最低要求的Cuda功能为3.5 [英] The minimum required Cuda capability is 3.5

问题描述

在g2.2xlarge EC2实例上安装TensorFlow及其依赖项之后，我尝试从入门页面运行MNIST示例：

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

但是我得到以下警告：

  I tensorflow / core / common_runtime / gpu / gpu_device.cc：611]忽略gpu设备
（设备： 0，名称：GRID K520，pci总线ID：0000：00：03.0），具有Cuda计算
能力3.0。最低要求的Cuda功能为3.5。 
  

这难吗？我有没有机会评论一下在TensorFlow的分支中签出？能够在AWS中训练模型真是太好了。

解决方案

当前只有正式的计算能力> = 3.5的GPU正式发布支持的。但是，GitHub用户@infojunkie提供了一个补丁，使TensorFlow可以与具有计算能力3.0的GPU一起使用。 / p>

官方修复程序正在开发中。同时，请查看关于 GitHub问题的讨论，以添加此支持。

After installing TensorFlow and its dependencies on a g2.2xlarge EC2 instance I tried to run an MNIST example from the getting started page:

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

But I get the following warning:

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.

Is this a hard requirement? Any chance I could comment that check out in a fork of TensorFlow? It would be super nice to be able to train models in AWS.

解决方案

Currently only GPUs with compute capability >= 3.5 are officially supported. However, GitHub user @infojunkie has offered a patch that makes it possible to use TensorFlow with a GPU with compute capability 3.0.

The official fix is in development. Meanwhile, check out the discussion on the GitHub issue for adding this support.

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