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This article will explain in detail how docker uses GPU. The editor thinks it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.

Download tf-gpu

Select the tf version you want to download in docker hub (pay attention to the version with GPU and py3)

Https://hub.docker.com/r/tensorflow/tensorflow/

Such as:

Docker pull tensorflow/tensorflow:2.0.3-gpu-py3

If the above download times out, you can configure Tsinghua Source.

Or download it with the following command:

Docker pull docker.mirrors.ustc.edu.cn/tensorflow/tensorflow:2.0.3-gpu-py3

Build your own image based on the tf-gpu image of La

The following dockerfile

FROM docker.mirrors.ustc.edu.cn/tensorflow/tensorflow:2.0.3-gpu-py3RUN ln-sf / usr/share/zoneinfo/Asia/Shanghai / etc/localtime & & echo "Asia/Shanghai" > / etc/timezone & &\ pip3 install-I https://pypi.tuna.tsinghua.edu.cn/simple numpy pandas sklearn scipy matplotlib seaborn pyyaml h6py hdfsRUN pip install deepctr [gpu]-I http://pypi.douban.com/simple-- trusted-host pypi.douban.comRUN Pip install keras==2.3.1-I http://pypi.douban.com/simple-- trusted-host pypi.douban.com# cd / data/wangguisen/ad_ctr# docker build-t tf-deepctr:1.0-f. / dk/Dockerfile_base. Start the image to check if docker run is available for GPU-- gpus'"device=0"\-- rm-it-- name ad_ctr\-v / data/wangguisen/ad_ctr:/data/ad_ctr\ tf-deepctr:1.0

Enter nvidia-smi if the message of the no. 0 graphics card indicates success.

This is the end of the article on "how docker uses GPU". I hope the above content can be helpful to you so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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