/** * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. * SPDX-License-Identifier: Apache-2.0. */ #pragma once #include #include #include #include namespace Aws { namespace Utils { namespace Json { class JsonValue; class JsonView; } // namespace Json } // namespace Utils namespace SageMaker { namespace Model { /** *

Contains information about the location of input model artifacts, the name * and shape of the expected data inputs, and the framework in which the model was * trained.

See Also:

AWS * API Reference

*/ class AWS_SAGEMAKER_API InputConfig { public: InputConfig(); InputConfig(Aws::Utils::Json::JsonView jsonValue); InputConfig& operator=(Aws::Utils::Json::JsonView jsonValue); Aws::Utils::Json::JsonValue Jsonize() const; /** *

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).

*/ inline const Aws::String& GetS3Uri() const{ return m_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).

*/ inline bool S3UriHasBeenSet() const { return m_s3UriHasBeenSet; } /** *

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).

*/ inline void SetS3Uri(const Aws::String& value) { m_s3UriHasBeenSet = true; m_s3Uri = value; } /** *

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).

*/ inline void SetS3Uri(Aws::String&& value) { m_s3UriHasBeenSet = true; m_s3Uri = std::move(value); } /** *

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).

*/ inline void SetS3Uri(const char* value) { m_s3UriHasBeenSet = true; m_s3Uri.assign(value); } /** *

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).

*/ inline InputConfig& WithS3Uri(const Aws::String& value) { SetS3Uri(value); return *this;} /** *

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).

*/ inline InputConfig& WithS3Uri(Aws::String&& value) { SetS3Uri(std::move(value)); return *this;} /** *

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).

*/ inline InputConfig& WithS3Uri(const char* value) { SetS3Uri(value); return *this;} /** *

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]}

      *

  • KERAS: You must specify the name and * shape (NCHW format) of expected data inputs using a dictionary format for your * trained model. Note that while Keras model artifacts should be uploaded in NHWC * (channel-last) format, DataInputConfig should be specified in NCHW * (channel-first) format. The dictionary formats required for the console and CLI * are different.

    • Examples for one input:

      • If * using the console, {"input_1":[1,3,224,224]}

      • If * using the CLI, {\"input_1\":[1,3,224,224]}

      • *

    • Examples for two inputs:

      • If using the console, * {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • *

      • If using the CLI, {\"input_1\": [1,3,224,224], * \"input_2\":[1,3,224,224]}

  • * 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.

*/ inline const Aws::String& GetDataInputConfig() const{ return m_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]}

      *

  • KERAS: You must specify the name and * shape (NCHW format) of expected data inputs using a dictionary format for your * trained model. Note that while Keras model artifacts should be uploaded in NHWC * (channel-last) format, DataInputConfig should be specified in NCHW * (channel-first) format. The dictionary formats required for the console and CLI * are different.

    • Examples for one input:

      • If * using the console, {"input_1":[1,3,224,224]}

      • If * using the CLI, {\"input_1\":[1,3,224,224]}

      • *

    • Examples for two inputs:

      • If using the console, * {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • *

      • If using the CLI, {\"input_1\": [1,3,224,224], * \"input_2\":[1,3,224,224]}

  • * 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.

*/ inline bool DataInputConfigHasBeenSet() const { return m_dataInputConfigHasBeenSet; } /** *

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]}

      *

  • KERAS: You must specify the name and * shape (NCHW format) of expected data inputs using a dictionary format for your * trained model. Note that while Keras model artifacts should be uploaded in NHWC * (channel-last) format, DataInputConfig should be specified in NCHW * (channel-first) format. The dictionary formats required for the console and CLI * are different.

    • Examples for one input:

      • If * using the console, {"input_1":[1,3,224,224]}

      • If * using the CLI, {\"input_1\":[1,3,224,224]}

      • *

    • Examples for two inputs:

      • If using the console, * {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • *

      • If using the CLI, {\"input_1\": [1,3,224,224], * \"input_2\":[1,3,224,224]}

  • * 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.

*/ inline void SetDataInputConfig(const Aws::String& value) { m_dataInputConfigHasBeenSet = true; m_dataInputConfig = value; } /** *

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]}

      *

  • KERAS: You must specify the name and * shape (NCHW format) of expected data inputs using a dictionary format for your * trained model. Note that while Keras model artifacts should be uploaded in NHWC * (channel-last) format, DataInputConfig should be specified in NCHW * (channel-first) format. The dictionary formats required for the console and CLI * are different.

    • Examples for one input:

      • If * using the console, {"input_1":[1,3,224,224]}

      • If * using the CLI, {\"input_1\":[1,3,224,224]}

      • *

    • Examples for two inputs:

      • If using the console, * {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • *

      • If using the CLI, {\"input_1\": [1,3,224,224], * \"input_2\":[1,3,224,224]}

  • * 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.

*/ inline void SetDataInputConfig(Aws::String&& value) { m_dataInputConfigHasBeenSet = true; m_dataInputConfig = std::move(value); } /** *

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]}

      *

  • KERAS: You must specify the name and * shape (NCHW format) of expected data inputs using a dictionary format for your * trained model. Note that while Keras model artifacts should be uploaded in NHWC * (channel-last) format, DataInputConfig should be specified in NCHW * (channel-first) format. The dictionary formats required for the console and CLI * are different.

    • Examples for one input:

      • If * using the console, {"input_1":[1,3,224,224]}

      • If * using the CLI, {\"input_1\":[1,3,224,224]}

      • *

    • Examples for two inputs:

      • If using the console, * {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • *

      • If using the CLI, {\"input_1\": [1,3,224,224], * \"input_2\":[1,3,224,224]}

  • * 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.

*/ inline void SetDataInputConfig(const char* value) { m_dataInputConfigHasBeenSet = true; m_dataInputConfig.assign(value); } /** *

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]}

      *

  • KERAS: You must specify the name and * shape (NCHW format) of expected data inputs using a dictionary format for your * trained model. Note that while Keras model artifacts should be uploaded in NHWC * (channel-last) format, DataInputConfig should be specified in NCHW * (channel-first) format. The dictionary formats required for the console and CLI * are different.

    • Examples for one input:

      • If * using the console, {"input_1":[1,3,224,224]}

      • If * using the CLI, {\"input_1\":[1,3,224,224]}

      • *

    • Examples for two inputs:

      • If using the console, * {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • *

      • If using the CLI, {\"input_1\": [1,3,224,224], * \"input_2\":[1,3,224,224]}

  • * 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.

*/ inline InputConfig& WithDataInputConfig(const Aws::String& value) { SetDataInputConfig(value); return *this;} /** *

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]}

      *

  • KERAS: You must specify the name and * shape (NCHW format) of expected data inputs using a dictionary format for your * trained model. Note that while Keras model artifacts should be uploaded in NHWC * (channel-last) format, DataInputConfig should be specified in NCHW * (channel-first) format. The dictionary formats required for the console and CLI * are different.

    • Examples for one input:

      • If * using the console, {"input_1":[1,3,224,224]}

      • If * using the CLI, {\"input_1\":[1,3,224,224]}

      • *

    • Examples for two inputs:

      • If using the console, * {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • *

      • If using the CLI, {\"input_1\": [1,3,224,224], * \"input_2\":[1,3,224,224]}

  • * 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.

*/ inline InputConfig& WithDataInputConfig(Aws::String&& value) { SetDataInputConfig(std::move(value)); return *this;} /** *

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]}

      *

  • KERAS: You must specify the name and * shape (NCHW format) of expected data inputs using a dictionary format for your * trained model. Note that while Keras model artifacts should be uploaded in NHWC * (channel-last) format, DataInputConfig should be specified in NCHW * (channel-first) format. The dictionary formats required for the console and CLI * are different.

    • Examples for one input:

      • If * using the console, {"input_1":[1,3,224,224]}

      • If * using the CLI, {\"input_1\":[1,3,224,224]}

      • *

    • Examples for two inputs:

      • If using the console, * {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • *

      • If using the CLI, {\"input_1\": [1,3,224,224], * \"input_2\":[1,3,224,224]}

  • * 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.

*/ inline InputConfig& WithDataInputConfig(const char* value) { SetDataInputConfig(value); return *this;} /** *

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

*/ inline const Framework& GetFramework() const{ return m_framework; } /** *

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

*/ inline bool FrameworkHasBeenSet() const { return m_frameworkHasBeenSet; } /** *

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

*/ inline void SetFramework(const Framework& value) { m_frameworkHasBeenSet = true; m_framework = value; } /** *

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

*/ inline void SetFramework(Framework&& value) { m_frameworkHasBeenSet = true; m_framework = std::move(value); } /** *

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

*/ inline InputConfig& WithFramework(const Framework& value) { SetFramework(value); return *this;} /** *

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

*/ inline InputConfig& WithFramework(Framework&& value) { SetFramework(std::move(value)); return *this;} private: Aws::String m_s3Uri; bool m_s3UriHasBeenSet; Aws::String m_dataInputConfig; bool m_dataInputConfigHasBeenSet; Framework m_framework; bool m_frameworkHasBeenSet; }; } // namespace Model } // namespace SageMaker } // namespace Aws