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lspci -vnn | grep VGA -A 12 查看
hylas@hylas-System-Product-Name:~$ lspci -vnn | grep VGA -A 12 01:00.0 VGA compatible controller [0300]: NVIDIA Corporation Device [10de:1c03] (rev a1) (prog-if 00 [VGA controller]) Subsystem: Device [1b4c:11d7] Flags: bus master, fast devsel, latency 0, IRQ 126 Memory at f6000000 (32-bit, non-prefetchable) [size=16M] Memory at e0000000 (64-bit, prefetchable) [size=256M] Memory at f0000000 (64-bit, prefetchable) [size=32M] I/O ports at e000 [size=128] [virtual] Expansion ROM at f7000000 [disabled] [size=512K] Capabilities: <access denied> Kernel driver in use: nvidia Kernel modules: nvidiafb, nouveau, nvidia_375_drm, nvidia_375 01:00.1 Audio device [0403]: NVIDIA Corporation Device [10de:10f1] (rev a1)
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安装
sudo add-apt-repository ppa:graphics-drivers/ppa sudo apt-get update sudo apt-get install nvidia-367 nvidia-367 (当前最新) sudo apt-get install mesa-common-dev
重启, 界面上看到 geforce gtx 1060 6gb
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以下参考:
http://keras-cn.readthedocs.io/en/latest/for_beginners/keras_linux/#3-cudacpu
安装 tensorflow-gpu tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl
下载地址 : https://github.com/tensorflow/tensorflow
安装
pip install tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl
安装 cuda 8.0 cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
下载地址 : https://developer.nvidia.com/cuda-downloads
安装 :
>>> sudo dpkg -i cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64.deb >>> sudo apt update >>> sudo apt install cuda
安装 cudnn 6.0 cudnn-8.0-linux-x64-v6.0-tgz
下载地址 : https://developer.nvidia.com/cudnn
>>> sudo cp include/cudnn.h /usr/local/cuda-8.0/include/ >>> sudo cp lib64/* /usr/local/cuda-8.0/lib64/
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最后成功运行 tensorflow - gpu
python temp.py name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate (GHz) 1.7335 pciBusID 0000:01:00.0 Total memory: 5.93GiB 2017-07-15 11:50:33.037446: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0) 60000/60000 [==============================] - 8s - loss: 0.3241 Epoch 2/10 60000/60000 [==============================] - 5s - loss: 0.1248 Epoch 3/10 60000/60000 [==============================] - 13s - loss: 0.0911