insightface/benchmarks/train/nvidia_rtx3080.md

Training performance report on NVIDIA RTX3080

GeForce RTX 3080 The GeForce RTX™ 3080 Ti and RTX 3080 graphics cards deliver the ultra performance that gamers crave, powered by Ampere—NVIDIA’s 2nd gen RTX architecture. They are built with enhanced RT Cores and Tensor Cores, new streaming multiprocessors, and superfast G6X memory for an amazing gaming experience.

Besides, we can also use GeForce RTX™ 3080 to train deep learning models by its FP16 and TF32 supports.

Test Server Spec

Key	Value
CPU	2 x Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz
Memory	384GB
GPU	8 x GeForce RTX™ 3080
OS	Ubuntu 18.04.4 LTS
Installation	CUDA 11.1,
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	×	×	Fail
WebFace40M	2 Millions	IResNet-50	512	x	√	~2190
WebFace40M	2 Millions	IResNet-50	512	√	×	Fail
WebFace40M	2 Millions	IResNet-50	512	√	√	~2620
WebFace40M	2 Millions	IResNet-50	1024	×	×	Fail
WebFace40M	2 Millions	IResNet-50	1024	x	√	Fail
WebFace40M	2 Millions	IResNet-50	1024	√	×	Fail
WebFace40M	2 Millions	IResNet-50	1024	√	√	~3800

2. 600K Identities

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

Dataset	Classes	Backbone	Batch-size	Partial FC	FP16	Samples/sec
WebFace600K	618K	IResNet-50	512	×	×	~2023
WebFace600K	618K	IResNet-50	512	×	√	~2392
WebFace600K	618K	IResNet-50	1024	×	×	Fail
WebFace600K	618K	IResNet-50	1024	×	√	Fail
WebFace600K	618K	IResNet-50	1024	√	√	~4010