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# Framework supportati, tipi di istanze e modelli Regioni AWS testati
<a name="training-compiler-support"></a>

**Importante**  
Amazon Web Services (AWS) annuncia che non ci saranno nuove release o versioni di SageMaker Training Compiler. Puoi continuare a utilizzare SageMaker Training Compiler tramite gli esistenti AWS Deep Learning Containers (DLCs) for SageMaker Training. È importante notare che, sebbene gli esistenti DLCs rimangano accessibili, non riceveranno più patch o aggiornamenti da AWS, in conformità con la [politica di supporto del AWS Deep Learning Containers Framework](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/support-policy.html).

Prima di utilizzare SageMaker Training Compiler, verifica se il framework che preferisci è supportato, se i tipi di istanza sono disponibili nel tuo Account AWS sistema e se il tuo Account AWS è in uno dei formati supportati. Regioni AWS

**Nota**  
SageMaker Training Compiler è disponibile in SageMaker Python SDK v2.70.0 o versione successiva.

## Framework supportati
<a name="training-compiler-supported-frameworks"></a>

SageMaker Training Compiler supporta i seguenti framework di deep learning ed è disponibile tramite 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**
  - **Versione di Framework:** PyTorch v1.13.1 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.12.0-gpu-py38-cu113-ubuntu20.04-sagemaker pytorch-trcomp-training / **Estendibile per la personalizzazione di Docker:** No
  - **Versione di Framework:** PyTorch v1.12.0 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.13.1-gpu-py39-cu117-ubuntu20.04-sagemaker pytorch-trcomp-training / **Estendibile per la personalizzazione di Docker:** No

- **PyTorch con Hugging Face Transformers**
  - **Versione di Framework:** Transformers v4.21.1<br />PyTorch v1.11.0 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.11.0-transformers4.21.1-gpu-py38-cu113-ubuntu20.04 huggingface-pytorch-trcomp-training / **Estendibile per la personalizzazione di Docker:** No
  - **Versione di Framework:** Transformers v4.17.0<br />PyTorch v1.10.2 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.10.2-transformers4.17.0-gpu-py38-cu113-ubuntu20.04 huggingface-pytorch-trcomp-training / **Estendibile per la personalizzazione di Docker:** No
  - **Versione di Framework:** Transformers v4.11.0<br />PyTorch v1.9.0 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:1.9.0-transformers4.11.0-gpu-py38-cu111-ubuntu20.04 huggingface-pytorch-training-comp / **Estendibile per la personalizzazione di Docker:** No



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



- **TensorFlow**
  - **Versione di Framework:** TensorFlow v2.11.0 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/tensorflow-training:2.11.0-gpu-py39-cu112-ubuntu20.04-sagemaker / **Estendibile per la personalizzazione di Docker:** Sì
  - **Versione di Framework:** TensorFlow v2.10.0 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/tensorflow-training:2.10.0-gpu-py39-cu112-ubuntu20.04-sagemaker / **Estendibile per la personalizzazione di Docker:** Sì
  - **Versione di Framework:** TensorFlow v2.9.1 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/tensorflow-training:2.9.1-gpu-py39-cu112-ubuntu20.04-sagemaker / **Estendibile per la personalizzazione di Docker:** Sì

- **TensorFlow con Hugging Face Transformers**
  - **Versione di Framework:** Transformers v4.17.0<br />TensorFlow v2.6.3 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:2.6.3-transformers4.17.0-gpu-py38-cu112-ubuntu20.04 huggingface-tensorflow-trcomp-training / **Estendibile per la personalizzazione di Docker:** No
  - **Versione di Framework:** Transformers v4.11.0<br />TensorFlow v2.5.1 / **URI del container Deep Learning:** 763104351884.dkr.ecr. {{<region>}}.amazonaws.com/:2.5.1-transformers4.11.0-gpu-py37-cu112-ubuntu18,04 huggingface-tensorflow-training-comp / **Estendibile per la personalizzazione di Docker:** No



Per ulteriori informazioni, consulta [Immagini disponibili](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) nel * GitHub repository AWS Deep Learning Containers*.

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

I [SageMaker Training Compiler Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-training-compiler-containers) sono disponibili nelle aree in Regioni AWS cui sono in servizio i [AWS Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md), ad eccezione delle regioni della Cina.

## Tipi di istanze supportati
<a name="training-compiler-supported-instance-types"></a>

SageMaker Training Compiler è testato e supporta i seguenti tipi di istanze ML.
+ Istanze P4
+ Istanze P3
+ Istanze G4dn
+ Istanze G5

Per le specifiche dei tipi di istanza, consultare la sezione **Accelerated Computing** nella [pagina Amazon EC2 Instance Types](https://aws.amazon.com/ec2/instance-types/). Per informazioni sui prezzi delle istanze, consulta la pagina [ SageMaker Prezzi di Amazon](https://aws.amazon.com/sagemaker/pricing/).

Se hai riscontrato un messaggio di errore simile al seguente, segui le istruzioni in [Richiedi un aumento della quota di servizio per le risorse di SageMaker intelligenza artificiale](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.
```

## Modelli testati
<a name="training-compiler-tested-models"></a>

La tabella seguente include un elenco dei modelli che sono stati testati con SageMaker Training Compiler. A titolo di riferimento, oltre ad altri parametri di allenamento viene inclusa anche la dimensione del batch più grande che è possibile memorizzare. SageMaker Training Compiler può modificare l'impronta di memoria del processo di addestramento del modello; di conseguenza, durante il processo di addestramento è spesso possibile utilizzare batch di dimensioni maggiori, riducendo ulteriormente il tempo di addestramento totale. In alcuni casi, SageMaker Training Compiler promuove in modo intelligente la memorizzazione nella cache, il che porta a una riduzione della dimensione del batch più grande che può contenere la GPU. È necessario ottimizzare nuovamente gli iperparametri del modello e trovare dimensioni del batch ottimale per il proprio caso. Per risparmiare tempo, utilizzare le seguenti tabelle di riferimento per cercare dimensioni del batch che possa essere un buon punto di partenza per il proprio caso d'uso.

**Nota**  
Le dimensioni del batch sono quelle del batch locale che si adattano a ogni singola GPU nel rispettivo tipo di istanza. Quando si modificano le dimensioni del batch è inoltre necessario modificare il tasso di apprendimento.

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

**Modelli di elaborazione del linguaggio naturale**

I seguenti modelli sono testati per processi di addestramento per tutte le combinazioni di nodo singolo e multinodo con core GPU singoli o multipli e Automatic Mixed Precision (AMP) come indicato.


<table>
<thead>
  <tr><th colspan="7">GPU singola node/multi-node single-GPU/multi</th></tr>
  <tr><th>Modello</th><th>Set di dati</th><th>Tipo di istanza</th><th>Precisione</th><th>Lunghezza della sequenza</th><th>Dimensioni batch per framework nativi </th><th>Dimensione del batch per 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>distillroberta-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>distillroberta-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>distillroberta-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>


**Modelli di visione artificiale (CV)**

Testato utilizzando [TensorFlowModel Garden](https://github.com/tensorflow/models) with Automatic Mixed Precision (AMP) come indicato.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-GPU</th></tr>
  <tr><th>Modello</th><th>Set di dati</th><th>Tipo di istanza</th><th>Precisione</th><th>Dimensioni batch per framework nativi </th><th>Dimensione del batch per 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>

**Modelli di elaborazione del linguaggio naturale**

I seguenti modelli sono testati per processi di addestramento per tutte le combinazioni di nodo singolo e multinodo con core GPU singoli o multipli e Automatic Mixed Precision (AMP) come indicato.


<table>
<thead>
  <tr><th colspan="7">GPU singola node/multi-node single-GPU/multi</th></tr>
  <tr><th>Modello</th><th>Set di dati</th><th>Tipo di istanza</th><th>Precisione</th><th>Lunghezza della sequenza</th><th>Dimensioni batch per framework nativi </th><th>Dimensione del batch per 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 211,0
<a name="training-compiler-tested-models-tf2110"></a>

**Modelli di visione artificiale (CV)**

Testato utilizzando [TensorFlowModel Garden](https://github.com/tensorflow/models) with Automatic Mixed Precision (AMP) come indicato.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-GPU</th></tr>
  <tr><th>Modello</th><th>Set di dati</th><th>Tipo di istanza</th><th>Precisione</th><th>Dimensioni batch per framework nativi </th><th>Dimensione del batch per SageMaker Training Compiler </th></tr>
</thead>
<tbody>
  <tr><td>Maschera CNN - 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>Maschera RCNN - ResNet 50-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>


**Modelli di elaborazione del linguaggio naturale**

Testato utilizzando i [modelli Transformer](https://github.com/huggingface/transformers) con `Sequence_Len=128` e Automatic Mixed Precision (AMP), come indicato.


<table>
<thead>
  <tr><th colspan="6">Single/multi-node single/multi-GPU</th></tr>
  <tr><th>Modello</th><th>Set di dati</th><th>Tipo di istanza</th><th>Precisione</th><th>Dimensioni batch per framework nativi </th><th>Dimensione del batch per 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>

**Modelli di visione artificiale (CV)**

Testato utilizzando [TensorFlowModel Garden](https://github.com/tensorflow/models) with Automatic Mixed Precision (AMP) come indicato.


<table>
<thead>
  <tr><th colspan="6">GPU a nodo singolo/multinodo GPU a nodo singolo/multinodo</th></tr>
  <tr><th>Modello</th><th>Set di dati</th><th>Tipo di istanza</th><th>Precisione</th><th>Dimensioni batch per framework nativi </th><th>Dimensione del batch per SageMaker Training Compiler </th></tr>
</thead>
<tbody>
  <tr><td>DetectionTransformer- 50 ResNet</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>Maschera CNN - 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>Maschera RCNN - ResNet 50-FPN</td><td>COCO-2017</td><td>ml.g5.2xlarge</td><td>float16</td><td>6</td><td>8</td></tr>
  <tr><td>Maschera RCNN - ResNet 50-FPN</td><td>COCO-2017</td><td>ml.g5.48xlarge</td><td>float16</td><td>48</td><td>64</td></tr>
  <tr><td>Maschera RCNN - ResNet 50-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>


**Modelli di elaborazione del linguaggio naturale**

Testato utilizzando i [modelli Transformer](https://github.com/huggingface/transformers) con `Sequence_Len=128` e Automatic Mixed Precision (AMP), come indicato.


<table>
<thead>
  <tr><th colspan="6">GPU a nodo singolo/multinodo GPU a nodo singolo/multinodo</th></tr>
  <tr><th>Modello</th><th>Set di dati</th><th>Tipo di istanza</th><th>Precisione</th><th>Dimensioni batch per framework nativi </th><th>Dimensione del batch per 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 29.1
<a name="training-compiler-tested-models-tf291"></a>

Testato utilizzando [TensorFlowModel Garden](https://github.com/tensorflow/models) con Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="5">GPU a nodo singolo/multinodo GPU a nodo singolo/multinodo</th></tr>
  <tr><th>Modello</th><th>Set di dati</th><th>Tipo di istanza</th><th>Dimensioni batch per framework nativi </th><th>Dimensione del batch per 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- 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">Maschera CNN - 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>


\* Le dimensioni del batch contrassegnate dal simbolo dell'asterisco (\*) indicano la dimensione del batch più grande testata dal team di sviluppatori di Training Compiler. SageMaker Per le celle contrassegnate, l'istanza potrebbe essere in grado di contenere un batch di dimensioni maggiori di quelle indicate.

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

Testato con `Sequence_Len=512` e Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="6">GPU a nodo singolo</th></tr>
  <tr><th>Modello </th><th>Set di dati</th><th>Tipo di istanza</th><th>Conteggio delle istanze</th><th>Dimensioni batch per framework nativi</th><th>Dimensioni batch per 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">distillroberta-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 con 1.10.2 PyTorch
<a name="training-compiler-tested-models-hf417-pt110"></a>

Testato con `Sequence_Len=512` e Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="4">GPU a nodo singolo</th></tr>
  <tr><th>Modello </th><th>Tipo di istanza</th><th>Dimensioni batch per framework nativi</th><th>Dimensioni batch per 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">distillroberta-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 con PyTorch 1.9.0
<a name="training-compiler-tested-models-hf411-pt190"></a>

Testato con `Sequence_Len=512` e Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="4">GPU a nodo singolo</th></tr>
  <tr><th>Modello </th><th>Tipo di istanza</th><th>Dimensioni batch per nativi</th><th>Dimensioni batch per 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>bert-base-japanese-wholecl-tohoku/ -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-inglese</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>Neimers/mini -L6-H384- LMv2 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">GPU multipla a nodo singolo</th></tr>
  <tr><th>Modello </th><th>Tipo di istanza</th><th>Dimensioni batch per nativi</th><th>Dimensioni batch per 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 con 2.6.3 TensorFlow
<a name="training-compiler-tested-models-hf417-tf263"></a>

Testato con `Sequence_Len=128` e Automatic Mixed Precision (AMP).


| Modello  | Tipo di istanza | Dimensioni batch per framework nativi | Dimensioni batch per 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 con 2.5.1 TensorFlow
<a name="training-compiler-tested-models-hf411-tf251"></a>

Testato con `Sequence_Len=128` e Automatic Mixed Precision (AMP).


<table>
<thead>
  <tr><th colspan="4">GPU a nodo singolo</th></tr>
  <tr><th>Modello </th><th>Tipo di istanza</th><th>Dimensioni batch per nativi</th><th>Dimensioni batch per 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/ bert-base-japanese-whole -mascheramento di parole </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>