本文是全系列中第4 / 4篇:TensorFlow 安装教程
- Ubuntu 安装 tensorflow-gpu 1.4 包含 CUDA 8.0 和cuDNN
- TensorFlow Windows 安装
- 在 Mac OS X 上安装 TensorFlow
- ubuntu 16.04 安装 tensorflow-gpu 包括 CUDA ,CUDNN,CONDA
ubuntu 16.04 安装 tensorflow-gpu 包括 CUDA ,CUDNN,CONDA
显卡驱动装好了,如图:
英文原文链接:
https://github.com/williamFalcon/tensorflow-gpu-install-ubuntu-16.04
英文内容:
Tensorflow GPU install on ubuntu 16.04
- update apt-get
sudo apt-get update
- Install apt-get deps
sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev
- install nvidia drivers
# The 16.04 installer works with 16.10. curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb apt-get update apt-get install cuda -y
2a. check nvidia driver install
nvidia-smi # you should see a list of gpus printed # if not, the previous steps failed.
- install cuda toolkit (MAKE SURE TO SELECT N TO INSTALL NVIDIA DRIVERS)
wget https://s3.amazonaws.com/personal-waf/cuda_8.0.61_375.26_linux.run sudo sh cuda_8.0.61_375.26_linux.run # press and hold s to skip agreement # Do you accept the previously read EULA? # accept # Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62? # ************************* VERY KEY **************************** # ******************** DON"T SAY Y ****************************** # n # Install the CUDA 8.0 Toolkit? # y # Enter Toolkit Location # press enter # Do you want to install a symbolic link at /usr/local/cuda? # y # Install the CUDA 8.0 Samples? # y # Enter CUDA Samples Location # press enter # now this prints: # Installing the CUDA Toolkit in /usr/local/cuda-8.0 … # Installing the CUDA Samples in /home/liping … # Copying samples to /home/liping/NVIDIA_CUDA-8.0_Samples now… # Finished copying samples.
- Install cudnn
wget https://s3.amazonaws.com/personal-waf/cudnn-8.0-linux-x64-v5.1.tgz sudo tar -xzvf cudnn-8.0-linux-x64-v5.1.tgz sudo cp cuda/include/cudnn.h /usr/local/cuda/include sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
- Add these lines to end of ~/.bashrc:
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" export CUDA_HOME=/usr/local/cuda
- Reload bashrc
source ~/.bashrc
- Install miniconda
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh bash Miniconda3-latest-Linux-x86_64.sh # press s to skip terms # Do you approve the license terms? [yes|no] # yes # Miniconda3 will now be installed into this location: # accept the location # Do you wish the installer to prepend the Miniconda3 install location # to PATH in your /home/ghost/.bashrc ? [yes|no] # yes
- Reload bashrc
source ~/.bashrc
- Create conda env to install tf
conda create -n tensorflow # press y a few times
- Activate env
source activate tensorflow
- Install tensorflow with GPU support for python 3.6
# pip install --ignore-installed --upgrade aTFUrl pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
- Test tf install
# start python shell python # run test script import tensorflow as tf hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() print(sess.run(hello))