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49 lines
2.1 KiB
Markdown
49 lines
2.1 KiB
Markdown
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# Training performance report on NVIDIA A10
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[NVIDIA A10 Tensor Core GPU](https://www.nvidia.com/en-us/data-center/products/a10-gpu/)
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We can use A10 to train deep learning models by its FP16 and TF32 supports.
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## Test Server Spec
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| Key | Value |
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| ------------ | ------------------------------------------------ |
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| System | ServMax G408-X2 Rackmountable Server |
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| CPU | 2 x Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz |
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| Memory | 384GB, 12 x Samsung 32GB DDR4-2933 |
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| GPU | 8 x NVIDIA A10 22GB |
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| Cooling | 2x Customized GPU Kit for GPU support FAN-1909L2 |
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| Hard Drive | Intel SSD S4500 1.9TB/SATA/TLC/2.5" |
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| OS | Ubuntu 16.04.7 LTS |
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| Installation | CUDA 11.1, cuDNN 8.0.5 |
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| Installation | Python 3.7.10 |
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| Installation | PyTorch 1.9.0 (conda) |
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This server is donated by [AMAX](https://www.amaxchina.com/), many thanks!
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## Experiments on arcface_torch
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We report training speed in following table, please also note that:
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1. The training dataset is in mxnet record format and located on SSD hard drive.
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2. Embedding-size are all set to 512.
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3. We use a large dataset which contains about 618K identities to simulate real cases.
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| Dataset | Classes | Backbone | Batch-size | FP16 | TF32 | Samples/sec |
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| ----------- | ------- | ----------- | ---------- | ---- | ---- | ----------- |
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| WebFace600K | 618K | IResNet-50 | 1024 | × | × | ~2040 |
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| WebFace600K | 618K | IResNet-50 | 1024 | × | √ | ~2255 |
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| WebFace600K | 618K | IResNet-50 | 1024 | √ | × | ~3300 |
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| WebFace600K | 618K | IResNet-50 | 1024 | √ | √ | ~3360 |
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| WebFace600K | 618K | IResNet-50 | 2048 | √ | √ | ~3940 |
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| WebFace600K | 618K | IResNet-100 | 1024 | √ | √ | ~2210 |
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| WebFace600K | 618K | IResNet-180 | 1024 | √ | √ | ~1410 |
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