/** * 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 InputConfig { public: AWS_SAGEMAKER_API InputConfig(); AWS_SAGEMAKER_API InputConfig(Aws::Utils::Json::JsonView jsonValue); AWS_SAGEMAKER_API InputConfig& operator=(Aws::Utils::Json::JsonView jsonValue); AWS_SAGEMAKER_API 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 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/DARKNET: 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.

DataInputConfig supports the following * parameters for CoreML TargetDevice (ML Model * format):

  • shape: Input shape, for example * {"input_1": {"shape": [1,224,224,3]}}. In addition to static input * shapes, CoreML converter supports Flexible input shapes:

    • Range * Dimension. You can use the Range Dimension feature if you know the input shape * will be within some specific interval in that dimension, for example: * {"input_1": {"shape": ["1..10", 224, 224, 3]}}

    • *

      Enumerated shapes. Sometimes, the models are trained to work only on a select * set of inputs. You can enumerate all supported input shapes, for example: * {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

      *

  • default_shape: Default input shape. You * can set a default shape during conversion for both Range Dimension and * Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, * 3], "default_shape": [1, 224, 224, 3]}}

  • * type: Input type. Allowed values: Image and * Tensor. By default, the converter generates an ML Model with inputs * of type Tensor (MultiArray). User can set input type to be Image. Image input * type requires additional input parameters such as bias and * scale.

  • bias: If the input type is * an Image, you need to provide the bias vector.

  • * scale: If the input type is an Image, you need to provide a scale * factor.

CoreML ClassifierConfig parameters can * be specified using OutputConfig * CompilerOptions. CoreML converter supports Tensorflow and PyTorch * models. CoreML conversion examples:

  • Tensor type input:

      *

    • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], * [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • *

  • Tensor type input without input name (PyTorch):

    • * "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], * "default_shape": [1,3,224,224]}]

  • Image * type input:

    • "DataInputConfig": {"input_1": {"shape": * [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", * "bias": [-1,-1,-1], "scale": 0.007843137255}}

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

  • Image type input without input name * (PyTorch):

    • "DataInputConfig": [{"shape": [[1,3,224,224], * [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": * [-1,-1,-1], "scale": 0.007843137255}]

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

Depending on the model format, * DataInputConfig requires the following parameters for * ml_eia2 OutputConfig:TargetDevice.

*

  • For TensorFlow models saved in the SavedModel format, specify the * input names from signature_def_key and the input model shapes for * DataInputConfig. Specify the signature_def_key in * OutputConfig:CompilerOptions if the model does not use * TensorFlow's default signature def key. For example:

    • * "DataInputConfig": {"inputs": [1, 224, 224, 3]}

    • * "CompilerOptions": {"signature_def_key": "serving_custom"}

      *

  • For TensorFlow models saved as a frozen graph, specify * the input tensor names and shapes in DataInputConfig and the output * tensor names for output_names in * OutputConfig:CompilerOptions . For example:

    • * "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}

      • *

    • "CompilerOptions": {"output_names": ["output_tensor:0"]} *

*/ 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 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/DARKNET: 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.

DataInputConfig supports the following * parameters for CoreML TargetDevice (ML Model * format):

  • shape: Input shape, for example * {"input_1": {"shape": [1,224,224,3]}}. In addition to static input * shapes, CoreML converter supports Flexible input shapes:

    • Range * Dimension. You can use the Range Dimension feature if you know the input shape * will be within some specific interval in that dimension, for example: * {"input_1": {"shape": ["1..10", 224, 224, 3]}}

    • *

      Enumerated shapes. Sometimes, the models are trained to work only on a select * set of inputs. You can enumerate all supported input shapes, for example: * {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

      *

  • default_shape: Default input shape. You * can set a default shape during conversion for both Range Dimension and * Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, * 3], "default_shape": [1, 224, 224, 3]}}

  • * type: Input type. Allowed values: Image and * Tensor. By default, the converter generates an ML Model with inputs * of type Tensor (MultiArray). User can set input type to be Image. Image input * type requires additional input parameters such as bias and * scale.

  • bias: If the input type is * an Image, you need to provide the bias vector.

  • * scale: If the input type is an Image, you need to provide a scale * factor.

CoreML ClassifierConfig parameters can * be specified using OutputConfig * CompilerOptions. CoreML converter supports Tensorflow and PyTorch * models. CoreML conversion examples:

  • Tensor type input:

      *

    • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], * [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • *

  • Tensor type input without input name (PyTorch):

    • * "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], * "default_shape": [1,3,224,224]}]

  • Image * type input:

    • "DataInputConfig": {"input_1": {"shape": * [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", * "bias": [-1,-1,-1], "scale": 0.007843137255}}

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

  • Image type input without input name * (PyTorch):

    • "DataInputConfig": [{"shape": [[1,3,224,224], * [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": * [-1,-1,-1], "scale": 0.007843137255}]

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

Depending on the model format, * DataInputConfig requires the following parameters for * ml_eia2 OutputConfig:TargetDevice.

*

  • For TensorFlow models saved in the SavedModel format, specify the * input names from signature_def_key and the input model shapes for * DataInputConfig. Specify the signature_def_key in * OutputConfig:CompilerOptions if the model does not use * TensorFlow's default signature def key. For example:

    • * "DataInputConfig": {"inputs": [1, 224, 224, 3]}

    • * "CompilerOptions": {"signature_def_key": "serving_custom"}

      *

  • For TensorFlow models saved as a frozen graph, specify * the input tensor names and shapes in DataInputConfig and the output * tensor names for output_names in * OutputConfig:CompilerOptions . For example:

    • * "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}

      • *

    • "CompilerOptions": {"output_names": ["output_tensor:0"]} *

*/ 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 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/DARKNET: 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.

DataInputConfig supports the following * parameters for CoreML TargetDevice (ML Model * format):

  • shape: Input shape, for example * {"input_1": {"shape": [1,224,224,3]}}. In addition to static input * shapes, CoreML converter supports Flexible input shapes:

    • Range * Dimension. You can use the Range Dimension feature if you know the input shape * will be within some specific interval in that dimension, for example: * {"input_1": {"shape": ["1..10", 224, 224, 3]}}

    • *

      Enumerated shapes. Sometimes, the models are trained to work only on a select * set of inputs. You can enumerate all supported input shapes, for example: * {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

      *

  • default_shape: Default input shape. You * can set a default shape during conversion for both Range Dimension and * Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, * 3], "default_shape": [1, 224, 224, 3]}}

  • * type: Input type. Allowed values: Image and * Tensor. By default, the converter generates an ML Model with inputs * of type Tensor (MultiArray). User can set input type to be Image. Image input * type requires additional input parameters such as bias and * scale.

  • bias: If the input type is * an Image, you need to provide the bias vector.

  • * scale: If the input type is an Image, you need to provide a scale * factor.

CoreML ClassifierConfig parameters can * be specified using OutputConfig * CompilerOptions. CoreML converter supports Tensorflow and PyTorch * models. CoreML conversion examples:

  • Tensor type input:

      *

    • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], * [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • *

  • Tensor type input without input name (PyTorch):

    • * "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], * "default_shape": [1,3,224,224]}]

  • Image * type input:

    • "DataInputConfig": {"input_1": {"shape": * [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", * "bias": [-1,-1,-1], "scale": 0.007843137255}}

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

  • Image type input without input name * (PyTorch):

    • "DataInputConfig": [{"shape": [[1,3,224,224], * [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": * [-1,-1,-1], "scale": 0.007843137255}]

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

Depending on the model format, * DataInputConfig requires the following parameters for * ml_eia2 OutputConfig:TargetDevice.

*

  • For TensorFlow models saved in the SavedModel format, specify the * input names from signature_def_key and the input model shapes for * DataInputConfig. Specify the signature_def_key in * OutputConfig:CompilerOptions if the model does not use * TensorFlow's default signature def key. For example:

    • * "DataInputConfig": {"inputs": [1, 224, 224, 3]}

    • * "CompilerOptions": {"signature_def_key": "serving_custom"}

      *

  • For TensorFlow models saved as a frozen graph, specify * the input tensor names and shapes in DataInputConfig and the output * tensor names for output_names in * OutputConfig:CompilerOptions . For example:

    • * "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}

      • *

    • "CompilerOptions": {"output_names": ["output_tensor:0"]} *

*/ 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 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/DARKNET: 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.

DataInputConfig supports the following * parameters for CoreML TargetDevice (ML Model * format):

  • shape: Input shape, for example * {"input_1": {"shape": [1,224,224,3]}}. In addition to static input * shapes, CoreML converter supports Flexible input shapes:

    • Range * Dimension. You can use the Range Dimension feature if you know the input shape * will be within some specific interval in that dimension, for example: * {"input_1": {"shape": ["1..10", 224, 224, 3]}}

    • *

      Enumerated shapes. Sometimes, the models are trained to work only on a select * set of inputs. You can enumerate all supported input shapes, for example: * {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

      *

  • default_shape: Default input shape. You * can set a default shape during conversion for both Range Dimension and * Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, * 3], "default_shape": [1, 224, 224, 3]}}

  • * type: Input type. Allowed values: Image and * Tensor. By default, the converter generates an ML Model with inputs * of type Tensor (MultiArray). User can set input type to be Image. Image input * type requires additional input parameters such as bias and * scale.

  • bias: If the input type is * an Image, you need to provide the bias vector.

  • * scale: If the input type is an Image, you need to provide a scale * factor.

CoreML ClassifierConfig parameters can * be specified using OutputConfig * CompilerOptions. CoreML converter supports Tensorflow and PyTorch * models. CoreML conversion examples:

  • Tensor type input:

      *

    • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], * [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • *

  • Tensor type input without input name (PyTorch):

    • * "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], * "default_shape": [1,3,224,224]}]

  • Image * type input:

    • "DataInputConfig": {"input_1": {"shape": * [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", * "bias": [-1,-1,-1], "scale": 0.007843137255}}

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

  • Image type input without input name * (PyTorch):

    • "DataInputConfig": [{"shape": [[1,3,224,224], * [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": * [-1,-1,-1], "scale": 0.007843137255}]

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

Depending on the model format, * DataInputConfig requires the following parameters for * ml_eia2 OutputConfig:TargetDevice.

*

  • For TensorFlow models saved in the SavedModel format, specify the * input names from signature_def_key and the input model shapes for * DataInputConfig. Specify the signature_def_key in * OutputConfig:CompilerOptions if the model does not use * TensorFlow's default signature def key. For example:

    • * "DataInputConfig": {"inputs": [1, 224, 224, 3]}

    • * "CompilerOptions": {"signature_def_key": "serving_custom"}

      *

  • For TensorFlow models saved as a frozen graph, specify * the input tensor names and shapes in DataInputConfig and the output * tensor names for output_names in * OutputConfig:CompilerOptions . For example:

    • * "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}

      • *

    • "CompilerOptions": {"output_names": ["output_tensor:0"]} *

*/ 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 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/DARKNET: 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.

DataInputConfig supports the following * parameters for CoreML TargetDevice (ML Model * format):

  • shape: Input shape, for example * {"input_1": {"shape": [1,224,224,3]}}. In addition to static input * shapes, CoreML converter supports Flexible input shapes:

    • Range * Dimension. You can use the Range Dimension feature if you know the input shape * will be within some specific interval in that dimension, for example: * {"input_1": {"shape": ["1..10", 224, 224, 3]}}

    • *

      Enumerated shapes. Sometimes, the models are trained to work only on a select * set of inputs. You can enumerate all supported input shapes, for example: * {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

      *

  • default_shape: Default input shape. You * can set a default shape during conversion for both Range Dimension and * Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, * 3], "default_shape": [1, 224, 224, 3]}}

  • * type: Input type. Allowed values: Image and * Tensor. By default, the converter generates an ML Model with inputs * of type Tensor (MultiArray). User can set input type to be Image. Image input * type requires additional input parameters such as bias and * scale.

  • bias: If the input type is * an Image, you need to provide the bias vector.

  • * scale: If the input type is an Image, you need to provide a scale * factor.

CoreML ClassifierConfig parameters can * be specified using OutputConfig * CompilerOptions. CoreML converter supports Tensorflow and PyTorch * models. CoreML conversion examples:

  • Tensor type input:

      *

    • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], * [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • *

  • Tensor type input without input name (PyTorch):

    • * "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], * "default_shape": [1,3,224,224]}]

  • Image * type input:

    • "DataInputConfig": {"input_1": {"shape": * [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", * "bias": [-1,-1,-1], "scale": 0.007843137255}}

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

  • Image type input without input name * (PyTorch):

    • "DataInputConfig": [{"shape": [[1,3,224,224], * [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": * [-1,-1,-1], "scale": 0.007843137255}]

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

Depending on the model format, * DataInputConfig requires the following parameters for * ml_eia2 OutputConfig:TargetDevice.

*

  • For TensorFlow models saved in the SavedModel format, specify the * input names from signature_def_key and the input model shapes for * DataInputConfig. Specify the signature_def_key in * OutputConfig:CompilerOptions if the model does not use * TensorFlow's default signature def key. For example:

    • * "DataInputConfig": {"inputs": [1, 224, 224, 3]}

    • * "CompilerOptions": {"signature_def_key": "serving_custom"}

      *

  • For TensorFlow models saved as a frozen graph, specify * the input tensor names and shapes in DataInputConfig and the output * tensor names for output_names in * OutputConfig:CompilerOptions . For example:

    • * "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}

      • *

    • "CompilerOptions": {"output_names": ["output_tensor:0"]} *

*/ 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 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/DARKNET: 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.

DataInputConfig supports the following * parameters for CoreML TargetDevice (ML Model * format):

  • shape: Input shape, for example * {"input_1": {"shape": [1,224,224,3]}}. In addition to static input * shapes, CoreML converter supports Flexible input shapes:

    • Range * Dimension. You can use the Range Dimension feature if you know the input shape * will be within some specific interval in that dimension, for example: * {"input_1": {"shape": ["1..10", 224, 224, 3]}}

    • *

      Enumerated shapes. Sometimes, the models are trained to work only on a select * set of inputs. You can enumerate all supported input shapes, for example: * {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

      *

  • default_shape: Default input shape. You * can set a default shape during conversion for both Range Dimension and * Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, * 3], "default_shape": [1, 224, 224, 3]}}

  • * type: Input type. Allowed values: Image and * Tensor. By default, the converter generates an ML Model with inputs * of type Tensor (MultiArray). User can set input type to be Image. Image input * type requires additional input parameters such as bias and * scale.

  • bias: If the input type is * an Image, you need to provide the bias vector.

  • * scale: If the input type is an Image, you need to provide a scale * factor.

CoreML ClassifierConfig parameters can * be specified using OutputConfig * CompilerOptions. CoreML converter supports Tensorflow and PyTorch * models. CoreML conversion examples:

  • Tensor type input:

      *

    • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], * [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • *

  • Tensor type input without input name (PyTorch):

    • * "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], * "default_shape": [1,3,224,224]}]

  • Image * type input:

    • "DataInputConfig": {"input_1": {"shape": * [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", * "bias": [-1,-1,-1], "scale": 0.007843137255}}

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

  • Image type input without input name * (PyTorch):

    • "DataInputConfig": [{"shape": [[1,3,224,224], * [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": * [-1,-1,-1], "scale": 0.007843137255}]

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

Depending on the model format, * DataInputConfig requires the following parameters for * ml_eia2 OutputConfig:TargetDevice.

*

  • For TensorFlow models saved in the SavedModel format, specify the * input names from signature_def_key and the input model shapes for * DataInputConfig. Specify the signature_def_key in * OutputConfig:CompilerOptions if the model does not use * TensorFlow's default signature def key. For example:

    • * "DataInputConfig": {"inputs": [1, 224, 224, 3]}

    • * "CompilerOptions": {"signature_def_key": "serving_custom"}

      *

  • For TensorFlow models saved as a frozen graph, specify * the input tensor names and shapes in DataInputConfig and the output * tensor names for output_names in * OutputConfig:CompilerOptions . For example:

    • * "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}

      • *

    • "CompilerOptions": {"output_names": ["output_tensor:0"]} *

*/ 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 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/DARKNET: 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.

DataInputConfig supports the following * parameters for CoreML TargetDevice (ML Model * format):

  • shape: Input shape, for example * {"input_1": {"shape": [1,224,224,3]}}. In addition to static input * shapes, CoreML converter supports Flexible input shapes:

    • Range * Dimension. You can use the Range Dimension feature if you know the input shape * will be within some specific interval in that dimension, for example: * {"input_1": {"shape": ["1..10", 224, 224, 3]}}

    • *

      Enumerated shapes. Sometimes, the models are trained to work only on a select * set of inputs. You can enumerate all supported input shapes, for example: * {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

      *

  • default_shape: Default input shape. You * can set a default shape during conversion for both Range Dimension and * Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, * 3], "default_shape": [1, 224, 224, 3]}}

  • * type: Input type. Allowed values: Image and * Tensor. By default, the converter generates an ML Model with inputs * of type Tensor (MultiArray). User can set input type to be Image. Image input * type requires additional input parameters such as bias and * scale.

  • bias: If the input type is * an Image, you need to provide the bias vector.

  • * scale: If the input type is an Image, you need to provide a scale * factor.

CoreML ClassifierConfig parameters can * be specified using OutputConfig * CompilerOptions. CoreML converter supports Tensorflow and PyTorch * models. CoreML conversion examples:

  • Tensor type input:

      *

    • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], * [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • *

  • Tensor type input without input name (PyTorch):

    • * "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], * "default_shape": [1,3,224,224]}]

  • Image * type input:

    • "DataInputConfig": {"input_1": {"shape": * [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", * "bias": [-1,-1,-1], "scale": 0.007843137255}}

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

  • Image type input without input name * (PyTorch):

    • "DataInputConfig": [{"shape": [[1,3,224,224], * [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": * [-1,-1,-1], "scale": 0.007843137255}]

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

Depending on the model format, * DataInputConfig requires the following parameters for * ml_eia2 OutputConfig:TargetDevice.

*

  • For TensorFlow models saved in the SavedModel format, specify the * input names from signature_def_key and the input model shapes for * DataInputConfig. Specify the signature_def_key in * OutputConfig:CompilerOptions if the model does not use * TensorFlow's default signature def key. For example:

    • * "DataInputConfig": {"inputs": [1, 224, 224, 3]}

    • * "CompilerOptions": {"signature_def_key": "serving_custom"}

      *

  • For TensorFlow models saved as a frozen graph, specify * the input tensor names and shapes in DataInputConfig and the output * tensor names for output_names in * OutputConfig:CompilerOptions . For example:

    • * "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}

      • *

    • "CompilerOptions": {"output_names": ["output_tensor:0"]} *

*/ 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 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/DARKNET: 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.

DataInputConfig supports the following * parameters for CoreML TargetDevice (ML Model * format):

  • shape: Input shape, for example * {"input_1": {"shape": [1,224,224,3]}}. In addition to static input * shapes, CoreML converter supports Flexible input shapes:

    • Range * Dimension. You can use the Range Dimension feature if you know the input shape * will be within some specific interval in that dimension, for example: * {"input_1": {"shape": ["1..10", 224, 224, 3]}}

    • *

      Enumerated shapes. Sometimes, the models are trained to work only on a select * set of inputs. You can enumerate all supported input shapes, for example: * {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

      *

  • default_shape: Default input shape. You * can set a default shape during conversion for both Range Dimension and * Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, * 3], "default_shape": [1, 224, 224, 3]}}

  • * type: Input type. Allowed values: Image and * Tensor. By default, the converter generates an ML Model with inputs * of type Tensor (MultiArray). User can set input type to be Image. Image input * type requires additional input parameters such as bias and * scale.

  • bias: If the input type is * an Image, you need to provide the bias vector.

  • * scale: If the input type is an Image, you need to provide a scale * factor.

CoreML ClassifierConfig parameters can * be specified using OutputConfig * CompilerOptions. CoreML converter supports Tensorflow and PyTorch * models. CoreML conversion examples:

  • Tensor type input:

      *

    • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], * [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • *

  • Tensor type input without input name (PyTorch):

    • * "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], * "default_shape": [1,3,224,224]}]

  • Image * type input:

    • "DataInputConfig": {"input_1": {"shape": * [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", * "bias": [-1,-1,-1], "scale": 0.007843137255}}

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

  • Image type input without input name * (PyTorch):

    • "DataInputConfig": [{"shape": [[1,3,224,224], * [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": * [-1,-1,-1], "scale": 0.007843137255}]

    • * "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} *

Depending on the model format, * DataInputConfig requires the following parameters for * ml_eia2 OutputConfig:TargetDevice.

*

  • For TensorFlow models saved in the SavedModel format, specify the * input names from signature_def_key and the input model shapes for * DataInputConfig. Specify the signature_def_key in * OutputConfig:CompilerOptions if the model does not use * TensorFlow's default signature def key. For example:

    • * "DataInputConfig": {"inputs": [1, 224, 224, 3]}

    • * "CompilerOptions": {"signature_def_key": "serving_custom"}

      *

  • For TensorFlow models saved as a frozen graph, specify * the input tensor names and shapes in DataInputConfig and the output * tensor names for output_names in * OutputConfig:CompilerOptions . For example:

    • * "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}

      • *

    • "CompilerOptions": {"output_names": ["output_tensor:0"]} *

*/ 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;} /** *

Specifies the framework version to use. This API field is only supported for * the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For * information about framework versions supported for cloud targets and edge * devices, see Cloud * Supported Instance Types and Frameworks and Edge * Supported Frameworks.

*/ inline const Aws::String& GetFrameworkVersion() const{ return m_frameworkVersion; } /** *

Specifies the framework version to use. This API field is only supported for * the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For * information about framework versions supported for cloud targets and edge * devices, see Cloud * Supported Instance Types and Frameworks and Edge * Supported Frameworks.

*/ inline bool FrameworkVersionHasBeenSet() const { return m_frameworkVersionHasBeenSet; } /** *

Specifies the framework version to use. This API field is only supported for * the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For * information about framework versions supported for cloud targets and edge * devices, see Cloud * Supported Instance Types and Frameworks and Edge * Supported Frameworks.

*/ inline void SetFrameworkVersion(const Aws::String& value) { m_frameworkVersionHasBeenSet = true; m_frameworkVersion = value; } /** *

Specifies the framework version to use. This API field is only supported for * the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For * information about framework versions supported for cloud targets and edge * devices, see Cloud * Supported Instance Types and Frameworks and Edge * Supported Frameworks.

*/ inline void SetFrameworkVersion(Aws::String&& value) { m_frameworkVersionHasBeenSet = true; m_frameworkVersion = std::move(value); } /** *

Specifies the framework version to use. This API field is only supported for * the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For * information about framework versions supported for cloud targets and edge * devices, see Cloud * Supported Instance Types and Frameworks and Edge * Supported Frameworks.

*/ inline void SetFrameworkVersion(const char* value) { m_frameworkVersionHasBeenSet = true; m_frameworkVersion.assign(value); } /** *

Specifies the framework version to use. This API field is only supported for * the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For * information about framework versions supported for cloud targets and edge * devices, see Cloud * Supported Instance Types and Frameworks and Edge * Supported Frameworks.

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

Specifies the framework version to use. This API field is only supported for * the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For * information about framework versions supported for cloud targets and edge * devices, see Cloud * Supported Instance Types and Frameworks and Edge * Supported Frameworks.

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

Specifies the framework version to use. This API field is only supported for * the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For * information about framework versions supported for cloud targets and edge * devices, see Cloud * Supported Instance Types and Frameworks and Edge * Supported Frameworks.

*/ inline InputConfig& WithFrameworkVersion(const char* value) { SetFrameworkVersion(value); return *this;} private: Aws::String m_s3Uri; bool m_s3UriHasBeenSet = false; Aws::String m_dataInputConfig; bool m_dataInputConfigHasBeenSet = false; Framework m_framework; bool m_frameworkHasBeenSet = false; Aws::String m_frameworkVersion; bool m_frameworkVersionHasBeenSet = false; }; } // namespace Model } // namespace SageMaker } // namespace Aws