所需的最低Cuda能力为3.5

Question

在TensorFlow及其依赖项安装到g2.2xlarge EC2实例后,我尝试从入门页面运行一个MNIST示例:

python tensorflow/models/image/mnist/convolutional.py

但我收到以下警告:

I tensorflow/core/common_runtime/gpu/gpu_device.cc:611] Ignoring gpu device 
(device: 0, name: GRID K520, pci bus id: 0000:00:03.0) with Cuda compute 
capability 3.0. The minimum required Cuda capability is 3.5.

这是一项艰难的要求吗？我有机会评论一下TensorFlow的分支吗？能够在AWS中训练模型真是太棒了.

Answer 1

官方安装页面中有一个部分可以指导您启用Cuda 3,但您需要从源代码构建Tensorflow.

$ TF_UNOFFICIAL_SETTING=1 ./configure

# Same as the official settings above

WARNING: You are configuring unofficial settings in TensorFlow. Because some
external libraries are not backward compatible, these settings are largely
untested and unsupported.

Please specify a list of comma-separated Cuda compute capabilities you want to
build with. You can find the compute capability of your device at:
https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases
your build time and binary size. [Default is: "3.5,5.2"]: 3.0

Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Configuration finished

Answer 2

目前,只有正式支持计算能力> = 3.5的GPU.但是,GitHub用户@infojunkie提供了一个补丁,可以将TensorFlow与具有计算能力3.0的GPU一起使用.

正式修复正在开发中.同时,查看有关添加此支持的GitHub问题的讨论.

Answer 3

有一个简单的技巧。您甚至不必从源代码构建 TF。

在文件中tensorflow\python\_pywrap_tensorflow.pyd有两次出现 regex 3\.5.*5\.2。只需将两者都替换3.5为3.0.

在 Windows 10、Anaconda 4.2.13、Python 3.5.2、TensorFlow 0.12、CUDA 8、NVidia GTX 660m（CUDA cap. 3.0）上测试。