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# Frameworks pris en charge, Régions AWS, types d'instances et modèles testés
<a name="training-compiler-support"></a>

**Important**  
Amazon Web Services (AWS) annonce qu'il n'y aura aucune nouvelle version ou version de SageMaker Training Compiler. Vous pouvez continuer à utiliser SageMaker Training Compiler via les conteneurs AWS Deep Learning (DLC) existants pour la SageMaker formation. Il est important de noter que même si les DLC existants restent accessibles, ils ne recevront plus de correctifs ni de mises à jour AWS, conformément à la politique [ de support du ](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/support-policy.html)AWS Deep Learning Containers Framework.

Avant d'utiliser SageMaker Training Compiler, vérifiez si le framework de votre choix est pris en charge, si les types d'instance sont disponibles sur votre AWS compte et si votre AWS compte fait partie de l'un des environnements pris en charge Régions AWS.

**Note**  
SageMaker Training Compiler est disponible dans le SDK SageMaker Python v2.70.0 ou version ultérieure.

## Cadres pris en charge
<a name="training-compiler-supported-frameworks"></a>

SageMaker Training Compiler prend en charge les frameworks d'apprentissage profond suivants et est disponible via les conteneurs AWS Deep Learning.

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

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



- **PyTorch**
  - **Version du cadre:** PyTorch V1.13.1 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/pytorch-trcomp-training : 1.12.0-gpu-py38-cu113-ubuntu20.04 - sagemaker / **Extensible pour personnalisation Docker:** Non
  - **Version du cadre:** PyTorch V1.12.0 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/pytorch-trcomp-training : 1.13.1-gpu-py39-cu117-ubuntu20.04 - sagemaker / **Extensible pour personnalisation Docker:** Non

- **PyTorch avec Hugging Face Transformers**
  - **Version du cadre:** Transformers v4.21.1<br />PyTorch V1.11.0 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/huggingface-pytorch-trcomp-training : 1.11.0-transformateurs 4.21.1-gpu-py38-cu113-ubuntu20.04 / **Extensible pour personnalisation Docker:** Non
  - **Version du cadre:** Transformers v4.17.0<br />PyTorch v1.10.2 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/huggingface-pytorch-trcomp-training : 1.10.2 - transformateurs 4.17.0 - gpu-py38-cu113 - ubuntu20.04 / **Extensible pour personnalisation Docker:** Non
  - **Version du cadre:** Transformers v4.11.0<br />PyTorch V1.9.0 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/huggingface-pytorch-training-comp:1.9.0-transformers4.11.0-gpu-py38-cu111-ubuntu20.04 / **Extensible pour personnalisation Docker:** Non



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



- **TensorFlow**
  - **Version du cadre:** TensorFlow v2.11.0 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/tensorflow-entraînement : 2.11.0-gpu-py39-cu112 - ubuntu20.04 - sagemaker / **Extensible pour personnalisation Docker:** Oui
  - **Version du cadre:** TensorFlow v2.10.0 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/tensorflow-entraînement : 2.10.0-gpu-py39-cu112 - ubuntu20.04 - sagemaker / **Extensible pour personnalisation Docker:** Oui
  - **Version du cadre:** TensorFlow v2.9.1 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/tensorflow-entraînement : 2.9.1 - gpu-py39-cu112 - ubuntu20.04 - sagemaker / **Extensible pour personnalisation Docker:** Oui

- **TensorFlow avec Hugging Face Transformers**
  - **Version du cadre:** Transformers v4.17.0<br />TensorFlow v2.6.3 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/huggingface-tensorflow-trcomp-training : 2.6.3 - transformateurs 4.17.0-gpu-py38-cu112 - ubuntu20.04 / **Extensible pour personnalisation Docker:** Non
  - **Version du cadre:** Transformers v4.11.0<br />TensorFlow v2.5.1 / **URI des Deep Learning Containers:** 763104351884.dkr.ecr. {{<region>}}. amazon. com/huggingface-tensorflow-training-comp:2.5.1-transformateurs 4.11.0-gpu-py37-cu112-ubuntu18.04 / **Extensible pour personnalisation Docker:** Non



Pour plus d'informations, consultez la section Images [ disponibles ](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) dans le GitHub référentiel *AWS Deep Learning Containers*.

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

Les conteneurs [ SageMaker Training Compiler ](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-training-compiler-containers) sont disponibles Régions AWS là où les conteneurs [AWS Deep Learning ](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) sont en service, à l'exception des régions de Chine.

## Types d’instance pris en charge
<a name="training-compiler-supported-instance-types"></a>

SageMaker Training Compiler est testé et prend en charge les types d'instances ML suivants.
+ Instances P4
+ instances P3
+ instances G4dn
+ Instances G5

Pour les spécifications des types d’instances, consultez la section **Calcul accéléré** sur la page [Types d’instances Amazon EC2](https://aws.amazon.com/ec2/instance-types/). Pour plus d'informations sur la tarification des instances, consultez la [ page SageMaker Tarification Amazon](https://aws.amazon.com/sagemaker/pricing/).

Si vous avez rencontré un message d'erreur similaire au suivant, suivez les instructions de la section [ Demander une augmentation du quota de service pour les ressources d' 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.
```

## Modèles testés
<a name="training-compiler-tested-models"></a>

Le tableau suivant inclut la liste des modèles qui ont été testés avec SageMaker Training Compiler. À titre de référence, la plus grande taille de lot pouvant être stockée en mémoire est également incluse aux côtés d'autres paramètres d'entraînement. SageMaker Training Compiler peut modifier l'empreinte mémoire du processus d'apprentissage du modèle ; par conséquent, une taille de lot plus importante peut souvent être utilisée pendant le processus d'apprentissage, ce qui réduit encore la durée totale de formation. Dans certains cas, SageMaker Training Compiler favorise intelligemment la mise en cache, ce qui entraîne une diminution de la plus grande taille de lot pouvant être installée sur le GPU. Vous devez réajuster les hyperparamètres de votre modèle et trouver la taille de lot optimale pour votre cas. Pour gagner du temps, utilisez les tableaux de référence suivants pour rechercher une taille de lot qui peut constituer un bon point de départ pour votre cas d’utilisation.

**Note**  
Les tailles de lots sont des tailles de lots locales qui s’adaptent à chaque GPU individuel dans le type d’instance respectif. Vous devez également ajuster le taux d’apprentissage lorsque vous modifiez la taille du lot.

### PyTorch 1,13,1
<a name="training-compiler-tested-models-pt1131"></a>

**Modèles de traitement du langage naturel (NLP)**

Les modèles suivants sont testés pour les tâches d’entraînement pour toutes les combinaisons de nœuds uniques et multiples avec un ou plusieurs cœurs GPU et une précision mixte automatique (AMP), comme indiqué.


<table>
<thead>
  <tr><th colspan="7">Single-node/multi-nodeunique- GPU/multi-GPU</th></tr>
  <tr><th>Modèle</th><th>Jeu de données</th><th>Type d’instance</th><th>Précision</th><th>Durée de la séquence</th><th>Taille du lot pour les frameworks natifs </th><th>Taille du lot pour 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>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>


**Modèles de vision par ordinateur (CV)**

Testé à l'aide de [ TensorFlow Model Garden ](https://github.com/tensorflow/models) avec Automatic Mixed Precision (AMP) comme indiqué.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-GPU</th></tr>
  <tr><th>Modèle</th><th>Jeu de données</th><th>Type d’instance</th><th>Précision</th><th>Taille du lot pour les frameworks natifs </th><th>Taille du lot pour 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>

**Modèles de traitement du langage naturel (NLP)**

Les modèles suivants sont testés pour les tâches d’entraînement pour toutes les combinaisons de nœuds uniques et multiples avec un ou plusieurs cœurs GPU et une précision mixte automatique (AMP), comme indiqué.


<table>
<thead>
  <tr><th colspan="7">Single-node/multi-nodeunique- GPU/multi-GPU</th></tr>
  <tr><th>Modèle</th><th>Jeu de données</th><th>Type d’instance</th><th>Précision</th><th>Durée de la séquence</th><th>Taille du lot pour les frameworks natifs </th><th>Taille du lot pour 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>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>

**Modèles de vision par ordinateur (CV)**

Testé à l'aide de [ TensorFlow Model Garden ](https://github.com/tensorflow/models) avec Automatic Mixed Precision (AMP) comme indiqué.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-Processeur graphique</th></tr>
  <tr><th>Modèle</th><th>Jeu de données</th><th>Type d’instance</th><th>Précision</th><th>Taille du lot pour les frameworks natifs </th><th>Taille du lot pour SageMaker Training Compiler </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>


**Modèles de traitement du langage naturel (NLP)**

Testé avec des [modèles de transformeur](https://github.com/huggingface/transformers) avec `Sequence_Len=128` et l’option Automatic Mixed Precision (AMP) comme indiqué.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-Processeur graphique</th></tr>
  <tr><th>Modèle</th><th>Jeu de données</th><th>Type d’instance</th><th>Précision</th><th>Taille du lot pour les frameworks natifs </th><th>Taille du lot pour 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-petit discriminateur</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-petit discriminateur</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>

**Modèles de vision par ordinateur (CV)**

Testé à l'aide de [ TensorFlow Model Garden ](https://github.com/tensorflow/models) avec Automatic Mixed Precision (AMP) comme indiqué.


<table>
<thead>
  <tr><th colspan="6">Single-nodeunique- GPU/multi-GPU</th></tr>
  <tr><th>Modèle</th><th>Jeu de données</th><th>Type d’instance</th><th>Précision</th><th>Taille du lot pour les frameworks natifs </th><th>Taille du lot pour SageMaker Training Compiler </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>1 152</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>


**Modèles de traitement du langage naturel (NLP)**

Testé avec des [modèles de transformeur](https://github.com/huggingface/transformers) avec `Sequence_Len=128` et l’option Automatic Mixed Precision (AMP) comme indiqué.


<table>
<thead>
  <tr><th colspan="6">Single-nodeunique- GPU/multi-GPU</th></tr>
  <tr><th>Modèle</th><th>Jeu de données</th><th>Type d’instance</th><th>Précision</th><th>Taille du lot pour les frameworks natifs </th><th>Taille du lot pour 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>

Testé à l'aide de [ TensorFlow Model Garden ](https://github.com/tensorflow/models) avec Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="5">Single-nodeunique- GPU/multi-GPU</th></tr>
  <tr><th>Modèle</th><th>Jeu de données</th><th>Type d’instance</th><th>Taille du lot pour les frameworks natifs </th><th>Taille du lot pour 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>1 024</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>


\* Les tailles de lots marquées d'un astérisque (\*) indiquent la plus grande taille de lot testée par l'équipe de développement de SageMaker Training Compiler. Pour les cellules marquées, l’instance peut éventuellement s’adapter à une taille de lot supérieure à celle indiquée.

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

Testé avec `Sequence_Len=512` et l’option Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="6">Single-node GPU unique</th></tr>
  <tr><th>Modèle </th><th>Jeu de données</th><th>Type d’instance</th><th>Nombre d’instances</th><th>Taille du lot pour les frameworks natifs</th><th>Taille du lot pour 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">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>


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

Testé avec `Sequence_Len=512` et l’option Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="4">Single-node GPU unique</th></tr>
  <tr><th>Modèle </th><th>Type d’instance</th><th>Taille du lot pour les frameworks natifs</th><th>Taille du lot pour 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">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>


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

Testé avec `Sequence_Len=512` et l’option Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="4">Single-node GPU unique</th></tr>
  <tr><th>Modèle </th><th>Type d’instance</th><th>Taille de lot pour natif</th><th>Taille du lot pour 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>masquage des mots entiers en japonais en base cl tohoku/bert -</td><td>ml.p3.2xlarge</td><td>12</td><td>24</td></tr>
  <tr><td>cl- tohoku/bert -base-japonais </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">Single-node Plusieurs processeurs graphiques</th></tr>
  <tr><th>Modèle </th><th>Type d’instance</th><th>Taille de lot pour natif</th><th>Taille du lot pour 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>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>


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

Testé avec `Sequence_Len=128` et l'option Automatic Mixed Precision (AMP).


| Modèle  | Type d’instance | Taille du lot pour les frameworks natifs | Taille du lot pour 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 avec 2.5.1 TensorFlow
<a name="training-compiler-tested-models-hf411-tf251"></a>

Testé avec `Sequence_Len=128` et l’option Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="4">Single-node GPU unique</th></tr>
  <tr><th>Modèle </th><th>Type d’instance</th><th>Taille de lot pour natif</th><th>Taille du lot pour 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-japonais </td><td>ml.p3.2xlarge</td><td>16</td><td>128</td></tr>
  <tr><td>masquage des mots entiers en japonais en base cl tohoku/bert - </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>