Supported Frameworks, Amazon Web Services Regions, Instance Types, and Tested Models - Amazon SageMaker
Supported FrameworksAmazon Web Services RegionsSupported Instance TypesTested Models
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Supported Frameworks, Amazon Web Services Regions, Instance Types, and Tested Models

Before using SageMaker Training Compiler, check if your framework of choice is supported, the instance types are available in your Amazon account, and your Amazon account is in one of the supported Amazon Web Services Regions.

Note

SageMaker Training Compiler is available in the SageMaker Python SDK v2.70.0 or later.

Supported Frameworks

SageMaker Training Compiler supports the following deep learning frameworks and is available through Amazon Deep Learning Containers.

PyTorch

Framework Framework version Deep Learning Container URI Extendable for Docker customization
PyTorch PyTorch v1.13.1 763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-trcomp-training:1.12.0-gpu-py38-cu113-ubuntu20.04-sagemaker No
PyTorch v1.12.0 763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-trcomp-training:1.13.1-gpu-py39-cu117-ubuntu20.04-sagemaker No
PyTorch with Hugging Face Transformers

Transformers v4.21.1

PyTorch v1.11.0

763104351884.dkr.ecr.<region>.amazonaws.com/huggingface-pytorch-trcomp-training:1.11.0-transformers4.21.1-gpu-py38-cu113-ubuntu20.04

 No

Transformers v4.17.0

PyTorch v1.10.2

763104351884.dkr.ecr.<region>.amazonaws.com/huggingface-pytorch-trcomp-training:1.10.2-transformers4.17.0-gpu-py38-cu113-ubuntu20.04

 No

Transformers v4.11.0

PyTorch v1.9.0

763104351884.dkr.ecr.<region>.amazonaws.com/huggingface-pytorch-training-comp:1.9.0-transformers4.11.0-gpu-py38-cu111-ubuntu20.04

 No

TensorFlow

Framework Framework version Deep Learning Container URI Extendable for Docker customization
TensorFlow

TensorFlow v2.11.0

763104351884.dkr.ecr.<region>.amazonaws.com/tensorflow-training:2.11.0-gpu-py39-cu112-ubuntu20.04-sagemaker

 Yes

TensorFlow v2.10.0

763104351884.dkr.ecr.<region>.amazonaws.com/tensorflow-training:2.10.0-gpu-py39-cu112-ubuntu20.04-sagemaker

 Yes

TensorFlow v2.9.1

763104351884.dkr.ecr.<region>.amazonaws.com/tensorflow-training:2.9.1-gpu-py39-cu112-ubuntu20.04-sagemaker

 Yes
TensorFlow with Hugging Face Transformers

Transformers v4.17.0

TensorFlow v2.6.3

763104351884.dkr.ecr.<region>.amazonaws.com/huggingface-tensorflow-trcomp-training:2.6.3-transformers4.17.0-gpu-py38-cu112-ubuntu20.04

 No

Transformers v4.11.0

TensorFlow v2.5.1

763104351884.dkr.ecr.<region>.amazonaws.com/huggingface-tensorflow-training-comp:2.5.1-transformers4.11.0-gpu-py37-cu112-ubuntu18.04

 No

For more information, see Available Images in the Amazon Deep Learning Containers GitHub repository.

Amazon Web Services Regions

The SageMaker Training Compiler Containers are available in the Amazon Web Services Regions where Amazon Deep Learning Containers are in service except the China regions.

Supported Instance Types

SageMaker Training Compiler is tested on and supports the following ML instance types.

  • P4 instances

  • P3 instances

  • G4dn instances

  • G5 instances

For specs of the instance types, see the Accelerated Computing section in the Amazon EC2 Instance Types page. For information about instance pricing, see Amazon SageMaker Pricing.

If you encountered an error message similar to the following, follow the instructions at Request a service quota increase for SageMaker resources.

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 Amazon support to request an increase for this limit.

Tested Models

The following table includes a list of the models that have been tested with SageMaker Training Compiler. For reference, the largest batch size that is able to fit into memory is also included alongside other training parameters. SageMaker Training Compiler can change the memory footprint of the model training process; as a result, a larger batch size can often be used during the training process, further decreasing total training time. In some cases, SageMaker Training Compiler intelligently promotes caching which leads to a decrease in the largest batch size that can fit on the GPU. You must retune your model hyperparameters and find an optimal batch size for your case. To save time, use the following reference tables to look up a batch size that can be a good starting point for your use case.

Note

The batch sizes are local batch size that fit into each individual GPU in the respective instance type. You should also adjust the learning rate when changing the batch size.

Natural language processing (NLP) models

The following models are tested for training jobs for all combinations of single-node and multi-node with single or multi GPU cores and Automatic Mixed Precision (AMP) as indicated.

Single-node/multi-node single-GPU/multi-GPU
Model Dataset Instance type Precision Sequence Length Batch size for native frameworks Batch size for SageMaker Training Compiler
albert-base-v2 wikitext-2-raw-v1 g4dn.16xlarge float16 128 80 192
albert-base-v2 wikitext-2-raw-v1 g5.4xlarge float16 128 128 332
albert-base-v2 wikitext-2-raw-v1 p3.2xlarge float16 128 80 224
bert-base-uncased wikitext-2-raw-v1 g5.4xlarge float16 128 160 288
camembert-base wikitext-2-raw-v1 g5.4xlarge float16 128 160 280
distilbert-base-uncased wikitext-2-raw-v1 g5.4xlarge float16 128 240 472
distilgpt2 wikitext-2-raw-v1 g4dn.16xlarge float16 128 77 128
distilgpt2 wikitext-2-raw-v1 g5.4xlarge float16 128 138 390
distilgpt2 wikitext-2-raw-v1 p3.2xlarge float16 128 96 256
distilroberta-base wikitext-2-raw-v1 g4dn.16xlarge float16 128 96 192
distilroberta-base wikitext-2-raw-v1 g5.4xlarge float16 128 171 380
distilroberta-base wikitext-2-raw-v1 p3.2xlarge float16 128 112 256
gpt2 wikitext-2-raw-v1 g4dn.16xlarge float16 128 52 152
gpt2 wikitext-2-raw-v1 g5.4xlarge float16 128 84 240
gpt2 wikitext-2-raw-v1 p3.2xlarge float16 128 58 164
microsoft/deberta-base wikitext-2-raw-v1 g4dn.16xlarge float16 128 48 128
microsoft/deberta-base wikitext-2-raw-v1 g5.4xlarge float16 128 84 207
microsoft/deberta-base wikitext-2-raw-v1 p3.2xlarge float16 128 53 133
roberta-base wikitext-2-raw-v1 g5.4xlarge float16 128 125 224
xlm-roberta-base wikitext-2-raw-v1 g4dn.16xlarge float16 128 16 31
xlm-roberta-base wikitext-2-raw-v1 p3.2xlarge float16 128 18 50
xlnet-base-cased wikitext-2-raw-v1 g5.4xlarge float16 128 128 240
bert-base-uncased wikitext-103-v1 g5.48xlarge float16 512 29 50
distilbert-base-uncased wikitext-103-v1 g5.48xlarge float16 512 45 64
gpt2 wikitext-103-v1 g5.48xlarge float16 512 18 45
roberta-base wikitext-103-v1 g5.48xlarge float16 512 23 44
gpt2 wikitext-103-v1 p4d.24xlarge float16 512 36 64

Computer Vision (CV) models

Tested using TensorFlow Model Garden with Automatic Mixed Precision (AMP) as indicated.

Single/multi-node single/multi-GPU
Model Dataset Instance type Precision Batch size for native frameworks Batch size for SageMaker Training Compiler
ResNet152 food101 g4dn.16xlarge float16 128 144
ResNet152 food101 g5.4xlarge float16 128 192
ResNet152 food101 p3.2xlarge float16 152 156
ViT food101 g4dn.16xlarge float16 512 512
ViT food101 g5.4xlarge float16 992 768
ViT food101 p3.2xlarge float16 848 768

Natural language processing (NLP) models

The following models are tested for training jobs for all combinations of single-node and multi-node with single or multi GPU cores and Automatic Mixed Precision (AMP) as indicated.

Single-node/multi-node single-GPU/multi-GPU
Model Dataset Instance type Precision Sequence Length Batch size for native frameworks Batch size for SageMaker Training Compiler
albert-base-v2 wikitext-2-raw-v1 ml.g5.2xlarge float16 128 128 248
bert-base-uncased wikitext-2-raw-v1 ml.g5.2xlarge float16 128 160 288
camembert-base wikitext-2-raw-v1 ml.g5.2xlarge float16 128 160 279
camembert-base wikitext-2-raw-v1 ml.p3.2xlarge float16 128 105 164
distilgpt2 wikitext-2-raw-v1 ml.g5.2xlarge float16 128 136 256
distilgpt2 wikitext-2-raw-v1 ml.p3.2xlarge float16 128 80 118
gpt2 wikitext-2-raw-v1 ml.g5.2xlarge float16 128 84 240
gpt2 wikitext-2-raw-v1 ml.p3.2xlarge float16 128 80 119
microsoft/deberta-base wikitext-2-raw-v1 ml.g5.2xlarge float16 128 93 197
microsoft/deberta-base wikitext-2-raw-v1 ml.p3.2xlarge float16 128 113 130
roberta-base wikitext-2-raw-v1 ml.g5.2xlarge float16 128 125 224
roberta-base wikitext-2-raw-v1 ml.p3.2xlarge float16 128 78 112
xlnet-base-cased wikitext-2-raw-v1 ml.g5.2xlarge float16 128 138 240
bert-base-uncased wikitext-103-v1 ml.p4d.24xlarge float16 512 52
distilbert-base-uncased wikitext-103-v1 ml.p4d.24xlarge float16 512 160
gpt2 wikitext-103-v1 ml.p4d.24xlarge float16 512 25
roberta-base wikitext-103-v1 ml.p4d.24xlarge float16 512 64

Computer Vision (CV) models

Tested using TensorFlow Model Garden with Automatic Mixed Precision (AMP) as indicated.

Single/multi-node single/multi-GPU
Model Dataset Instance type Precision Batch size for native frameworks Batch size for SageMaker Training Compiler
MaskRCNN-ResNet50-FPN COCO-2017 ml.g5.2xlarge float16 6 8
MaskRCNN-ResNet50-FPN COCO-2017 ml.p3.2xlarge float16 4 6
ResNet50 ImageNet ml.g5.2xlarge float16 192 256
ResNet50 ImageNet ml.p3.2xlarge float16 256 256
ResNet101 ImageNet ml.g5.2xlarge float16 128 256
ResNet101 ImageNet ml.p3.2xlarge float16 128 128
ResNet152 ImageNet ml.g5.2xlarge float16 128 224
ResNet152 ImageNet ml.p3.2xlarge float16 128 128
VisionTransformer ImageNet ml.g5.2xlarge float16 112 144
VisionTransformer ImageNet ml.p3.2xlarge float16 96 128

Natural Language Processing (NLP) models

Tested using Transformer models with Sequence_Len=128 and Automatic Mixed Precision (AMP) as indicated.

Single/multi-node single/multi-GPU
Model Dataset Instance type Precision Batch size for native frameworks Batch size for SageMaker Training Compiler
albert-base-v2 wikitext-2-raw-v1 ml.g5.2xlarge float16 160 197
albert-base-v2 wikitext-2-raw-v1 ml.p3.2xlarge float16 95 127
bert-base-uncased wikitext-2-raw-v1 ml.g5.2xlarge float16 160 128
bert-base-uncased wikitext-2-raw-v1 ml.p3.2xlarge float16 104 111
bert-large-uncased wikitext-2-raw-v1 ml.g5.2xlarge float16 65 48
bert-large-uncased wikitext-2-raw-v1 ml.p3.2xlarge float16 40 35
camembert-base wikitext-2-raw-v1 ml.g5.2xlarge float16 128 162
camembert-base wikitext-2-raw-v1 ml.p3.2xlarge float16 105 111
distilbert-base-uncased wikitext-2-raw-v1 ml.g5.2xlarge float16 256 264
distilbert-base-uncased wikitext-2-raw-v1 ml.p3.2xlarge float16 128 169
gpt2 wikitext-2-raw-v1 ml.g5.2xlarge float16 128 120
gpt2 wikitext-2-raw-v1 ml.p3.2xlarge float16 80 83
jplu/tf-xlm-roberta-base wikitext-2-raw-v1 ml.g5.2xlarge float16 32 32
jplu/tf-xlm-roberta-base wikitext-2-raw-v1 ml.p3.2xlarge float16 32 36
microsoft/mpnet-base wikitext-2-raw-v1 ml.g5.2xlarge float16 144 160
microsoft/mpnet-base wikitext-2-raw-v1 ml.p3.2xlarge float16 106 110
roberta-base wikitext-2-raw-v1 ml.g5.2xlarge float16 128 128
roberta-base wikitext-2-raw-v1 ml.p3.2xlarge float16 72 98
albert-base-v2 wikitext-2-raw-v1 ml.g5.48xlarge float16 128 192
albert-base-v2 wikitext-2-raw-v1 ml.p3.16xlarge float16 95 96
distilbert-base-uncased wikitext-2-raw-v1 ml.g5.48xlarge float16 256 256
distilbert-base-uncased wikitext-2-raw-v1 ml.p3.16xlarge float16 140 184
google/electra-small-discriminator wikitext-2-raw-v1 ml.g5.48xlarge float16 256 384
google/electra-small-discriminator wikitext-2-raw-v1 ml.p3.16xlarge float16 256 268
gpt2 wikitext-2-raw-v1 ml.g5.48xlarge float16 116 116
gpt2 wikitext-2-raw-v1 ml.p3.16xlarge float16 85 83
gpt2 wikitext-2-raw-v1 ml.p4d.24xlarge float16 94 110
microsoft/mpnet-base wikitext-2-raw-v1 ml.g5.48xlarge float16 187 164
microsoft/mpnet-base wikitext-2-raw-v1 ml.p3.16xlarge float16 106 111

Computer Vision (CV) models

Tested using TensorFlow Model Garden with Automatic Mixed Precision (AMP) as indicated.

Single-node single-GPU/multi-GPU
Model Dataset Instance type Precision Batch size for native frameworks Batch size for SageMaker Training Compiler
DetectionTransformer-ResNet50 COCO-2017 ml.g4dn.2xlarge float32 2 4
DetectionTransformer-ResNet50 COCO-2017 ml.g5.2xlarge float32 3 6
DetectionTransformer-ResNet50 COCO-2017 ml.p3.2xlarge float32 2 4
MaskRCNN-ResNet50-FPN COCO-2017 ml.g4dn.2xlarge float16 4 6
MaskRCNN-ResNet50-FPN COCO-2017 ml.g5.2xlarge float16 6 8
MaskRCNN-ResNet50-FPN COCO-2017 ml.g5.48xlarge float16 48 64
MaskRCNN-ResNet50-FPN COCO-2017 ml.p3.2xlarge float16 4 6
ResNet50 ImageNet ml.g4dn.2xlarge float16 224 256
ResNet50 ImageNet ml.g5.2xlarge float16 192 160
ResNet50 ImageNet ml.g5.48xlarge float16 2048 2048
ResNet50 ImageNet ml.p3.2xlarge float16 224 160
ResNet101 ImageNet ml.g4dn.2xlarge float16 160 128
ResNet101 ImageNet ml.g5.2xlarge float16 192 256
ResNet101 ImageNet ml.g5.48xlarge float16 2048 2048
ResNet101 ImageNet ml.p3.2xlarge float16 160 224
ResNet152 ImageNet ml.g4dn.2xlarge float16 128 128
ResNet152 ImageNet ml.g5.2xlarge float16 192 224
ResNet152 ImageNet ml.g5.48xlarge float16 1536 1792
ResNet152 ImageNet ml.p3.2xlarge float16 128 160
VisionTransformer ImageNet ml.g4dn.2xlarge float16 80 128
VisionTransformer ImageNet ml.g5.2xlarge float16 112 144
VisionTransformer ImageNet ml.g5.48xlarge float16 896 1152
VisionTransformer ImageNet ml.p3.2xlarge float16 80 128

Natural Language Processing (NLP) models

Tested using Transformer models with Sequence_Len=128 and Automatic Mixed Precision (AMP) as indicated.

Single-node single-GPU/multi-GPU
Model Dataset Instance type Precision Batch size for native frameworks Batch size for SageMaker Training Compiler
albert-base-v2 wikitext-2-raw-v1 g4dn.16xlarge float16 128 112
albert-base-v2 wikitext-2-raw-v1 p3.2xlarge float16 128 128
albert-base-v2 wikitext-2-raw-v1 p3.8xlarge float16 128 135
albert-base-v2 wikitext-2-raw-v1 g5.4xlarge float16 128 191
bert-base-uncased wikitext-2-raw-v1 g4dn.16xlarge float16 64 94
bert-base-uncased wikitext-2-raw-v1 p3.2xlarge float16 96 101
bert-base-uncased wikitext-2-raw-v1 p3.8xlarge float16 96 96
bert-base-uncased wikitext-2-raw-v1 g5.4xlarge float16 128 128
bert-large-uncased wikitext-2-raw-v1 g4dn.16xlarge float16 35 21
bert-large-uncased wikitext-2-raw-v1 p3.2xlarge float16 39 26
bert-large-uncased wikitext-2-raw-v1 g5.4xlarge float16 60 50
camembert-base wikitext-2-raw-v1 g4dn.16xlarge float16 96 90
camembert-base wikitext-2-raw-v1 p3.2xlarge float16 96 98
camembert-base wikitext-2-raw-v1 p3.8xlarge float16 96 96
camembert-base wikitext-2-raw-v1 g5.4xlarge float16 128 128
distilbert-base-uncased wikitext-2-raw-v1 g4dn.16xlarge float16 256 160
distilbert-base-uncased wikitext-2-raw-v1 p3.2xlarge float16 128 176
distilbert-base-uncased wikitext-2-raw-v1 p3.8xlarge float16 128 160
distilbert-base-uncased wikitext-2-raw-v1 g5.4xlarge float16 256 258
google_electra-small-discriminator wikitext-2-raw-v1 g4dn.16xlarge float16 256 216
google_electra-small-discriminator wikitext-2-raw-v1 p3.2xlarge float16 256 230
google_electra-small-discriminator wikitext-2-raw-v1 p3.8xlarge float16 256 224
google_electra-small-discriminator wikitext-2-raw-v1 g5.4xlarge float16 256 320
gpt2 wikitext-2-raw-v1 g4dn.16xlarge float16 80 64
gpt2 wikitext-2-raw-v1 p3.2xlarge float16 80 77
gpt2 wikitext-2-raw-v1 p3.8xlarge float16 80 72
gpt2 wikitext-2-raw-v1 g5.4xlarge float16 128 120
jplu_tf-xlm-roberta-base wikitext-2-raw-v1 g4dn.16xlarge float16 28 24
jplu_tf-xlm-roberta-base wikitext-2-raw-v1 p3.2xlarge float16 32 24
jplu_tf-xlm-roberta-base wikitext-2-raw-v1 p3.8xlarge float16 32 26
jplu_tf-xlm-roberta-base wikitext-2-raw-v1 g5.4xlarge float16 66 52
microsoft_mpnet-base wikitext-2-raw-v1 g4dn.16xlarge float16 96 92
microsoft_mpnet-base wikitext-2-raw-v1 p3.2xlarge float16 96 101
microsoft_mpnet-base wikitext-2-raw-v1 p3.8xlarge float16 96 101
microsoft_mpnet-base wikitext-2-raw-v1 g5.4xlarge float16 128 152
roberta-base wikitext-2-raw-v1 g4dn.16xlarge float16 64 72
roberta-base wikitext-2-raw-v1 p3.2xlarge float16 64 84
roberta-base wikitext-2-raw-v1 p3.8xlarge float16 64 86
roberta-base wikitext-2-raw-v1 g5.4xlarge float16 128 128

Tested using TensorFlow Model Garden with Automatic Mixed Precision (AMP).

Single-node single-GPU/multi-GPU
Model Dataset Instance type Batch size for native frameworks Batch size for SageMaker Training Compiler
ResNet50 ImageNet ml.g4dn.2xlarge 192 256*
ResNet101 ImageNet ml.g4dn.2xlarge 128 160
ml.g5.2xlarge 224 256*
ml.p3.16xlarge 1536 1792
ResNet152 ImageNet ml.g5.2xlarge 192 224
ml.p3.2xlarge 160 160
ml.p3.16xlarge 1024 1280
VisionTransformer ImageNet ml.g4dn.2xlarge 80 128*
ml.g5.2xlarge 112 128*
ml.p3.2xlarge 56 128*
ml.p3.16xlarge 640 1024*
DetectionTransformer-ResNet50 COCO-2017 ml.g4dn.2xlarge 2 2
ml.g5.2xlarge 3 6
ml.p3.2xlarge 2 4
ml.p3.16xlarge 8 32
MaskRCNN-ResNet50-FPN COCO-2017 ml.g4dn.2xlarge 4 4
ml.g5.2xlarge 6 8
ml.p3.2xlarge 4 6

* The batch sizes marked with the asterisk symbol (*) indicate the largest batch size tested by the SageMaker Training Compiler developer team. For the marked cells, the instance may be able to fit a larger batch size than what is indicated.

Tested with Sequence_Len=512 and Automatic Mixed Precision (AMP).

Single-node single-GPU
Model Dataset Instance type Instance count Batch size for native frameworks Batch size for Training Compiler
albert-base-v2 wikitext-2 ml.g4dn.2xlarge 1 14 28
ml.g5.2xlarge 1 18 40
ml.p3.2xlarge 1 14 32
bert-base-cased wikitext-2 ml.g4dn.2xlarge 1 12 24
ml.g5.2xlarge 1 28 44
ml.p3.2xlarge 1 16 20
camembert-base wikitext-2 ml.g4dn.2xlarge 1 16 28
ml.g5.2xlarge 1 24 40
ml.p3.2xlarge 1 16 24
distilbert-base-uncased wikitext-2 ml.g4dn.2xlarge 1 28 52
ml.g5.2xlarge 1 40 76
ml.p3.2xlarge 1 32 48
wikitext-103-v1 ml.p4d.24xlarge 4 82 160
distilgpt2 wikitext-2 ml.g4dn.2xlarge 1 6 18
ml.g5.2xlarge 1 12 28
ml.p3.2xlarge 1 6 16
distilroberta-base wikitext-2 ml.g4dn.2xlarge 1 20 40
ml.g5.2xlarge 1 28 56
ml.p3.2xlarge 1 24 40
EleutherAI/gpt-neo-125M wikitext-2 ml.g4dn.2xlarge 1 4 8
ml.g5.2xlarge 1 6 14
ml.p3.2xlarge 1 4 10
gpt2 wikitext-2 ml.g4dn.2xlarge 1 4 8
ml.g5.2xlarge 1 6 16
ml.p3.2xlarge 1 4 10
wikitext-103-v1 ml.p4d.24xlarge 4 13 25
roberta-base wikitext-2 ml.g4dn.2xlarge 1 12 20
ml.g5.2xlarge 1 24 36
ml.p3.2xlarge 1 12 20
wikitext-103-v1 ml.p4d.24xlarge 4 36 64
xlnet-base-cased wikitext-2 ml.g4dn.2xlarge 1 2 6
ml.g5.2xlarge 1 2 10
ml.p3.2xlarge 1 2 8
bert-base-uncased wikitext-103-v1 ml.p4d.24xlarge 2 32 64
4 32 64
8 32 64
16 32 64
roberta-large wikitext-103-v1 ml.p4d.24xlarge 4 16 24
microsoft/deberta-v3-base wikitext-103-v1 ml.p4d.24xlarge 16 9 23

Tested with Sequence_Len=512 and Automatic Mixed Precision (AMP).

Single-node single-GPU
Model Instance type Batch size for native frameworks Batch size for Training Compiler
albert-base-v2 ml.p3.2xlarge 14 28
ml.g4dn.2xlarge 14 24
bert-base-cased ml.p3.2xlarge 16 24
ml.g4dn.2xlarge 12 24
bert-base-uncased ml.p3.2xlarge 16 24
ml.g4dn.2xlarge 12 28
camembert-base ml.p3.2xlarge 12 24
ml.g4dn.2xlarge 12 28
distilbert-base-uncased ml.p3.2xlarge 28 48
ml.g4dn.2xlarge 24 52
distilgpt2 ml.p3.2xlarge 6 12
ml.g4dn.2xlarge 6 14
distilroberta-base ml.p3.2xlarge 20 40
ml.g4dn.2xlarge 12 40
EleutherAI/gpt-neo-125M ml.p3.2xlarge 2 10
ml.g4dn.2xlarge 2 8
facebook/bart-base ml.p3.2xlarge 2 6
ml.g4dn.2xlarge 2 6
gpt2 ml.p3.2xlarge 4 8
ml.g4dn.2xlarge 2 8
roberta-base ml.p3.2xlarge 12 20
ml.g4dn.2xlarge 12 20
xlnet-base-cased ml.p3.2xlarge 2 8
ml.g4dn.2xlarge 4 6

Tested with Sequence_Len=512 and Automatic Mixed Precision (AMP).

Single-node single-GPU
Model Instance type Batch size for native Batch size for Training Compiler
albert-base-v2 ml.p3.2xlarge 12 32
bert-base-cased ml.p3.2xlarge 14 24
bert-base-chinese ml.p3.2xlarge 16 24
bert-base-multilingual-cased ml.p3.2xlarge 4 16
bert-base-multilingual-uncased ml.p3.2xlarge 8 16
bert-base-uncased ml.p3.2xlarge 12 24
cl-tohoku/bert-base-japanese-whole-word-masking ml.p3.2xlarge 12 24
cl-tohoku/bert-base-japanese ml.p3.2xlarge 12 24
distilbert-base-uncased ml.p3.2xlarge 28 32
distilbert-base-uncased-finetuned-sst-2-english ml.p3.2xlarge 28 32
distilgpt2 ml.p3.2xlarge 16 32
facebook/bart-base ml.p3.2xlarge 4 8
gpt2 ml.p3.2xlarge 6 20
nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large ml.p3.2xlarge 20 32
roberta-base ml.p3.2xlarge 12 20
Single-node multi-GPU
Model Instance type Batch size for native Batch size for Training Compiler
bert-base-chinese ml.p3.8xlarge 16 26
bert-base-multilingual-cased ml.p3.8xlarge 6 16
bert-base-multilingual-uncased ml.p3.8xlarge 6 16
bert-base-uncased ml.p3.8xlarge 14 24
distilbert-base-uncased ml.p3.8xlarge 14 32
distilgpt2 ml.p3.8xlarge 6 32
facebook/bart-base ml.p3.8xlarge 8 16
gpt2 ml.p3.8xlarge 8 20
roberta-base ml.p3.8xlarge 12 20

Tested with Sequence_Len=128 and Automatic Mixed Precision (AMP).

Model Instance type Batch size for native frameworks Batch size for 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

Tested with Sequence_Len=128 and Automatic Mixed Precision (AMP).

Single-node single-GPU
Model Instance type Batch size for native Batch size for Training Compiler
albert-base-v2 ml.p3.2xlarge 128 128
bart-base ml.p3.2xlarge 12 64
bart-large ml.p3.2xlarge 4 28
bert-base-cased ml.p3.2xlarge 16 128
bert-base-chinese ml.p3.2xlarge 16 128
bert-base-multilingual-cased ml.p3.2xlarge 12 64
bert-base-multilingual-uncased ml.p3.2xlarge 16 96
bert-base-uncased ml.p3.2xlarge 16 96
bert-large-uncased ml.p3.2xlarge 4 24
cl-tohoku/bert-base-japanese ml.p3.2xlarge 16 128
cl-tohoku/bert-base-japanese-whole-word-masking ml.p3.2xlarge 16 128
distilbert-base-sst2 ml.p3.2xlarge 32 128
distilbert-base-uncased ml.p3.2xlarge 32 128
distilgpt2 ml.p3.2xlarge 32 128
gpt2 ml.p3.2xlarge 12 64
gpt2-large ml.p3.2xlarge 2 24
jplu/tf-xlm-roberta-base ml.p3.2xlarge 12 32
roberta-base ml.p3.2xlarge 4 64
roberta-large ml.p3.2xlarge 4 64
t5-base ml.p3.2xlarge 64 64
t5-small ml.p3.2xlarge 128 128