]

• XGBOOST: input data name and shape are not needed.

Parameters:

dataInputConfig - Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.

• TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

  • Examples for one input:

    • If using the console, {"input":[1,1024,1024,3]}

    • If using the CLI, {\"input\":[1,1024,1024,3]}

  • Examples for two inputs:

    • If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}

    • If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}

• MXNET/ONNX: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

  • Examples for one input:

    • If using the console, {"data":[1,3,1024,1024]}

    • If using the CLI, {\"data\":[1,3,1024,1024]}

  • Examples for two inputs:

    • If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}

    • If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}

• PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

  • Examples for one input in dictionary format:

    • If using the console, {"input0":[1,3,224,224]}

    • If using the CLI, {\"input0\":[1,3,224,224]}

  • Example for one input in list format: [[1,3,224,224]]

  • Examples for two inputs in dictionary format:

    • If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}

    • If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}

  • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]

• XGBOOST: input data name and shape are not needed.

Returns:

Returns a reference to this object so that method calls can be chained together.

framework

InputConfig.Builder framework(String framework)

Identifies the framework in which the model was trained. For example: TENSORFLOW.

Parameters:

framework - Identifies the framework in which the model was trained. For example: TENSORFLOW.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

Framework, Framework

framework

InputConfig.Builder framework(Framework framework)

Identifies the framework in which the model was trained. For example: TENSORFLOW.

Parameters:

framework - Identifies the framework in which the model was trained. For example: TENSORFLOW.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

Framework, Framework