insightface/benchmarks/train/nvidia_a30.md

Training performance report on NVIDIA A30

NVIDIA A30 Tensor Core GPU is the most versatile mainstream compute GPU for AI inference and mainstream enterprise workloads.

Besides, we can also use A30 to train deep learning models by its FP16 and TF32 supports.

Test Server Spec

Key	Value
System	ServMax G408-X2 Rackmountable Server
CPU	2 x Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz
Memory	384GB, 12 x Samsung 32GB DDR4-2933
GPU	8 x NVIDIA A30 24GB
Cooling	2x Customized GPU Kit for GPU support FAN-1909L2
Hard Drive	Intel SSD S4500 1.9TB/SATA/TLC/2.5"
OS	Ubuntu 16.04.7 LTS
Installation	CUDA 11.1, cuDNN 8.0.5
Installation	Python 3.7.10
Installation	PyTorch 1.9 (conda)

This server is donated by AMAX, many thanks!

Experiments on arcface_torch

We report training speed in following table, please also note that:

1. The training dataset is in mxnet record format and located on SSD hard drive.

2. Embedding-size are all set to 512.

3. We use a large dataset which contains about 618K identities to simulate real cases.

Dataset	Classes	Backbone	Batch-size	FP16	TF32	Samples/sec
WebFace600K	618K	IResNet-50	1024	×	×	~2230
WebFace600K	618K	IResNet-50	1024	×	√	~3200
WebFace600K	618K	IResNet-50	1024	√	×	~3940
WebFace600K	618K	IResNet-50	1024	√	√	~4350
WebFace600K	618K	IResNet-50	2048	√	√	~5100
WebFace600K	618K	IResNet-100	1024	√	√	~2810
WebFace600K	618K	IResNet-180	1024	√	√	~1800