insightface/benchmarks/train/nvidia_v100.md

Training performance report on NVIDIA® V100

NVIDIA® V100 NVIDIA® V100 Tensor Core is the most advanced data center GPU ever built to accelerate AI, high performance computing (HPC), data science and graphics. It’s powered by NVIDIA Volta architecture, comes in 16 and 32GB configurations, and offers the performance of up to 32 CPUs in a single GPU.

Besides, we can also use NVIDIA® V100 to train deep learning models by its FP16 and FP32 supports.

Test Server Spec

Key	Value
CPU	2 x Intel(R) Xeon(R) Gold 6133 CPU @ 2.50GHz
Memory	384GB
GPU	8 x Tesla V100-SXM2-32GB
OS	Ubuntu 16.04 LTS
Installation	CUDA 10.2
Installation	Python 3.7.3
Installation	PyTorch 1.9.0 (pip)

Experiments on arcface_torch

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

1. The training dataset is SyntheticDataset.

2. Embedding-size are all set to 512.

1. 2 Million Identities

We use a large dataset which contains about 2 millions identities to simulate real cases.

Dataset	Classes	Backbone	Batch-size	FP16	Partial FC	Samples/sec
WebFace40M	2 Millions	IResNet-50	512	×	×	~1868
WebFace40M	2 Millions	IResNet-50	512	x	√	~2712
WebFace40M	2 Millions	IResNet-50	512	√	×	~2576
WebFace40M	2 Millions	IResNet-50	512	√	√	~4501
WebFace40M	2 Millions	IResNet-50	1024	×	×	~1960
WebFace40M	2 Millions	IResNet-50	1024	x	√	~2922
WebFace40M	2 Millions	IResNet-50	1024	√	×	~2810
WebFace40M	2 Millions	IResNet-50	1024	√	√	~5430
WebFace40M	2 Millions	IResNet-50	2048	√	√	~6095

2. 600K Identities

We use a large dataset which contains about 600k identities to simulate real cases.

Dataset	Classes	Backbone	Batch-size	FP16	Samples/sec
WebFace600K	618K	IResNet-50	512	×	~2430
WebFace600K	618K	IResNet-50	512	√	~3889
WebFace600K	618K	IResNet-50	1024	×	~2607
WebFace600K	618K	IResNet-50	1024	√	~4322
WebFace600K	618K	IResNet-50	2048	√	~4921