本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 支援的架構 AWS 區域、執行個體類型和已測試的模型
<a name="training-compiler-support"></a>

**重要**  
Amazon Web Services (AWS) 宣佈不再推出新版本的 SageMaker Training Compiler。您可以透過現有的 SageMaker 訓練 AWS 深度學習容器 (DLC)，繼續利用 SageMaker Training Compiler。請務必注意，雖然現有 DLCs仍可存取，但根據深度學習容器架構支援政策 AWS，他們將不再收到來自 的修補程式或更新。 [AWS](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/support-policy.html)

使用 SageMaker Training Compiler 之前，請檢查您選擇的架構是否受到支援、您的 中是否有可用的執行個體類型 AWS 帳戶，以及您的 AWS 帳戶 是否位於其中一個支援的 中 AWS 區域。

**注意**  
SageMaker Training Compiler 適用 SageMaker Python SDK v2.70.0 或更新版本。

## 支援的架構
<a name="training-compiler-supported-frameworks"></a>

SageMaker Training Compiler 支援下列深度學習架構，並可透過 AWS 深度學習容器取得。

**Topics**
+ [PyTorch](#training-compiler-supported-frameworks-pytorch)
+ [TensorFlow](#training-compiler-supported-frameworks-tensorflow)

### PyTorch
<a name="training-compiler-supported-frameworks-pytorch"></a>



- **PyTorch**
  - **框架版本:** PyTorch v1.13.1 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/pytorch-trcomp-training:1.12.0-gpu-py38-cu113-ubuntu20.04-sagemaker / **可針對 Docker 自訂進行擴展:** 否
  - **框架版本:** PyTorch v1.12.0 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/pytorch-trcomp-training:1.13.1-gpu-py39-cu117-ubuntu20.04-sagemaker / **可針對 Docker 自訂進行擴展:** 否

- **PyTorch，搭配 Hugging Face 轉換器**
  - **框架版本:** 轉換器 v4.21.1<br />PyTorch v1.11.0 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/huggingface-pytorch-trcomp-training:1.11.0-transformers4.21.1-gpu-py38-cu113-ubuntu20.04 / **可針對 Docker 自訂進行擴展:** 否
  - **框架版本:** 轉換器 v4.17.0<br />PyTorch v1.10.2 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/huggingface-pytorch-trcomp-training:1.10.2-transformers4.17.0-gpu-py38-cu113-ubuntu20.04 / **可針對 Docker 自訂進行擴展:** 否
  - **框架版本:** 轉換器 v4.11.0<br />PyTorch v1.9.0 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/huggingface-pytorch-training-comp:1.9.0-transformers4.11.0-gpu-py38-cu111-ubuntu20.04 / **可針對 Docker 自訂進行擴展:** 否



### TensorFlow
<a name="training-compiler-supported-frameworks-tensorflow"></a>



- **TensorFlow**
  - **框架版本:** TensorFlow v2.11.0 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/tensorflow-training:2.11.0-gpu-py39-cu112-ubuntu20.04-sagemaker / **可針對 Docker 自訂進行擴展:** 是
  - **框架版本:** TensorFlow v2.10.0 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/tensorflow-training:2.10.0-gpu-py39-cu112-ubuntu20.04-sagemaker / **可針對 Docker 自訂進行擴展:** 是
  - **框架版本:** TensorFlow v2.9.1 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/tensorflow-training:2.9.1-gpu-py39-cu112-ubuntu20.04-sagemaker / **可針對 Docker 自訂進行擴展:** 是

- **TensorFlow，搭配 Hugging Face 轉換器**
  - **框架版本:** 轉換器 v4.17.0<br />TensorFlow v2.6.3 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/huggingface-tensorflow-trcomp-training:2.6.3-transformers4.17.0-gpu-py38-cu112-ubuntu20.04 / **可針對 Docker 自訂進行擴展:** 否
  - **框架版本:** 轉換器 v4.11.0<br />TensorFlow v2.5.1 / **深度學習容器 URI:** 763104351884.dkr.ecr.{{<region>}}.amazonaws.com/huggingface-tensorflow-training-comp:2.5.1-transformers4.11.0-gpu-py37-cu112-ubuntu18.04 / **可針對 Docker 自訂進行擴展:** 否



有關詳細資訊，請參閱 *AWS 深度學習容器 GitHub 儲存庫*的[可用映像](https://github.com/aws/deep-learning-containers/blob/master/available_images.md)。

## AWS 區域
<a name="training-compiler-availablity-zone"></a>

[SageMaker Training Compiler Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-training-compiler-containers) 可在[AWS 深度學習容器](https://github.com/aws/deep-learning-containers/blob/master/available_images.md)提供服務的 中 AWS 區域 取得，但中國區域除外。

## 支援的執行個體類型
<a name="training-compiler-supported-instance-types"></a>

SageMaker Training Compiler 已在下列機器學習 (ML) 執行個體類型進行測試並提供支援。
+ P4 執行個體
+ P3 執行個體
+ G4dn 執行個體
+ G5 執行個體

如需執行個體類型規格，請參閱 [Amazon EC2 執行個體類型頁面](https://aws.amazon.com/ec2/instance-types/)的**加速運算**一節。有關執行個體定價資訊，請參閱 [Amazon SageMaker 定價](https://aws.amazon.com/sagemaker/pricing/)。

如果您遇到類似下列內容的錯誤訊息，請按照[請求 SageMaker AI 資源服務配額增加](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure)的指示進行操作。

```
ResourceLimitExceeded: An error occurred (ResourceLimitExceeded) when calling
the CreateTrainingJob operation: The account-level service limit 'ml.p3dn.24xlarge
for training job usage' is 0 Instances, with current utilization of 0 Instances
and a request delta of 1 Instances.
Please contact AWS support to request an increase for this limit.
```

## 測試過的模型
<a name="training-compiler-tested-models"></a>

下表所包含的模型清單均已採用 SageMaker Training Compiler 進行測試。同時列出記憶體可容納的最大批次大小與其他訓練參數以供參考。SageMaker Training Compiler 可變更模型訓練程序的記憶體用量；因此，在訓練過程通常可使用較大批次大小，進一步減少總訓練時間。在某些情況，SageMaker Training Compiler 會以智慧方式提升快取，因而減少 GPU 可容納的最大批次大小。您必須重新調整模型超參數，找到適合您案例的最佳批次大小。若要節省時間，請利用下列參考資料表來查詢批次大小，作為您使用案例的良好起點。

**注意**  
這些批次大小是本機批次大小，適合個別執行個體類型的每個單獨 GPU。在變更批次大小時，您也應調整學習速率。

### PyTorch 1.13.1
<a name="training-compiler-tested-models-pt1131"></a>

**自然語言處理 (NLP) 模型**

下列模型已採用單或多 GPU 核心及自動混合精度 (AMP) 針對所有單節點與多節點組合的訓練工作進行測試，如下所示。


<table>
<thead>
  <tr><th colspan="7">單節點/多節點，單 GPU/多 GPU</th></tr>
  <tr><th>模型</th><th>資料集</th><th>執行個體類型</th><th>精確度</th><th>序列長度</th><th>原生架構的批次大小 </th><th>SageMaker Training Compiler 的批次大小 </th></tr>
</thead>
<tbody>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>128</td><td>80</td><td>192</td></tr>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>128</td><td>332</td></tr>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>128</td><td>80</td><td>224</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>160</td><td>288</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>160</td><td>280</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>240</td><td>472</td></tr>
  <tr><td>distilgpt2</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>128</td><td>77</td><td>128</td></tr>
  <tr><td>distilgpt2</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>138</td><td>390</td></tr>
  <tr><td>distilgpt2</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>128</td><td>96</td><td>256</td></tr>
  <tr><td>distilroberta-base</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>128</td><td>96</td><td>192</td></tr>
  <tr><td>distilroberta-base</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>171</td><td>380</td></tr>
  <tr><td>distilroberta-base</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>128</td><td>112</td><td>256</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>128</td><td>52</td><td>152</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>84</td><td>240</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>128</td><td>58</td><td>164</td></tr>
  <tr><td>microsoft/deberta-base</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>128</td><td>48</td><td>128</td></tr>
  <tr><td>microsoft/deberta-base</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>84</td><td>207</td></tr>
  <tr><td>microsoft/deberta-base</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>128</td><td>53</td><td>133</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>125</td><td>224</td></tr>
  <tr><td>xlm-roberta-base</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>128</td><td>16</td><td>31</td></tr>
  <tr><td>xlm-roberta-base</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>128</td><td>18</td><td>50</td></tr>
  <tr><td>xlnet-base-cased</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>128</td><td>240</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-103-v1</td><td>g5.48xlarge</td><td>float16</td><td>512</td><td>29</td><td>50</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-103-v1</td><td>g5.48xlarge</td><td>float16</td><td>512</td><td>45</td><td>64</td></tr>
  <tr><td>gpt2</td><td>wikitext-103-v1</td><td>g5.48xlarge</td><td>float16</td><td>512</td><td>18</td><td>45</td></tr>
  <tr><td>roberta-base</td><td>wikitext-103-v1</td><td>g5.48xlarge</td><td>float16</td><td>512</td><td>23</td><td>44</td></tr>
  <tr><td>gpt2</td><td>wikitext-103-v1</td><td>p4d.24xlarge</td><td>float16</td><td>512</td><td>36</td><td>64</td></tr>
</tbody>
</table>


**電腦視覺 (CV) 模型**

已採用具有自動混合精度 (AMP) 的 [TensorFlow Model Garden](https://github.com/tensorflow/models)進行測試，如下所示。


<table>
<thead>
  <tr><th colspan="6">單/多節點，單/多 GPU</th></tr>
  <tr><th>模型</th><th>資料集</th><th>執行個體類型</th><th>精確度</th><th>原生架構的批次大小 </th><th>SageMaker Training Compiler 的批次大小 </th></tr>
</thead>
<tbody>
  <tr><td>ResNet152</td><td>food101</td><td>g4dn.16xlarge</td><td>float16</td><td>128</td><td>144</td></tr>
  <tr><td>ResNet152</td><td>food101</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>192</td></tr>
  <tr><td>ResNet152</td><td>food101</td><td>p3.2xlarge</td><td>float16</td><td>152</td><td>156</td></tr>
  <tr><td>ViT</td><td>food101</td><td>g4dn.16xlarge</td><td>float16</td><td>512</td><td>512</td></tr>
  <tr><td>ViT</td><td>food101</td><td>g5.4xlarge</td><td>float16</td><td>992</td><td>768</td></tr>
  <tr><td>ViT</td><td>food101</td><td>p3.2xlarge</td><td>float16</td><td>848</td><td>768</td></tr>
</tbody>
</table>


### PyTorch 1.12.0
<a name="training-compiler-tested-models-pt1120"></a>

**自然語言處理 (NLP) 模型**

下列模型已採用單或多 GPU 核心及自動混合精度 (AMP) 針對所有單節點與多節點組合的訓練工作進行測試，如下所示。


<table>
<thead>
  <tr><th colspan="7">單節點/多節點，單 GPU/多 GPU</th></tr>
  <tr><th>模型</th><th>資料集</th><th>執行個體類型</th><th>精確度</th><th>序列長度</th><th>原生架構的批次大小 </th><th>SageMaker Training Compiler 的批次大小 </th></tr>
</thead>
<tbody>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>128</td><td>248</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>160</td><td>288</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>160</td><td>279</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>105</td><td>164</td></tr>
  <tr><td>distilgpt2</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>136</td><td>256</td></tr>
  <tr><td>distilgpt2</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>80</td><td>118</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>84</td><td>240</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>80</td><td>119</td></tr>
  <tr><td>microsoft/deberta-base</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>93</td><td>197</td></tr>
  <tr><td>microsoft/deberta-base</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>113</td><td>130</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>125</td><td>224</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>78</td><td>112</td></tr>
  <tr><td>xlnet-base-cased</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>138</td><td>240</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>float16</td><td>512</td><td></td><td>52</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>float16</td><td>512</td><td></td><td>160</td></tr>
  <tr><td>gpt2</td><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>float16</td><td>512</td><td></td><td>25</td></tr>
  <tr><td>roberta-base</td><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>float16</td><td>512</td><td></td><td>64</td></tr>
</tbody>
</table>


### TensorFlow 2.11.0
<a name="training-compiler-tested-models-tf2110"></a>

**電腦視覺 (CV) 模型**

已採用具有自動混合精度 (AMP) 的 [TensorFlow Model Garden](https://github.com/tensorflow/models)進行測試，如下所示。


<table>
<thead>
  <tr><th colspan="6">單/多節點，單/多 GPU</th></tr>
  <tr><th>模型</th><th>資料集</th><th>執行個體類型</th><th>精確度</th><th>原生架構的批次大小 </th><th>SageMaker Training Compiler 的批次大小 </th></tr>
</thead>
<tbody>
  <tr><td>MaskRCNN-ResNet50-FPN</td><td>COCO-2017</td><td>ml.g5.2xlarge</td><td>float16</td><td>6</td><td>8</td></tr>
  <tr><td>MaskRCNN-ResNet50-FPN</td><td>COCO-2017</td><td>ml.p3.2xlarge</td><td>float16</td><td>4</td><td>6</td></tr>
  <tr><td>ResNet50</td><td>ImageNet</td><td>ml.g5.2xlarge</td><td>float16</td><td>192</td><td>256</td></tr>
  <tr><td>ResNet50</td><td>ImageNet</td><td>ml.p3.2xlarge</td><td>float16</td><td>256</td><td>256</td></tr>
  <tr><td>ResNet101</td><td>ImageNet</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>256</td></tr>
  <tr><td>ResNet101</td><td>ImageNet</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>128</td></tr>
  <tr><td>ResNet152</td><td>ImageNet</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>224</td></tr>
  <tr><td>ResNet152</td><td>ImageNet</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>128</td></tr>
  <tr><td>VisionTransformer</td><td>ImageNet</td><td>ml.g5.2xlarge</td><td>float16</td><td>112</td><td>144</td></tr>
  <tr><td>VisionTransformer</td><td>ImageNet</td><td>ml.p3.2xlarge</td><td>float16</td><td>96</td><td>128</td></tr>
</tbody>
</table>


**自然語言處理 (NLP) 模型**

採用具 `Sequence_Len=128` 與自動混合精度 (AMP) 的[轉換器模型](https://github.com/huggingface/transformers)進行測試，如下所示。


<table>
<thead>
  <tr><th colspan="6">單/多節點，單/多 GPU</th></tr>
  <tr><th>模型</th><th>資料集</th><th>執行個體類型</th><th>精確度</th><th>原生架構的批次大小 </th><th>SageMaker Training Compiler 的批次大小 </th></tr>
</thead>
<tbody>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>160</td><td>197</td></tr>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>95</td><td>127</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>160</td><td>128</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>104</td><td>111</td></tr>
  <tr><td>bert-large-uncased</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>65</td><td>48</td></tr>
  <tr><td>bert-large-uncased</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>40</td><td>35</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>162</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>105</td><td>111</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>256</td><td>264</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>169</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>120</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>80</td><td>83</td></tr>
  <tr><td>jplu/tf-xlm-roberta-base</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>32</td><td>32</td></tr>
  <tr><td>jplu/tf-xlm-roberta-base</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>32</td><td>36</td></tr>
  <tr><td>microsoft/mpnet-base</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>144</td><td>160</td></tr>
  <tr><td>microsoft/mpnet-base</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>106</td><td>110</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>ml.g5.2xlarge</td><td>float16</td><td>128</td><td>128</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>ml.p3.2xlarge</td><td>float16</td><td>72</td><td>98</td></tr>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>ml.g5.48xlarge</td><td>float16</td><td>128</td><td>192</td></tr>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>ml.p3.16xlarge</td><td>float16</td><td>95</td><td>96</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>ml.g5.48xlarge</td><td>float16</td><td>256</td><td>256</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>ml.p3.16xlarge</td><td>float16</td><td>140</td><td>184</td></tr>
  <tr><td>google/electra-small-discriminator</td><td>wikitext-2-raw-v1</td><td>ml.g5.48xlarge</td><td>float16</td><td>256</td><td>384</td></tr>
  <tr><td>google/electra-small-discriminator</td><td>wikitext-2-raw-v1</td><td>ml.p3.16xlarge</td><td>float16</td><td>256</td><td>268</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>ml.g5.48xlarge</td><td>float16</td><td>116</td><td>116</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>ml.p3.16xlarge</td><td>float16</td><td>85</td><td>83</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>ml.p4d.24xlarge</td><td>float16</td><td>94</td><td>110</td></tr>
  <tr><td>microsoft/mpnet-base</td><td>wikitext-2-raw-v1</td><td>ml.g5.48xlarge</td><td>float16</td><td>187</td><td>164</td></tr>
  <tr><td>microsoft/mpnet-base</td><td>wikitext-2-raw-v1</td><td>ml.p3.16xlarge</td><td>float16</td><td>106</td><td>111</td></tr>
</tbody>
</table>


### TensorFlow 2.10.0
<a name="training-compiler-tested-models-tf2100"></a>

**電腦視覺 (CV) 模型**

已採用具有自動混合精度 (AMP) 的 [TensorFlow Model Garden](https://github.com/tensorflow/models)進行測試，如下所示。


<table>
<thead>
  <tr><th colspan="6">單節點，單 GPU/多 GPU</th></tr>
  <tr><th>模型</th><th>資料集</th><th>執行個體類型</th><th>精確度</th><th>原生架構的批次大小 </th><th>SageMaker Training Compiler 的批次大小 </th></tr>
</thead>
<tbody>
  <tr><td>DetectionTransformer-ResNet50</td><td>COCO-2017</td><td>ml.g4dn.2xlarge</td><td>float32</td><td>2</td><td>4</td></tr>
  <tr><td>DetectionTransformer-ResNet50</td><td>COCO-2017</td><td>ml.g5.2xlarge</td><td>float32</td><td>3</td><td>6</td></tr>
  <tr><td>DetectionTransformer-ResNet50</td><td>COCO-2017</td><td>ml.p3.2xlarge</td><td>float32</td><td>2</td><td>4</td></tr>
  <tr><td>MaskRCNN-ResNet50-FPN</td><td>COCO-2017</td><td>ml.g4dn.2xlarge</td><td>float16</td><td>4</td><td>6</td></tr>
  <tr><td>MaskRCNN-ResNet50-FPN</td><td>COCO-2017</td><td>ml.g5.2xlarge</td><td>float16</td><td>6</td><td>8</td></tr>
  <tr><td>MaskRCNN-ResNet50-FPN</td><td>COCO-2017</td><td>ml.g5.48xlarge</td><td>float16</td><td>48</td><td>64</td></tr>
  <tr><td>MaskRCNN-ResNet50-FPN</td><td>COCO-2017</td><td>ml.p3.2xlarge</td><td>float16</td><td>4</td><td>6</td></tr>
  <tr><td>ResNet50</td><td>ImageNet</td><td>ml.g4dn.2xlarge</td><td>float16</td><td>224</td><td>256</td></tr>
  <tr><td>ResNet50</td><td>ImageNet</td><td>ml.g5.2xlarge</td><td>float16</td><td>192</td><td>160</td></tr>
  <tr><td>ResNet50</td><td>ImageNet</td><td>ml.g5.48xlarge</td><td>float16</td><td>2048</td><td>2048</td></tr>
  <tr><td>ResNet50</td><td>ImageNet</td><td>ml.p3.2xlarge</td><td>float16</td><td>224</td><td>160</td></tr>
  <tr><td>ResNet101</td><td>ImageNet</td><td>ml.g4dn.2xlarge</td><td>float16</td><td>160</td><td>128</td></tr>
  <tr><td>ResNet101</td><td>ImageNet</td><td>ml.g5.2xlarge</td><td>float16</td><td>192</td><td>256</td></tr>
  <tr><td>ResNet101</td><td>ImageNet</td><td>ml.g5.48xlarge</td><td>float16</td><td>2048</td><td>2048</td></tr>
  <tr><td>ResNet101</td><td>ImageNet</td><td>ml.p3.2xlarge</td><td>float16</td><td>160</td><td>224</td></tr>
  <tr><td>ResNet152</td><td>ImageNet</td><td>ml.g4dn.2xlarge</td><td>float16</td><td>128</td><td>128</td></tr>
  <tr><td>ResNet152</td><td>ImageNet</td><td>ml.g5.2xlarge</td><td>float16</td><td>192</td><td>224</td></tr>
  <tr><td>ResNet152</td><td>ImageNet</td><td>ml.g5.48xlarge</td><td>float16</td><td>1536</td><td>1792</td></tr>
  <tr><td>ResNet152</td><td>ImageNet</td><td>ml.p3.2xlarge</td><td>float16</td><td>128</td><td>160</td></tr>
  <tr><td>VisionTransformer</td><td>ImageNet</td><td>ml.g4dn.2xlarge</td><td>float16</td><td>80</td><td>128</td></tr>
  <tr><td>VisionTransformer</td><td>ImageNet</td><td>ml.g5.2xlarge</td><td>float16</td><td>112</td><td>144</td></tr>
  <tr><td>VisionTransformer</td><td>ImageNet</td><td>ml.g5.48xlarge</td><td>float16</td><td>896</td><td>1152</td></tr>
  <tr><td>VisionTransformer</td><td>ImageNet</td><td>ml.p3.2xlarge</td><td>float16</td><td>80</td><td>128</td></tr>
</tbody>
</table>


**自然語言處理 (NLP) 模型**

採用具 `Sequence_Len=128` 與自動混合精度 (AMP) 的[轉換器模型](https://github.com/huggingface/transformers)進行測試，如下所示。


<table>
<thead>
  <tr><th colspan="6">單節點，單 GPU/多 GPU</th></tr>
  <tr><th>模型</th><th>資料集</th><th>執行個體類型</th><th>精確度</th><th>原生架構的批次大小 </th><th>SageMaker Training Compiler 的批次大小 </th></tr>
</thead>
<tbody>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>128</td><td>112</td></tr>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>128</td><td>128</td></tr>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>128</td><td>135</td></tr>
  <tr><td>albert-base-v2</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>191</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>64</td><td>94</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>96</td><td>101</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>96</td><td>96</td></tr>
  <tr><td>bert-base-uncased</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>128</td></tr>
  <tr><td>bert-large-uncased</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>35</td><td>21</td></tr>
  <tr><td>bert-large-uncased</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>39</td><td>26</td></tr>
  <tr><td>bert-large-uncased</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>60</td><td>50</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>96</td><td>90</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>96</td><td>98</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>96</td><td>96</td></tr>
  <tr><td>camembert-base</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>128</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>256</td><td>160</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>128</td><td>176</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>128</td><td>160</td></tr>
  <tr><td>distilbert-base-uncased</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>256</td><td>258</td></tr>
  <tr><td>google\_electra-small-discriminator</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>256</td><td>216</td></tr>
  <tr><td>google\_electra-small-discriminator</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>256</td><td>230</td></tr>
  <tr><td>google\_electra-small-discriminator</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>256</td><td>224</td></tr>
  <tr><td>google\_electra-small-discriminator</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>256</td><td>320</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>80</td><td>64</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>80</td><td>77</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>80</td><td>72</td></tr>
  <tr><td>gpt2</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>120</td></tr>
  <tr><td>jplu\_tf-xlm-roberta-base</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>28</td><td>24</td></tr>
  <tr><td>jplu\_tf-xlm-roberta-base</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>32</td><td>24</td></tr>
  <tr><td>jplu\_tf-xlm-roberta-base</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>32</td><td>26</td></tr>
  <tr><td>jplu\_tf-xlm-roberta-base</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>66</td><td>52</td></tr>
  <tr><td>microsoft\_mpnet-base</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>96</td><td>92</td></tr>
  <tr><td>microsoft\_mpnet-base</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>96</td><td>101</td></tr>
  <tr><td>microsoft\_mpnet-base</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>96</td><td>101</td></tr>
  <tr><td>microsoft\_mpnet-base</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>152</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>g4dn.16xlarge</td><td>float16</td><td>64</td><td>72</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>p3.2xlarge</td><td>float16</td><td>64</td><td>84</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>p3.8xlarge</td><td>float16</td><td>64</td><td>86</td></tr>
  <tr><td>roberta-base</td><td>wikitext-2-raw-v1</td><td>g5.4xlarge</td><td>float16</td><td>128</td><td>128</td></tr>
</tbody>
</table>


### TensorFlow 2.9.1
<a name="training-compiler-tested-models-tf291"></a>

已採用具有自動混合精度 (AMP) 的 [TensorFlow Model Garden](https://github.com/tensorflow/models)進行測試。


<table>
<thead>
  <tr><th colspan="5">單節點，單 GPU/多 GPU</th></tr>
  <tr><th>模型</th><th>資料集</th><th>執行個體類型</th><th>原生架構的批次大小 </th><th>SageMaker Training Compiler 的批次大小 </th></tr>
</thead>
<tbody>
  <tr><td>ResNet50</td><td>ImageNet</td><td>ml.g4dn.2xlarge</td><td>192</td><td>256\*</td></tr>
  <tr><td rowspan="3">ResNet101</td><td rowspan="3">ImageNet</td><td>ml.g4dn.2xlarge</td><td>128</td><td>160</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>224</td><td>256\*</td></tr>
  <tr><td>ml.p3.16xlarge</td><td>1536</td><td>1792</td></tr>
  <tr><td rowspan="3">ResNet152</td><td rowspan="3">ImageNet</td><td>ml.g5.2xlarge</td><td>192</td><td>224</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>160</td><td>160</td></tr>
  <tr><td>ml.p3.16xlarge</td><td>1024</td><td>1280</td></tr>
  <tr><td rowspan="4">VisionTransformer</td><td rowspan="4">ImageNet</td><td>ml.g4dn.2xlarge</td><td>80</td><td>128\*</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>112</td><td>128\*</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>56</td><td>128\*</td></tr>
  <tr><td>ml.p3.16xlarge</td><td>640</td><td>1024\*</td></tr>
  <tr><td rowspan="4">DetectionTransformer-ResNet50</td><td rowspan="4">COCO-2017</td><td>ml.g4dn.2xlarge</td><td>2</td><td>2</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>3</td><td>6</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>2</td><td>4</td></tr>
  <tr><td>ml.p3.16xlarge</td><td>8</td><td>32</td></tr>
  <tr><td rowspan="3">MaskRCNN-ResNet50-FPN</td><td rowspan="3">COCO-2017</td><td>ml.g4dn.2xlarge</td><td>4</td><td>4</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>6</td><td>8</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>4</td><td>6</td></tr>
</tbody>
</table>


\* 標有星號 (\*) 的批次大小表示由 SageMaker Training Compiler 開發人員團隊測試的最大批次大小。對於標記的儲存格，執行個體可能可容納比指示更大的批次大小。

### 轉換器 4.21.1，搭配 PyTorch 1.11.0
<a name="training-compiler-tested-models-hf421-pt111"></a>

已採用 `Sequence_Len=512` 與自動混合精度 (AMP) 進行測試。


<table>
<thead>
  <tr><th colspan="6">單節點，單 GPU</th></tr>
  <tr><th>模型 </th><th>資料集</th><th>執行個體類型</th><th>執行個體計數</th><th>原生架構的批次大小</th><th>Training Compiler 的批次大小</th></tr>
</thead>
<tbody>
  <tr><td rowspan="3">albert-base-v2</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>14</td><td>28</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>18</td><td>40</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>14</td><td>32</td></tr>
  <tr><td rowspan="3">bert-base-cased</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>12</td><td>24</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>28</td><td>44</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>16</td><td>20</td></tr>
  <tr><td rowspan="3">camembert-base</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>16</td><td>28</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>24</td><td>40</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>16</td><td>24</td></tr>
  <tr><td rowspan="4">distilbert-base-uncased</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>28</td><td>52</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>40</td><td>76</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>32</td><td>48</td></tr>
  <tr><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>4</td><td>82</td><td>160</td></tr>
  <tr><td rowspan="3">distilgpt2</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>6</td><td>18</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>12</td><td>28</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>6</td><td>16</td></tr>
  <tr><td rowspan="3">distilroberta-base</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>20</td><td>40</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>28</td><td>56</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>24</td><td>40</td></tr>
  <tr><td rowspan="3">EleutherAI/gpt-neo-125M</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>4</td><td>8</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>6</td><td>14</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>4</td><td>10</td></tr>
  <tr><td rowspan="4">gpt2</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>4</td><td>8</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>6</td><td>16</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>4</td><td>10</td></tr>
  <tr><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>4</td><td>13</td><td>25</td></tr>
  <tr><td rowspan="4">roberta-base</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>12</td><td>20</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>24</td><td>36</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>12</td><td>20</td></tr>
  <tr><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>4</td><td>36</td><td>64</td></tr>
  <tr><td rowspan="3">xlnet-base-cased</td><td rowspan="3">wikitext-2</td><td>ml.g4dn.2xlarge</td><td>1</td><td>2</td><td>6</td></tr>
  <tr><td>ml.g5.2xlarge</td><td>1</td><td>2</td><td>10</td></tr>
  <tr><td>ml.p3.2xlarge</td><td>1</td><td>2</td><td>8</td></tr>
  <tr><td rowspan="4">bert-base-uncased</td><td rowspan="4">wikitext-103-v1</td><td rowspan="4">ml.p4d.24xlarge</td><td>2</td><td>32</td><td>64</td></tr>
  <tr><td>4</td><td>32</td><td>64</td></tr>
  <tr><td>8</td><td>32</td><td>64</td></tr>
  <tr><td>16</td><td>32</td><td>64</td></tr>
  <tr><td>roberta-large</td><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>4</td><td>16</td><td>24</td></tr>
  <tr><td>microsoft/deberta-v3-base</td><td>wikitext-103-v1</td><td>ml.p4d.24xlarge</td><td>16</td><td>9</td><td>23</td></tr>
</tbody>
</table>


### 轉換器 4.17.0，搭配 PyTorch 1.10.2
<a name="training-compiler-tested-models-hf417-pt110"></a>

已採用 `Sequence_Len=512` 與自動混合精度 (AMP) 進行測試。


<table>
<thead>
  <tr><th colspan="4">單節點，單 GPU</th></tr>
  <tr><th>模型 </th><th>執行個體類型</th><th>原生架構的批次大小</th><th>Training Compiler 的批次大小</th></tr>
</thead>
<tbody>
  <tr><td rowspan="2">albert-base-v2</td><td>ml.p3.2xlarge</td><td>14</td><td>28</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>14</td><td>24</td></tr>
  <tr><td rowspan="2">bert-base-cased</td><td>ml.p3.2xlarge</td><td>16</td><td>24</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>12</td><td>24</td></tr>
  <tr><td rowspan="2">bert-base-uncased</td><td>ml.p3.2xlarge</td><td>16</td><td>24</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>12</td><td>28</td></tr>
  <tr><td rowspan="2">camembert-base</td><td>ml.p3.2xlarge</td><td>12</td><td>24</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>12</td><td>28</td></tr>
  <tr><td rowspan="2">distilbert-base-uncased</td><td>ml.p3.2xlarge</td><td>28</td><td>48</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>24</td><td>52</td></tr>
  <tr><td rowspan="2">distilgpt2</td><td>ml.p3.2xlarge</td><td>6</td><td>12</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>6</td><td>14</td></tr>
  <tr><td rowspan="2">distilroberta-base</td><td>ml.p3.2xlarge</td><td>20</td><td>40</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>12</td><td>40</td></tr>
  <tr><td rowspan="2">EleutherAI/gpt-neo-125M</td><td>ml.p3.2xlarge</td><td>2</td><td>10</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>2</td><td>8</td></tr>
  <tr><td rowspan="2">facebook/bart-base</td><td>ml.p3.2xlarge</td><td>2</td><td>6</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>2</td><td>6</td></tr>
  <tr><td rowspan="2">gpt2</td><td>ml.p3.2xlarge</td><td>4</td><td>8</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>2</td><td>8</td></tr>
  <tr><td rowspan="2">roberta-base</td><td>ml.p3.2xlarge</td><td>12</td><td>20</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>12</td><td>20</td></tr>
  <tr><td rowspan="2">xlnet-base-cased</td><td>ml.p3.2xlarge</td><td>2</td><td>8</td></tr>
  <tr><td>ml.g4dn.2xlarge</td><td>4</td><td>6</td></tr>
</tbody>
</table>


### 轉換器 4.11.0，搭配 PyTorch 1.9.0
<a name="training-compiler-tested-models-hf411-pt190"></a>

已採用 `Sequence_Len=512` 與自動混合精度 (AMP) 進行測試。


<table>
<thead>
  <tr><th colspan="4">單節點，單 GPU</th></tr>
  <tr><th>模型 </th><th>執行個體類型</th><th>原生批次大小</th><th>Training Compiler 的批次大小</th></tr>
</thead>
<tbody>
  <tr><td>albert-base-v2 </td><td>ml.p3.2xlarge</td><td>12</td><td>32</td></tr>
  <tr><td>bert-base-cased </td><td>ml.p3.2xlarge</td><td>14</td><td>24</td></tr>
  <tr><td>bert-base-chinese</td><td>ml.p3.2xlarge</td><td>16</td><td>24</td></tr>
  <tr><td>bert-base-multilingual-cased </td><td>ml.p3.2xlarge</td><td>4</td><td>16</td></tr>
  <tr><td>bert-base-multilingual-uncased </td><td>ml.p3.2xlarge</td><td>8</td><td>16</td></tr>
  <tr><td>bert-base-uncased </td><td>ml.p3.2xlarge</td><td>12</td><td>24</td></tr>
  <tr><td>cl-tohoku/bert-base-japanese-whole-word-masking</td><td>ml.p3.2xlarge</td><td>12</td><td>24</td></tr>
  <tr><td>cl-tohoku/bert-base-japanese </td><td>ml.p3.2xlarge</td><td>12</td><td>24</td></tr>
  <tr><td>distilbert-base-uncased </td><td>ml.p3.2xlarge</td><td>28</td><td>32</td></tr>
  <tr><td>distilbert-base-uncased-finetuned-sst-2-english</td><td>ml.p3.2xlarge</td><td>28</td><td>32</td></tr>
  <tr><td>distilgpt2 </td><td>ml.p3.2xlarge</td><td>16</td><td>32</td></tr>
  <tr><td>facebook/bart-base </td><td>ml.p3.2xlarge</td><td>4</td><td>8</td></tr>
  <tr><td>gpt2</td><td>ml.p3.2xlarge</td><td>6</td><td>20</td></tr>
  <tr><td>nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large </td><td>ml.p3.2xlarge</td><td>20</td><td>32</td></tr>
  <tr><td>roberta-base </td><td>ml.p3.2xlarge</td><td>12</td><td>20</td></tr>
</tbody>
</table>



<table>
<thead>
  <tr><th colspan="4">單節點，多 GPU</th></tr>
  <tr><th>模型 </th><th>執行個體類型</th><th>原生批次大小</th><th>Training Compiler 的批次大小</th></tr>
</thead>
<tbody>
  <tr><td>bert-base-chinese </td><td>ml.p3.8xlarge</td><td>16</td><td>26</td></tr>
  <tr><td>bert-base-multilingual-cased </td><td>ml.p3.8xlarge</td><td>6</td><td>16</td></tr>
  <tr><td>bert-base-multilingual-uncased</td><td>ml.p3.8xlarge</td><td>6</td><td>16</td></tr>
  <tr><td>bert-base-uncased </td><td>ml.p3.8xlarge</td><td>14</td><td>24</td></tr>
  <tr><td>distilbert-base-uncased </td><td>ml.p3.8xlarge</td><td>14</td><td>32</td></tr>
  <tr><td>distilgpt2</td><td>ml.p3.8xlarge</td><td>6</td><td>32</td></tr>
  <tr><td>facebook/bart-base</td><td>ml.p3.8xlarge</td><td>8</td><td>16</td></tr>
  <tr><td>gpt2 </td><td>ml.p3.8xlarge</td><td>8</td><td>20</td></tr>
  <tr><td>roberta-base </td><td>ml.p3.8xlarge</td><td>12</td><td>20</td></tr>
</tbody>
</table>


### 轉換器 4.17.0，搭配 TensorFlow 2.6.3
<a name="training-compiler-tested-models-hf417-tf263"></a>

已採用 `Sequence_Len=128` 與自動混合精度 (AMP) 進行測試。


| 模型  | 執行個體類型 | 原生架構的批次大小 | Training Compiler 的批次大小 | 
| --- | --- | --- | --- | 
| albert-base-v2 | ml.g4dn.16xlarge | 136 | 208 | 
| albert-base-v2 | ml.g5.4xlarge | 219 | 312 | 
| albert-base-v2 | ml.p3.2xlarge | 152 | 208 | 
| albert-base-v2 | ml.p3.8xlarge | 152 | 192 | 
| bert-base-uncased | ml.g4dn.16xlarge | 120 | 101 | 
| bert-base-uncased | ml.g5.4xlarge | 184 | 160 | 
| bert-base-uncased | ml.p3.2xlarge | 128 | 108 | 
| bert-large-uncased | ml.g4dn.16xlarge | 37 | 28 | 
| bert-large-uncased | ml.g5.4xlarge | 64 | 55 | 
| bert-large-uncased | ml.p3.2xlarge | 40 | 32 | 
| camembert-base | ml.g4dn.16xlarge | 96 | 100 | 
| camembert-base | ml.g5.4xlarge | 190 | 160 | 
| camembert-base | ml.p3.2xlarge | 129 | 108 | 
| camembert-base | ml.p3.8xlarge | 128 | 104 | 
| distilbert-base-uncased | ml.g4dn.16xlarge | 210 | 160 | 
| distilbert-base-uncased | ml.g5.4xlarge | 327 | 288 | 
| distilbert-base-uncased | ml.p3.2xlarge | 224 | 196 | 
| distilbert-base-uncased | ml.p3.8xlarge | 192 | 182 | 
| google\_electra-small-discriminator | ml.g4dn.16xlarge | 336 | 288 | 
| google\_electra-small-discriminator | ml.g5.4xlarge | 504 | 384 | 
| google\_electra-small-discriminator | ml.p3.2xlarge | 352 | 323 | 
| gpt2 | ml.g4dn.16xlarge | 89 | 64 | 
| gpt2 | ml.g5.4xlarge | 140 | 146 | 
| gpt2 | ml.p3.2xlarge | 94 | 96 | 
| gpt2 | ml.p3.8xlarge | 96 | 88 | 
| jplu\_tf-xlm-roberta-base | ml.g4dn.16xlarge | 52 | 16 | 
| jplu\_tf-xlm-roberta-base | ml.g5.4xlarge | 64 | 44 | 
| microsoft\_mpnet-base | ml.g4dn.16xlarge | 120 | 100 | 
| microsoft\_mpnet-base | ml.g5.4xlarge | 192 | 160 | 
| microsoft\_mpnet-base | ml.p3.2xlarge | 128 | 104 | 
| microsoft\_mpnet-base | ml.p3.8xlarge | 130 | 92 | 
| roberta-base | ml.g4dn.16xlarge | 108 | 64 | 
| roberta-base | ml.g5.4xlarge | 176 | 142 | 
| roberta-base | ml.p3.2xlarge | 118 | 100 | 
| roberta-base | ml.p3.8xlarge | 112 | 88 | 

### 轉換器 4.11.0，搭配 TensorFlow 2.5.1
<a name="training-compiler-tested-models-hf411-tf251"></a>

已採用 `Sequence_Len=128` 與自動混合精度 (AMP) 進行測試。


<table>
<thead>
  <tr><th colspan="4">單節點，單 GPU</th></tr>
  <tr><th>模型 </th><th>執行個體類型</th><th>原生批次大小</th><th>Training Compiler 的批次大小</th></tr>
</thead>
<tbody>
  <tr><td>albert-base-v2 </td><td>ml.p3.2xlarge</td><td>128</td><td>128</td></tr>
  <tr><td>bart-base </td><td>ml.p3.2xlarge</td><td>12</td><td>64</td></tr>
  <tr><td>bart-large </td><td>ml.p3.2xlarge</td><td>4</td><td>28</td></tr>
  <tr><td>bert-base-cased </td><td>ml.p3.2xlarge</td><td>16</td><td>128</td></tr>
  <tr><td>bert-base-chinese</td><td>ml.p3.2xlarge</td><td>16</td><td>128</td></tr>
  <tr><td>bert-base-multilingual-cased </td><td>ml.p3.2xlarge</td><td>12</td><td>64</td></tr>
  <tr><td>bert-base-multilingual-uncased </td><td>ml.p3.2xlarge</td><td>16</td><td>96</td></tr>
  <tr><td>bert-base-uncased</td><td>ml.p3.2xlarge</td><td>16</td><td>96</td></tr>
  <tr><td>bert-large-uncased </td><td>ml.p3.2xlarge</td><td>4</td><td>24</td></tr>
  <tr><td>cl-tohoku/bert-base-japanese </td><td>ml.p3.2xlarge</td><td>16</td><td>128</td></tr>
  <tr><td>cl-tohoku/bert-base-japanese-whole-word-masking </td><td>ml.p3.2xlarge</td><td>16</td><td>128</td></tr>
  <tr><td>distilbert-base-sst2 </td><td>ml.p3.2xlarge</td><td>32</td><td>128</td></tr>
  <tr><td>distilbert-base-uncased </td><td>ml.p3.2xlarge</td><td>32</td><td>128</td></tr>
  <tr><td>distilgpt2</td><td>ml.p3.2xlarge</td><td>32</td><td>128</td></tr>
  <tr><td>gpt2 </td><td>ml.p3.2xlarge</td><td>12</td><td>64</td></tr>
  <tr><td>gpt2-large </td><td>ml.p3.2xlarge</td><td>2</td><td>24</td></tr>
  <tr><td>jplu/tf-xlm-roberta-base </td><td>ml.p3.2xlarge</td><td>12</td><td>32</td></tr>
  <tr><td>roberta-base </td><td>ml.p3.2xlarge</td><td>4</td><td>64</td></tr>
  <tr><td>roberta-large </td><td>ml.p3.2xlarge</td><td>4</td><td>64</td></tr>
  <tr><td>t5-base </td><td>ml.p3.2xlarge</td><td>64</td><td>64</td></tr>
  <tr><td>t5-small </td><td>ml.p3.2xlarge</td><td>128</td><td>128</td></tr>
</tbody>
</table>