576 lines
37 KiB
C
576 lines
37 KiB
C
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/**
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* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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* SPDX-License-Identifier: Apache-2.0.
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*/
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#pragma once
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#include <aws/sagemaker/SageMaker_EXPORTS.h>
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#include <aws/core/utils/memory/stl/AWSString.h>
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#include <aws/sagemaker/model/Framework.h>
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#include <utility>
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namespace Aws
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{
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namespace Utils
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{
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namespace Json
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{
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class JsonValue;
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class JsonView;
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} // namespace Json
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} // namespace Utils
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namespace SageMaker
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{
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namespace Model
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{
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/**
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* <p>Contains information about the location of input model artifacts, the name
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* and shape of the expected data inputs, and the framework in which the model was
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* trained.</p><p><h3>See Also:</h3> <a
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* href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/InputConfig">AWS
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* API Reference</a></p>
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*/
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class AWS_SAGEMAKER_API InputConfig
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{
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public:
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InputConfig();
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InputConfig(Aws::Utils::Json::JsonView jsonValue);
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InputConfig& operator=(Aws::Utils::Json::JsonView jsonValue);
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Aws::Utils::Json::JsonValue Jsonize() const;
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/**
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* <p>The S3 path where the model artifacts, which result from model training, are
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* stored. This path must point to a single gzip compressed tar archive (.tar.gz
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* suffix).</p>
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*/
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inline const Aws::String& GetS3Uri() const{ return m_s3Uri; }
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/**
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* <p>The S3 path where the model artifacts, which result from model training, are
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* stored. This path must point to a single gzip compressed tar archive (.tar.gz
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* suffix).</p>
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*/
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inline bool S3UriHasBeenSet() const { return m_s3UriHasBeenSet; }
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/**
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* <p>The S3 path where the model artifacts, which result from model training, are
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* stored. This path must point to a single gzip compressed tar archive (.tar.gz
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* suffix).</p>
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*/
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inline void SetS3Uri(const Aws::String& value) { m_s3UriHasBeenSet = true; m_s3Uri = value; }
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/**
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* <p>The S3 path where the model artifacts, which result from model training, are
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* stored. This path must point to a single gzip compressed tar archive (.tar.gz
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* suffix).</p>
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*/
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inline void SetS3Uri(Aws::String&& value) { m_s3UriHasBeenSet = true; m_s3Uri = std::move(value); }
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/**
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* <p>The S3 path where the model artifacts, which result from model training, are
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* stored. This path must point to a single gzip compressed tar archive (.tar.gz
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* suffix).</p>
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*/
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inline void SetS3Uri(const char* value) { m_s3UriHasBeenSet = true; m_s3Uri.assign(value); }
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/**
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* <p>The S3 path where the model artifacts, which result from model training, are
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* stored. This path must point to a single gzip compressed tar archive (.tar.gz
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* suffix).</p>
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*/
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inline InputConfig& WithS3Uri(const Aws::String& value) { SetS3Uri(value); return *this;}
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/**
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* <p>The S3 path where the model artifacts, which result from model training, are
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* stored. This path must point to a single gzip compressed tar archive (.tar.gz
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* suffix).</p>
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*/
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inline InputConfig& WithS3Uri(Aws::String&& value) { SetS3Uri(std::move(value)); return *this;}
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/**
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* <p>The S3 path where the model artifacts, which result from model training, are
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* stored. This path must point to a single gzip compressed tar archive (.tar.gz
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* suffix).</p>
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*/
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inline InputConfig& WithS3Uri(const char* value) { SetS3Uri(value); return *this;}
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/**
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* <p>Specifies the name and shape of the expected data inputs for your trained
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* model with a JSON dictionary form. The data inputs are
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* <a>InputConfig$Framework</a> specific. </p> <ul> <li> <p>
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* <code>TensorFlow</code>: You must specify the name and shape (NHWC format) of
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* the expected data inputs using a dictionary format for your trained model. The
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* dictionary formats required for the console and CLI are different.</p> <ul> <li>
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* <p>Examples for one input:</p> <ul> <li> <p>If using the console,
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* <code>{"input":[1,1024,1024,3]}</code> </p> </li> <li> <p>If using the CLI,
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* <code>{\"input\":[1,1024,1024,3]}</code> </p> </li> </ul> </li> <li> <p>Examples
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* for two inputs:</p> <ul> <li> <p>If using the console, <code>{"data1":
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* [1,28,28,1], "data2":[1,28,28,1]}</code> </p> </li> <li> <p>If using the CLI,
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* <code>{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}</code> </p> </li> </ul>
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* </li> </ul> </li> <li> <p> <code>KERAS</code>: You must specify the name and
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* shape (NCHW format) of expected data inputs using a dictionary format for your
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* trained model. Note that while Keras model artifacts should be uploaded in NHWC
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* (channel-last) format, <code>DataInputConfig</code> should be specified in NCHW
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* (channel-first) format. The dictionary formats required for the console and CLI
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* are different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If
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* using the console, <code>{"input_1":[1,3,224,224]}</code> </p> </li> <li> <p>If
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* using the CLI, <code>{\"input_1\":[1,3,224,224]}</code> </p> </li> </ul> </li>
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* <li> <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
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* <code>{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} </code> </p> </li>
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* <li> <p>If using the CLI, <code>{\"input_1\": [1,3,224,224],
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* \"input_2\":[1,3,224,224]}</code> </p> </li> </ul> </li> </ul> </li> <li> <p>
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* <code>MXNET/ONNX</code>: You must specify the name and shape (NCHW format) of
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* the expected data inputs in order using a dictionary format for your trained
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* model. The dictionary formats required for the console and CLI are
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* different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If using
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* the console, <code>{"data":[1,3,1024,1024]}</code> </p> </li> <li> <p>If using
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* the CLI, <code>{\"data\":[1,3,1024,1024]}</code> </p> </li> </ul> </li> <li>
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* <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
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* <code>{"var1": [1,1,28,28], "var2":[1,1,28,28]} </code> </p> </li> <li> <p>If
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* using the CLI, <code>{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}</code> </p>
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* </li> </ul> </li> </ul> </li> <li> <p> <code>PyTorch</code>: You can either
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* specify the name and shape (NCHW format) of expected data inputs in order using
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* a dictionary format for your trained model or you can specify the shape only
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* using a list format. The dictionary formats required for the console and CLI are
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* different. The list formats for the console and CLI are the same.</p> <ul> <li>
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* <p>Examples for one input in dictionary format:</p> <ul> <li> <p>If using the
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* console, <code>{"input0":[1,3,224,224]}</code> </p> </li> <li> <p>If using the
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* CLI, <code>{\"input0\":[1,3,224,224]}</code> </p> </li> </ul> </li> <li>
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* <p>Example for one input in list format: <code>[[1,3,224,224]]</code> </p> </li>
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* <li> <p>Examples for two inputs in dictionary format:</p> <ul> <li> <p>If using
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* the console, <code>{"input0":[1,3,224,224], "input1":[1,3,224,224]}</code> </p>
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* </li> <li> <p>If using the CLI, <code>{\"input0\":[1,3,224,224],
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* \"input1\":[1,3,224,224]} </code> </p> </li> </ul> </li> <li> <p>Example for two
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* inputs in list format: <code>[[1,3,224,224], [1,3,224,224]]</code> </p> </li>
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* </ul> </li> <li> <p> <code>XGBOOST</code>: input data name and shape are not
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* needed.</p> </li> </ul>
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*/
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inline const Aws::String& GetDataInputConfig() const{ return m_dataInputConfig; }
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/**
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* <p>Specifies the name and shape of the expected data inputs for your trained
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* model with a JSON dictionary form. The data inputs are
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* <a>InputConfig$Framework</a> specific. </p> <ul> <li> <p>
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* <code>TensorFlow</code>: You must specify the name and shape (NHWC format) of
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* the expected data inputs using a dictionary format for your trained model. The
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* dictionary formats required for the console and CLI are different.</p> <ul> <li>
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* <p>Examples for one input:</p> <ul> <li> <p>If using the console,
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* <code>{"input":[1,1024,1024,3]}</code> </p> </li> <li> <p>If using the CLI,
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* <code>{\"input\":[1,1024,1024,3]}</code> </p> </li> </ul> </li> <li> <p>Examples
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* for two inputs:</p> <ul> <li> <p>If using the console, <code>{"data1":
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* [1,28,28,1], "data2":[1,28,28,1]}</code> </p> </li> <li> <p>If using the CLI,
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* <code>{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}</code> </p> </li> </ul>
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* </li> </ul> </li> <li> <p> <code>KERAS</code>: You must specify the name and
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* shape (NCHW format) of expected data inputs using a dictionary format for your
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* trained model. Note that while Keras model artifacts should be uploaded in NHWC
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* (channel-last) format, <code>DataInputConfig</code> should be specified in NCHW
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* (channel-first) format. The dictionary formats required for the console and CLI
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* are different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If
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* using the console, <code>{"input_1":[1,3,224,224]}</code> </p> </li> <li> <p>If
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* using the CLI, <code>{\"input_1\":[1,3,224,224]}</code> </p> </li> </ul> </li>
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* <li> <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
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* <code>{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} </code> </p> </li>
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* <li> <p>If using the CLI, <code>{\"input_1\": [1,3,224,224],
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* \"input_2\":[1,3,224,224]}</code> </p> </li> </ul> </li> </ul> </li> <li> <p>
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* <code>MXNET/ONNX</code>: You must specify the name and shape (NCHW format) of
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* the expected data inputs in order using a dictionary format for your trained
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* model. The dictionary formats required for the console and CLI are
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* different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If using
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* the console, <code>{"data":[1,3,1024,1024]}</code> </p> </li> <li> <p>If using
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* the CLI, <code>{\"data\":[1,3,1024,1024]}</code> </p> </li> </ul> </li> <li>
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* <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
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* <code>{"var1": [1,1,28,28], "var2":[1,1,28,28]} </code> </p> </li> <li> <p>If
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* using the CLI, <code>{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}</code> </p>
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* </li> </ul> </li> </ul> </li> <li> <p> <code>PyTorch</code>: You can either
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* specify the name and shape (NCHW format) of expected data inputs in order using
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* a dictionary format for your trained model or you can specify the shape only
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* using a list format. The dictionary formats required for the console and CLI are
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* different. The list formats for the console and CLI are the same.</p> <ul> <li>
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* <p>Examples for one input in dictionary format:</p> <ul> <li> <p>If using the
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* console, <code>{"input0":[1,3,224,224]}</code> </p> </li> <li> <p>If using the
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* CLI, <code>{\"input0\":[1,3,224,224]}</code> </p> </li> </ul> </li> <li>
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* <p>Example for one input in list format: <code>[[1,3,224,224]]</code> </p> </li>
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* <li> <p>Examples for two inputs in dictionary format:</p> <ul> <li> <p>If using
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* the console, <code>{"input0":[1,3,224,224], "input1":[1,3,224,224]}</code> </p>
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* </li> <li> <p>If using the CLI, <code>{\"input0\":[1,3,224,224],
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* \"input1\":[1,3,224,224]} </code> </p> </li> </ul> </li> <li> <p>Example for two
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* inputs in list format: <code>[[1,3,224,224], [1,3,224,224]]</code> </p> </li>
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* </ul> </li> <li> <p> <code>XGBOOST</code>: input data name and shape are not
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* needed.</p> </li> </ul>
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*/
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inline bool DataInputConfigHasBeenSet() const { return m_dataInputConfigHasBeenSet; }
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/**
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* <p>Specifies the name and shape of the expected data inputs for your trained
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* model with a JSON dictionary form. The data inputs are
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* <a>InputConfig$Framework</a> specific. </p> <ul> <li> <p>
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* <code>TensorFlow</code>: You must specify the name and shape (NHWC format) of
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* the expected data inputs using a dictionary format for your trained model. The
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* dictionary formats required for the console and CLI are different.</p> <ul> <li>
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* <p>Examples for one input:</p> <ul> <li> <p>If using the console,
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* <code>{"input":[1,1024,1024,3]}</code> </p> </li> <li> <p>If using the CLI,
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* <code>{\"input\":[1,1024,1024,3]}</code> </p> </li> </ul> </li> <li> <p>Examples
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* for two inputs:</p> <ul> <li> <p>If using the console, <code>{"data1":
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* [1,28,28,1], "data2":[1,28,28,1]}</code> </p> </li> <li> <p>If using the CLI,
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* <code>{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}</code> </p> </li> </ul>
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* </li> </ul> </li> <li> <p> <code>KERAS</code>: You must specify the name and
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* shape (NCHW format) of expected data inputs using a dictionary format for your
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* trained model. Note that while Keras model artifacts should be uploaded in NHWC
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* (channel-last) format, <code>DataInputConfig</code> should be specified in NCHW
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* (channel-first) format. The dictionary formats required for the console and CLI
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* are different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If
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* using the console, <code>{"input_1":[1,3,224,224]}</code> </p> </li> <li> <p>If
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* using the CLI, <code>{\"input_1\":[1,3,224,224]}</code> </p> </li> </ul> </li>
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* <li> <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
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* <code>{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} </code> </p> </li>
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* <li> <p>If using the CLI, <code>{\"input_1\": [1,3,224,224],
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* \"input_2\":[1,3,224,224]}</code> </p> </li> </ul> </li> </ul> </li> <li> <p>
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* <code>MXNET/ONNX</code>: You must specify the name and shape (NCHW format) of
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* the expected data inputs in order using a dictionary format for your trained
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* model. The dictionary formats required for the console and CLI are
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* different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If using
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* the console, <code>{"data":[1,3,1024,1024]}</code> </p> </li> <li> <p>If using
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* the CLI, <code>{\"data\":[1,3,1024,1024]}</code> </p> </li> </ul> </li> <li>
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* <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
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|
|
* <code>{"var1": [1,1,28,28], "var2":[1,1,28,28]} </code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}</code> </p>
|
|||
|
|
* </li> </ul> </li> </ul> </li> <li> <p> <code>PyTorch</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input in dictionary format:</p> <ul> <li> <p>If using the
|
|||
|
|
* console, <code>{"input0":[1,3,224,224]}</code> </p> </li> <li> <p>If using the
|
|||
|
|
* CLI, <code>{\"input0\":[1,3,224,224]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Example for one input in list format: <code>[[1,3,224,224]]</code> </p> </li>
|
|||
|
|
* <li> <p>Examples for two inputs in dictionary format:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"input0":[1,3,224,224], "input1":[1,3,224,224]}</code> </p>
|
|||
|
|
* </li> <li> <p>If using the CLI, <code>{\"input0\":[1,3,224,224],
|
|||
|
|
* \"input1\":[1,3,224,224]} </code> </p> </li> </ul> </li> <li> <p>Example for two
|
|||
|
|
* inputs in list format: <code>[[1,3,224,224], [1,3,224,224]]</code> </p> </li>
|
|||
|
|
* </ul> </li> <li> <p> <code>XGBOOST</code>: input data name and shape are not
|
|||
|
|
* needed.</p> </li> </ul>
|
|||
|
|
*/
|
|||
|
|
inline void SetDataInputConfig(const Aws::String& value) { m_dataInputConfigHasBeenSet = true; m_dataInputConfig = value; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Specifies the name and shape of the expected data inputs for your trained
|
|||
|
|
* model with a JSON dictionary form. The data inputs are
|
|||
|
|
* <a>InputConfig$Framework</a> specific. </p> <ul> <li> <p>
|
|||
|
|
* <code>TensorFlow</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input":[1,1024,1024,3]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"input\":[1,1024,1024,3]}</code> </p> </li> </ul> </li> <li> <p>Examples
|
|||
|
|
* for two inputs:</p> <ul> <li> <p>If using the console, <code>{"data1":
|
|||
|
|
* [1,28,28,1], "data2":[1,28,28,1]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}</code> </p> </li> </ul>
|
|||
|
|
* </li> </ul> </li> <li> <p> <code>KERAS</code>: 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, <code>DataInputConfig</code> should be specified in NCHW
|
|||
|
|
* (channel-first) format. The dictionary formats required for the console and CLI
|
|||
|
|
* are different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If
|
|||
|
|
* using the console, <code>{"input_1":[1,3,224,224]}</code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"input_1\":[1,3,224,224]}</code> </p> </li> </ul> </li>
|
|||
|
|
* <li> <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} </code> </p> </li>
|
|||
|
|
* <li> <p>If using the CLI, <code>{\"input_1\": [1,3,224,224],
|
|||
|
|
* \"input_2\":[1,3,224,224]}</code> </p> </li> </ul> </li> </ul> </li> <li> <p>
|
|||
|
|
* <code>MXNET/ONNX</code>: 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.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"data":[1,3,1024,1024]}</code> </p> </li> <li> <p>If using
|
|||
|
|
* the CLI, <code>{\"data\":[1,3,1024,1024]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"var1": [1,1,28,28], "var2":[1,1,28,28]} </code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}</code> </p>
|
|||
|
|
* </li> </ul> </li> </ul> </li> <li> <p> <code>PyTorch</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input in dictionary format:</p> <ul> <li> <p>If using the
|
|||
|
|
* console, <code>{"input0":[1,3,224,224]}</code> </p> </li> <li> <p>If using the
|
|||
|
|
* CLI, <code>{\"input0\":[1,3,224,224]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Example for one input in list format: <code>[[1,3,224,224]]</code> </p> </li>
|
|||
|
|
* <li> <p>Examples for two inputs in dictionary format:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"input0":[1,3,224,224], "input1":[1,3,224,224]}</code> </p>
|
|||
|
|
* </li> <li> <p>If using the CLI, <code>{\"input0\":[1,3,224,224],
|
|||
|
|
* \"input1\":[1,3,224,224]} </code> </p> </li> </ul> </li> <li> <p>Example for two
|
|||
|
|
* inputs in list format: <code>[[1,3,224,224], [1,3,224,224]]</code> </p> </li>
|
|||
|
|
* </ul> </li> <li> <p> <code>XGBOOST</code>: input data name and shape are not
|
|||
|
|
* needed.</p> </li> </ul>
|
|||
|
|
*/
|
|||
|
|
inline void SetDataInputConfig(Aws::String&& value) { m_dataInputConfigHasBeenSet = true; m_dataInputConfig = std::move(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Specifies the name and shape of the expected data inputs for your trained
|
|||
|
|
* model with a JSON dictionary form. The data inputs are
|
|||
|
|
* <a>InputConfig$Framework</a> specific. </p> <ul> <li> <p>
|
|||
|
|
* <code>TensorFlow</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input":[1,1024,1024,3]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"input\":[1,1024,1024,3]}</code> </p> </li> </ul> </li> <li> <p>Examples
|
|||
|
|
* for two inputs:</p> <ul> <li> <p>If using the console, <code>{"data1":
|
|||
|
|
* [1,28,28,1], "data2":[1,28,28,1]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}</code> </p> </li> </ul>
|
|||
|
|
* </li> </ul> </li> <li> <p> <code>KERAS</code>: 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, <code>DataInputConfig</code> should be specified in NCHW
|
|||
|
|
* (channel-first) format. The dictionary formats required for the console and CLI
|
|||
|
|
* are different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If
|
|||
|
|
* using the console, <code>{"input_1":[1,3,224,224]}</code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"input_1\":[1,3,224,224]}</code> </p> </li> </ul> </li>
|
|||
|
|
* <li> <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} </code> </p> </li>
|
|||
|
|
* <li> <p>If using the CLI, <code>{\"input_1\": [1,3,224,224],
|
|||
|
|
* \"input_2\":[1,3,224,224]}</code> </p> </li> </ul> </li> </ul> </li> <li> <p>
|
|||
|
|
* <code>MXNET/ONNX</code>: 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.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"data":[1,3,1024,1024]}</code> </p> </li> <li> <p>If using
|
|||
|
|
* the CLI, <code>{\"data\":[1,3,1024,1024]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"var1": [1,1,28,28], "var2":[1,1,28,28]} </code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}</code> </p>
|
|||
|
|
* </li> </ul> </li> </ul> </li> <li> <p> <code>PyTorch</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input in dictionary format:</p> <ul> <li> <p>If using the
|
|||
|
|
* console, <code>{"input0":[1,3,224,224]}</code> </p> </li> <li> <p>If using the
|
|||
|
|
* CLI, <code>{\"input0\":[1,3,224,224]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Example for one input in list format: <code>[[1,3,224,224]]</code> </p> </li>
|
|||
|
|
* <li> <p>Examples for two inputs in dictionary format:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"input0":[1,3,224,224], "input1":[1,3,224,224]}</code> </p>
|
|||
|
|
* </li> <li> <p>If using the CLI, <code>{\"input0\":[1,3,224,224],
|
|||
|
|
* \"input1\":[1,3,224,224]} </code> </p> </li> </ul> </li> <li> <p>Example for two
|
|||
|
|
* inputs in list format: <code>[[1,3,224,224], [1,3,224,224]]</code> </p> </li>
|
|||
|
|
* </ul> </li> <li> <p> <code>XGBOOST</code>: input data name and shape are not
|
|||
|
|
* needed.</p> </li> </ul>
|
|||
|
|
*/
|
|||
|
|
inline void SetDataInputConfig(const char* value) { m_dataInputConfigHasBeenSet = true; m_dataInputConfig.assign(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Specifies the name and shape of the expected data inputs for your trained
|
|||
|
|
* model with a JSON dictionary form. The data inputs are
|
|||
|
|
* <a>InputConfig$Framework</a> specific. </p> <ul> <li> <p>
|
|||
|
|
* <code>TensorFlow</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input":[1,1024,1024,3]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"input\":[1,1024,1024,3]}</code> </p> </li> </ul> </li> <li> <p>Examples
|
|||
|
|
* for two inputs:</p> <ul> <li> <p>If using the console, <code>{"data1":
|
|||
|
|
* [1,28,28,1], "data2":[1,28,28,1]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}</code> </p> </li> </ul>
|
|||
|
|
* </li> </ul> </li> <li> <p> <code>KERAS</code>: 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, <code>DataInputConfig</code> should be specified in NCHW
|
|||
|
|
* (channel-first) format. The dictionary formats required for the console and CLI
|
|||
|
|
* are different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If
|
|||
|
|
* using the console, <code>{"input_1":[1,3,224,224]}</code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"input_1\":[1,3,224,224]}</code> </p> </li> </ul> </li>
|
|||
|
|
* <li> <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} </code> </p> </li>
|
|||
|
|
* <li> <p>If using the CLI, <code>{\"input_1\": [1,3,224,224],
|
|||
|
|
* \"input_2\":[1,3,224,224]}</code> </p> </li> </ul> </li> </ul> </li> <li> <p>
|
|||
|
|
* <code>MXNET/ONNX</code>: 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.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"data":[1,3,1024,1024]}</code> </p> </li> <li> <p>If using
|
|||
|
|
* the CLI, <code>{\"data\":[1,3,1024,1024]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"var1": [1,1,28,28], "var2":[1,1,28,28]} </code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}</code> </p>
|
|||
|
|
* </li> </ul> </li> </ul> </li> <li> <p> <code>PyTorch</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input in dictionary format:</p> <ul> <li> <p>If using the
|
|||
|
|
* console, <code>{"input0":[1,3,224,224]}</code> </p> </li> <li> <p>If using the
|
|||
|
|
* CLI, <code>{\"input0\":[1,3,224,224]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Example for one input in list format: <code>[[1,3,224,224]]</code> </p> </li>
|
|||
|
|
* <li> <p>Examples for two inputs in dictionary format:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"input0":[1,3,224,224], "input1":[1,3,224,224]}</code> </p>
|
|||
|
|
* </li> <li> <p>If using the CLI, <code>{\"input0\":[1,3,224,224],
|
|||
|
|
* \"input1\":[1,3,224,224]} </code> </p> </li> </ul> </li> <li> <p>Example for two
|
|||
|
|
* inputs in list format: <code>[[1,3,224,224], [1,3,224,224]]</code> </p> </li>
|
|||
|
|
* </ul> </li> <li> <p> <code>XGBOOST</code>: input data name and shape are not
|
|||
|
|
* needed.</p> </li> </ul>
|
|||
|
|
*/
|
|||
|
|
inline InputConfig& WithDataInputConfig(const Aws::String& value) { SetDataInputConfig(value); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Specifies the name and shape of the expected data inputs for your trained
|
|||
|
|
* model with a JSON dictionary form. The data inputs are
|
|||
|
|
* <a>InputConfig$Framework</a> specific. </p> <ul> <li> <p>
|
|||
|
|
* <code>TensorFlow</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input":[1,1024,1024,3]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"input\":[1,1024,1024,3]}</code> </p> </li> </ul> </li> <li> <p>Examples
|
|||
|
|
* for two inputs:</p> <ul> <li> <p>If using the console, <code>{"data1":
|
|||
|
|
* [1,28,28,1], "data2":[1,28,28,1]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}</code> </p> </li> </ul>
|
|||
|
|
* </li> </ul> </li> <li> <p> <code>KERAS</code>: 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, <code>DataInputConfig</code> should be specified in NCHW
|
|||
|
|
* (channel-first) format. The dictionary formats required for the console and CLI
|
|||
|
|
* are different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If
|
|||
|
|
* using the console, <code>{"input_1":[1,3,224,224]}</code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"input_1\":[1,3,224,224]}</code> </p> </li> </ul> </li>
|
|||
|
|
* <li> <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} </code> </p> </li>
|
|||
|
|
* <li> <p>If using the CLI, <code>{\"input_1\": [1,3,224,224],
|
|||
|
|
* \"input_2\":[1,3,224,224]}</code> </p> </li> </ul> </li> </ul> </li> <li> <p>
|
|||
|
|
* <code>MXNET/ONNX</code>: 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.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"data":[1,3,1024,1024]}</code> </p> </li> <li> <p>If using
|
|||
|
|
* the CLI, <code>{\"data\":[1,3,1024,1024]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"var1": [1,1,28,28], "var2":[1,1,28,28]} </code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}</code> </p>
|
|||
|
|
* </li> </ul> </li> </ul> </li> <li> <p> <code>PyTorch</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input in dictionary format:</p> <ul> <li> <p>If using the
|
|||
|
|
* console, <code>{"input0":[1,3,224,224]}</code> </p> </li> <li> <p>If using the
|
|||
|
|
* CLI, <code>{\"input0\":[1,3,224,224]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Example for one input in list format: <code>[[1,3,224,224]]</code> </p> </li>
|
|||
|
|
* <li> <p>Examples for two inputs in dictionary format:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"input0":[1,3,224,224], "input1":[1,3,224,224]}</code> </p>
|
|||
|
|
* </li> <li> <p>If using the CLI, <code>{\"input0\":[1,3,224,224],
|
|||
|
|
* \"input1\":[1,3,224,224]} </code> </p> </li> </ul> </li> <li> <p>Example for two
|
|||
|
|
* inputs in list format: <code>[[1,3,224,224], [1,3,224,224]]</code> </p> </li>
|
|||
|
|
* </ul> </li> <li> <p> <code>XGBOOST</code>: input data name and shape are not
|
|||
|
|
* needed.</p> </li> </ul>
|
|||
|
|
*/
|
|||
|
|
inline InputConfig& WithDataInputConfig(Aws::String&& value) { SetDataInputConfig(std::move(value)); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Specifies the name and shape of the expected data inputs for your trained
|
|||
|
|
* model with a JSON dictionary form. The data inputs are
|
|||
|
|
* <a>InputConfig$Framework</a> specific. </p> <ul> <li> <p>
|
|||
|
|
* <code>TensorFlow</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input":[1,1024,1024,3]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"input\":[1,1024,1024,3]}</code> </p> </li> </ul> </li> <li> <p>Examples
|
|||
|
|
* for two inputs:</p> <ul> <li> <p>If using the console, <code>{"data1":
|
|||
|
|
* [1,28,28,1], "data2":[1,28,28,1]}</code> </p> </li> <li> <p>If using the CLI,
|
|||
|
|
* <code>{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}</code> </p> </li> </ul>
|
|||
|
|
* </li> </ul> </li> <li> <p> <code>KERAS</code>: 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, <code>DataInputConfig</code> should be specified in NCHW
|
|||
|
|
* (channel-first) format. The dictionary formats required for the console and CLI
|
|||
|
|
* are different.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If
|
|||
|
|
* using the console, <code>{"input_1":[1,3,224,224]}</code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"input_1\":[1,3,224,224]}</code> </p> </li> </ul> </li>
|
|||
|
|
* <li> <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"input_1": [1,3,224,224], "input_2":[1,3,224,224]} </code> </p> </li>
|
|||
|
|
* <li> <p>If using the CLI, <code>{\"input_1\": [1,3,224,224],
|
|||
|
|
* \"input_2\":[1,3,224,224]}</code> </p> </li> </ul> </li> </ul> </li> <li> <p>
|
|||
|
|
* <code>MXNET/ONNX</code>: 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.</p> <ul> <li> <p>Examples for one input:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"data":[1,3,1024,1024]}</code> </p> </li> <li> <p>If using
|
|||
|
|
* the CLI, <code>{\"data\":[1,3,1024,1024]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Examples for two inputs:</p> <ul> <li> <p>If using the console,
|
|||
|
|
* <code>{"var1": [1,1,28,28], "var2":[1,1,28,28]} </code> </p> </li> <li> <p>If
|
|||
|
|
* using the CLI, <code>{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}</code> </p>
|
|||
|
|
* </li> </ul> </li> </ul> </li> <li> <p> <code>PyTorch</code>: 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.</p> <ul> <li>
|
|||
|
|
* <p>Examples for one input in dictionary format:</p> <ul> <li> <p>If using the
|
|||
|
|
* console, <code>{"input0":[1,3,224,224]}</code> </p> </li> <li> <p>If using the
|
|||
|
|
* CLI, <code>{\"input0\":[1,3,224,224]}</code> </p> </li> </ul> </li> <li>
|
|||
|
|
* <p>Example for one input in list format: <code>[[1,3,224,224]]</code> </p> </li>
|
|||
|
|
* <li> <p>Examples for two inputs in dictionary format:</p> <ul> <li> <p>If using
|
|||
|
|
* the console, <code>{"input0":[1,3,224,224], "input1":[1,3,224,224]}</code> </p>
|
|||
|
|
* </li> <li> <p>If using the CLI, <code>{\"input0\":[1,3,224,224],
|
|||
|
|
* \"input1\":[1,3,224,224]} </code> </p> </li> </ul> </li> <li> <p>Example for two
|
|||
|
|
* inputs in list format: <code>[[1,3,224,224], [1,3,224,224]]</code> </p> </li>
|
|||
|
|
* </ul> </li> <li> <p> <code>XGBOOST</code>: input data name and shape are not
|
|||
|
|
* needed.</p> </li> </ul>
|
|||
|
|
*/
|
|||
|
|
inline InputConfig& WithDataInputConfig(const char* value) { SetDataInputConfig(value); return *this;}
|
|||
|
|
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Identifies the framework in which the model was trained. For example:
|
|||
|
|
* TENSORFLOW.</p>
|
|||
|
|
*/
|
|||
|
|
inline const Framework& GetFramework() const{ return m_framework; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Identifies the framework in which the model was trained. For example:
|
|||
|
|
* TENSORFLOW.</p>
|
|||
|
|
*/
|
|||
|
|
inline bool FrameworkHasBeenSet() const { return m_frameworkHasBeenSet; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Identifies the framework in which the model was trained. For example:
|
|||
|
|
* TENSORFLOW.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetFramework(const Framework& value) { m_frameworkHasBeenSet = true; m_framework = value; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Identifies the framework in which the model was trained. For example:
|
|||
|
|
* TENSORFLOW.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetFramework(Framework&& value) { m_frameworkHasBeenSet = true; m_framework = std::move(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Identifies the framework in which the model was trained. For example:
|
|||
|
|
* TENSORFLOW.</p>
|
|||
|
|
*/
|
|||
|
|
inline InputConfig& WithFramework(const Framework& value) { SetFramework(value); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>Identifies the framework in which the model was trained. For example:
|
|||
|
|
* TENSORFLOW.</p>
|
|||
|
|
*/
|
|||
|
|
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
|