From 760d6de043d7f654c5963391271f215dab461547 Mon Sep 17 00:00:00 2001 From: AnXiang Date: Wed, 6 Jan 2021 12:29:06 +0800 Subject: [PATCH] Update Docker for partial-fc. --- recognition/partial_fc/README.md | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/recognition/partial_fc/README.md b/recognition/partial_fc/README.md index 1a7fe8aae..25dd18fe3 100644 --- a/recognition/partial_fc/README.md +++ b/recognition/partial_fc/README.md @@ -75,11 +75,27 @@ Use [unpack_glint360k.py](./unpack_glint360k.py) to unpack. ## Docker For Partial-FC Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to -install the CUDA Toolkit and other independence on the host system, but the NVIDIA driver needs to be installed +install the CUDA Toolkit and other independence on the host system, but the NVIDIA driver needs to be installed. +Because the CUDA version used in the image is 10.1, +the graphics driver version on the physical machine must be greater than 418. ### 1. Docker Getting Started +You can use dockerhub or offline docker.tar to get the image of the Partial-fc. +1. dockerhub +```shell +docker pull insightface/partial_fc:v1 +``` + +2. offline images +coming soon! + +### 2. Getting Started +```shell +sudo docker run -it -v /train_tmp:/train_tmp --net=host --privileged --gpus 8 --shm-size=1g insightface/partial_fc:v1 /bin/bash +``` - +`/train_tmp` is where you put your training set (if you have enough RAM memory, +you can turn it into `tmpfs` first). ## Training Speed Benchmark ### 1. Train Glint360K Using MXNET