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# Estruturas suportadas Regiões da AWS, tipos de instância e modelos testados
<a name="training-compiler-support"></a>

**Importante**  
A Amazon Web Services (AWS) anuncia que não haverá novos lançamentos ou versões do SageMaker Training Compiler. Você pode continuar a utilizar o SageMaker Training Compiler por meio dos AWS Deep Learning Containers (DLCs) existentes para SageMaker treinamento. É importante observar que, embora os existentes DLCs permaneçam acessíveis, eles não receberão mais patches ou atualizações de AWS, de acordo com a [Política de Suporte do AWS Deep Learning Containers Framework](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/support-policy.html).

Antes de usar o SageMaker Training Compiler, verifique se sua estrutura preferida é compatível, se os tipos de instância estão disponíveis na sua Conta da AWS e se a sua Conta da AWS está em uma das suportadas Regiões da AWS.

**nota**  
SageMaker O Training Compiler está disponível no SageMaker Python SDK v2.70.0 ou posterior.

## Estruturas compatíveis
<a name="training-compiler-supported-frameworks"></a>

SageMaker O Training Compiler oferece suporte às seguintes estruturas de aprendizado profundo e está disponível por meio do AWS Deep Learning Containers.

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

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



- **PyTorch**
  - **Versão do framework:** PyTorch v1.13.1 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.12.0-gpu-py38-cu113-ubuntu20.04-sagemaker pytorch-trcomp-training / **Extensível para personalização do Docker:** Não
  - **Versão do framework:** PyTorch v1.12.0 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.13.1-gpu-py39-cu117-ubuntu20.04-sagemaker pytorch-trcomp-training / **Extensível para personalização do Docker:** Não

- **PyTorch com Hugging Face Transformers**
  - **Versão do framework:** Transformadores v4.21.1<br />PyTorch v1.11.0 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.11.0-transformers4.21.1-gpu-py38-cu113-ubuntu20.04 huggingface-pytorch-trcomp-training / **Extensível para personalização do Docker:** Não
  - **Versão do framework:** Transformadores v4.17.0<br />PyTorch v1.10.2 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.10.2-transformers4.17.0-gpu-py38-cu113-ubuntu20.04 huggingface-pytorch-trcomp-training / **Extensível para personalização do Docker:** Não
  - **Versão do framework:** Transformadores v4.11.0<br />PyTorch v1.9.0 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.9.0-transformers4.11.0-gpu-py38-cu111-ubuntu20.04 huggingface-pytorch-training-comp / **Extensível para personalização do Docker:** Não



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



- **TensorFlow**
  - **Versão do framework:** TensorFlow v2.11.0 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/tensorflow-training:2.11.0-gpu-py39-cu112-ubuntu20.04-sagemaker / **Extensível para personalização do Docker:** Sim
  - **Versão do framework:** TensorFlow v2.10.0 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/tensorflow-training:2.10.0-gpu-py39-cu112-ubuntu20.04-sagemaker / **Extensível para personalização do Docker:** Sim
  - **Versão do framework:** TensorFlow v2.9.1 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/tensorflow-training:2.9.1-gpu-py39-cu112-ubuntu20.04-sagemaker / **Extensível para personalização do Docker:** Sim

- **TensorFlow com Hugging Face Transformers**
  - **Versão do framework:** Transformadores v4.17.0<br />TensorFlow v2.6.3 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:2.6.3-transformers4.17.0-gpu-py38-cu112-ubuntu20.04 huggingface-tensorflow-trcomp-training / **Extensível para personalização do Docker:** Não
  - **Versão do framework:** Transformadores v4.11.0<br />TensorFlow v2.5.1 / **Contêineres de aprendizado profundo:** 763104351884.dkr.ecr. {{<region>}}huggingface-tensorflow-training-comp.amazonaws.com/:2.5.1-transformers4.11.0-gpu-py37-cu112-ubuntu18.04 / **Extensível para personalização do Docker:** Não



Para obter mais informações, consulte [Imagens disponíveis](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) no * GitHub repositório AWS Deep Learning Containers*.

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

Os [SageMaker Training Compiler Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-training-compiler-containers) estão disponíveis Regiões da AWS onde os [AWS Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) estão em serviço, exceto nas regiões da China.

## Tipos de instâncias compatíveis
<a name="training-compiler-supported-instance-types"></a>

SageMaker O Training Compiler foi testado e é compatível com os seguintes tipos de instância de ML.
+ Instâncias P4
+ Instâncias P3
+ Instâncias G4dn
+ Instâncias G5

Para especificações dos tipos de instância, consulte a seção **Computação acelerada** na [página Tipos de instância do Amazon EC2](https://aws.amazon.com/ec2/instance-types/). Para obter informações sobre preços de instâncias, consulte [Amazon SageMaker Pricing](https://aws.amazon.com/sagemaker/pricing/).

Se você encontrou uma mensagem de erro semelhante à seguinte, siga as instruções em [Solicitar um aumento da cota de serviço para recursos de SageMaker IA](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure).

```
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.
```

## Modelos testados
<a name="training-compiler-tested-models"></a>

A tabela a seguir inclui uma lista dos modelos que foram testados com o SageMaker Training Compiler. Para referência, o maior tamanho de lote que pode caber na memória também está incluído junto com outros parâmetros de treinamento. SageMaker O Training Compiler pode alterar a pegada de memória do processo de treinamento do modelo; como resultado, um lote maior geralmente pode ser usado durante o processo de treinamento, diminuindo ainda mais o tempo total de treinamento. Em alguns casos, o SageMaker Training Compiler promove o armazenamento em cache de forma inteligente, o que leva a uma diminuição no maior tamanho do lote que cabe na GPU. Você precisa reajustar os hiperparâmetros do seu modelo e encontrar um tamanho de lote (batch size) ideal para o seu caso. Para economizar tempo, use as tabelas de referência a seguir para pesquisar um tamanho de lote que pode ser um bom ponto de partida para seu caso de uso.

**nota**  
Os tamanhos dos lotes são tamanhos de lote locais que cabem em cada GPU individual no respectivo tipo de instância. Você também deve ajustar a taxa de aprendizado ao alterar o tamanho do lote.

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

**Modelos de processamento de linguagem natural (PLN)**

Os modelos a seguir são testados para trabalhos de treinamento para todas as combinações de nó único e vários nós com núcleos de GPU únicas ou várias e precisão mista automática (AMP), conforme indicado.


<table>
<thead>
  <tr><th colspan="7">GPU node/multi-node single-GPU/multi única</th></tr>
  <tr><th>Modelo</th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Precisão</th><th>Comprimento da sequência</th><th>Tamanho do lote para frameworks nativos </th><th>Tamanho do lote para o SageMaker Training Compiler </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>distil2</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>distil2</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>distil2</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>


**Modelos de visão computacional (CV)**

Testado usando o [TensorFlowModel Garden](https://github.com/tensorflow/models) com precisão mista automática (AMP), conforme indicado.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-GPU</th></tr>
  <tr><th>Modelo</th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Precisão</th><th>Tamanho do lote para frameworks nativos </th><th>Tamanho do lote para o SageMaker Training Compiler </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>

**Modelos de processamento de linguagem natural (PLN)**

Os modelos a seguir são testados para trabalhos de treinamento para todas as combinações de nó único e vários nós com núcleos de GPU únicas ou várias e precisão mista automática (AMP), conforme indicado.


<table>
<thead>
  <tr><th colspan="7">GPU node/multi-node single-GPU/multi única</th></tr>
  <tr><th>Modelo</th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Precisão</th><th>Comprimento da sequência</th><th>Tamanho do lote para frameworks nativos </th><th>Tamanho do lote para o SageMaker Training Compiler </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>distil2</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>distil2</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>

**Modelos de visão computacional (CV)**

Testado usando o [TensorFlowModel Garden](https://github.com/tensorflow/models) com precisão mista automática (AMP), conforme indicado.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-GPU</th></tr>
  <tr><th>Modelo</th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Precisão</th><th>Tamanho do lote para frameworks nativos </th><th>Tamanho do lote para o SageMaker Training Compiler </th></tr>
</thead>
<tbody>
  <tr><td>Máscara RCNN - 50-FPN ResNet</td><td>COCO-2017</td><td>ml.g5.2xlarge</td><td>float16</td><td>6</td><td>8</td></tr>
  <tr><td>Máscara RCNN - 50-FPN ResNet</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>


**Modelos de processamento de linguagem natural (PLN)**

Testado usando [modelos Transformadores](https://github.com/huggingface/transformers) com `Sequence_Len=128` e Precisão Mista Automática (AMP), conforme indicado.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-GPU</th></tr>
  <tr><th>Modelo</th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Precisão</th><th>Tamanho do lote para frameworks nativos </th><th>Tamanho do lote para o SageMaker Training Compiler </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>

**Modelos de visão computacional (CV)**

Testado usando o [TensorFlowModel Garden](https://github.com/tensorflow/models) com precisão mista automática (AMP), conforme indicado.


<table>
<thead>
  <tr><th colspan="6">Nó único GPU única/várias GPU</th></tr>
  <tr><th>Modelo</th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Precisão</th><th>Tamanho do lote para frameworks nativos </th><th>Tamanho do lote para o SageMaker Training Compiler </th></tr>
</thead>
<tbody>
  <tr><td>DetectionTransformer- ResNet 50</td><td>COCO-2017</td><td>ml.g4dn.2xlarge</td><td>float32</td><td>2</td><td>4</td></tr>
  <tr><td>DetectionTransformer- ResNet 50</td><td>COCO-2017</td><td>ml.g5.2xlarge</td><td>float32</td><td>3</td><td>6</td></tr>
  <tr><td>DetectionTransformer- ResNet 50</td><td>COCO-2017</td><td>ml.p3.2xlarge</td><td>float32</td><td>2</td><td>4</td></tr>
  <tr><td>Máscara RCNN - 50-FPN ResNet</td><td>COCO-2017</td><td>ml.g4dn.2xlarge</td><td>float16</td><td>4</td><td>6</td></tr>
  <tr><td>Máscara RCNN - 50-FPN ResNet</td><td>COCO-2017</td><td>ml.g5.2xlarge</td><td>float16</td><td>6</td><td>8</td></tr>
  <tr><td>Máscara RCNN - 50-FPN ResNet</td><td>COCO-2017</td><td>ml.g5.48xlarge</td><td>float16</td><td>48</td><td>64</td></tr>
  <tr><td>Máscara RCNN - 50-FPN ResNet</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>


**Modelos de processamento de linguagem natural (PLN)**

Testado usando [modelos Transformadores](https://github.com/huggingface/transformers) com `Sequence_Len=128` e Precisão Mista Automática (AMP), conforme indicado.


<table>
<thead>
  <tr><th colspan="6">Nó único GPU única/várias GPU</th></tr>
  <tr><th>Modelo</th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Precisão</th><th>Tamanho do lote para frameworks nativos </th><th>Tamanho do lote para o SageMaker Training Compiler </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>

Testado usando o [TensorFlowModel Garden](https://github.com/tensorflow/models) com precisão mista automática (AMP).


<table>
<thead>
  <tr><th colspan="5">Nó único GPU única/várias GPU</th></tr>
  <tr><th>Modelo</th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Tamanho do lote para frameworks nativos </th><th>Tamanho do lote para o SageMaker Training Compiler </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>1.280</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- ResNet 50</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">Máscara RCNN - 50-FPN ResNet</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>


\* Os tamanhos de lote marcados com o símbolo de asterisco (\*) indicam o maior tamanho de lote testado pela equipe de desenvolvedores do SageMaker Training Compiler. Para as células marcadas, a instância pode caber em um tamanho de lote maior do que o indicado.

### Transformers 4.21.1 com 1.11.0 PyTorch
<a name="training-compiler-tested-models-hf421-pt111"></a>

Testado com `Sequence_Len=512` precisão mista automática (AMP).


<table>
<thead>
  <tr><th colspan="6">Nó único GPU única</th></tr>
  <tr><th>Modelo </th><th>Conjunto de dados</th><th>Tipo de instância</th><th>Contagem de instâncias</th><th>Tamanho do lote para frameworks nativos</th><th>Tamanho do lote para o Training Compiler</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">distil2</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>


### Transformers 4.17.0 com 1.10.2 PyTorch
<a name="training-compiler-tested-models-hf417-pt110"></a>

Testado com `Sequence_Len=512` precisão mista automática (AMP).


<table>
<thead>
  <tr><th colspan="4">Nó único GPU única</th></tr>
  <tr><th>Modelo </th><th>Tipo de instância</th><th>Tamanho do lote para frameworks nativos</th><th>Tamanho do lote para o Training Compiler</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">distil2</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>


### Transformers 4.11.0 com 1.9.0 PyTorch
<a name="training-compiler-tested-models-hf411-pt190"></a>

Testado com `Sequence_Len=512` precisão mista automática (AMP).


<table>
<thead>
  <tr><th colspan="4">Nó único GPU única</th></tr>
  <tr><th>Modelo </th><th>Tipo de instância</th><th>Tamanho do lote para nativo</th><th>Tamanho do lote para o Training Compiler</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/ -mascaramento de palavras bert-base-japanese-whole</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-inglês</td><td>ml.p3.2xlarge</td><td>28</td><td>32</td></tr>
  <tr><td>distil2 </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>Neimers/MINI LMv2 -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">Nó único várias GPUs</th></tr>
  <tr><th>Modelo </th><th>Tipo de instância</th><th>Tamanho do lote para nativo</th><th>Tamanho do lote para o Training Compiler</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>distil2</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>


### Transformers 4.17.0 com 2.6.3 TensorFlow
<a name="training-compiler-tested-models-hf417-tf263"></a>

Testado com `Sequence_Len=128` precisão mista automática (AMP).


| Modelo  | Tipo de instância | Tamanho do lote para frameworks nativos | Tamanho do lote para o Training Compiler | 
| --- | --- | --- | --- | 
| 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 | 

### Transformers 4.11.0 com 2.5.1 TensorFlow
<a name="training-compiler-tested-models-hf411-tf251"></a>

Testado com `Sequence_Len=128` precisão mista automática (AMP).


<table>
<thead>
  <tr><th colspan="4">Nó único GPU única</th></tr>
  <tr><th>Modelo </th><th>Tipo de instância</th><th>Tamanho do lote para nativo</th><th>Tamanho do lote para o Training Compiler</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/ -mascaramento de palavras bert-base-japanese-whole </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>distil2</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>