Tensorflow 似乎没有看到我的 GPU [英] Tensorflow doesn't seem to see my gpu
问题描述
我在 cuda 7.5 和 8.0 上都尝试过 tensorflow,没有 cudnn(我的 GPU 很旧,cudnn 不支持它).
I've tried tensorflow on both cuda 7.5 and 8.0, w/o cudnn (my GPU is old, cudnn doesn't support it).
当我执行 device_lib.list_local_devices()
时,输出中没有 GPU.Theano 看到了我的 gpu,并且可以很好地使用它,并且/usr/share/cuda/samples 中的示例也可以正常工作.
When I execute device_lib.list_local_devices()
, there is no gpu in the output. Theano sees my gpu, and works fine with it, and examples in /usr/share/cuda/samples work fine as well.
我通过 pip install 安装了 tensorflow.我的 gpu 太旧了,tf 无法支持它吗?gtx 460
I installed tensorflow through pip install. Is my gpu too old for tf to support it? gtx 460
推荐答案
当我查看你的 GPU 时,我发现它只支持 CUDA Compute Capability 2.1.(可以通过 https://developer.nvidia.com/cuda-gpus 查看)不幸的是,TensorFlow 需要具有最低 CUDA 计算能力 3.0 的 GPU.https://www.tensorflow.org/get_started/os_setup#optional_install_cuda_gpus_on_linux
When I look up your GPU, I see that it only supports CUDA Compute Capability 2.1. (Can be checked through https://developer.nvidia.com/cuda-gpus) Unfortunately, TensorFlow needs a GPU with minimum CUDA Compute Capability 3.0. https://www.tensorflow.org/get_started/os_setup#optional_install_cuda_gpus_on_linux
您可能会看到一些来自 TensorFlow 的日志正在检查您的 GPU,但最终该库将避免使用不受支持的 GPU.
You might see some logs from TensorFlow checking your GPU, but ultimately the library will avoid using an unsupported GPU.
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