tensorflow 使用多块GPU同时训练多个模型

最新推荐文章于 2024-05-19 08:28:05 发布

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最新推荐文章于 2024-05-19 08:28:05 发布

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分类专栏：深度学习 TensorFlow 文章标签： TensorFlow gpu session

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本文介绍如何在多个GPU上配置不同的TensorFlow会话。通过设置不同的CUDA_VISIBLE_DEVICES环境变量，可以在特定的GPU上运行不同的Python进程。这种方式能够有效地利用多GPU资源，并避免不同会话之间的内存冲突。

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转自：http://stackoverflow.com/questions/34775522/tensorflow-mutiple-sessions-with-mutiple-gpus

TensorFlow will attempt to use (an equal fraction of the memory of) all GPU devices that are visible to it. If you want to run different sessions on different GPUs, you should do the following.

Run each session in a different Python process.
Start each process with a different value for the CUDA_VISIBLE_DEVICES environment variable. For example, if your script is called my_script.py and you have 4 GPUs, you could run the following:
```
$ CUDA_VISIBLE_DEVICES=0 python my_script.py  # Uses GPU 0.
$ CUDA_VISIBLE_DEVICES=1 python my_script.py  # Uses GPU 1.
$ CUDA_VISIBLE_DEVICES=2,3 python my_script.py  # Uses GPUs 2 and 3.
```
Note the GPU devices in TensorFlow will still be numbered from zero (i.e. "/gpu:0" etc.), but they will correspond to the devices that you have made visible with CUDA_VISIBLE_DEVICES.