安装

CPU 版

最简单的方法使用 pip 来安装

# Python 2.7
pip install --upgrade tensorflow
# Python 3.x
pip3 install --upgrade tensorflow

docker 使用镜像 gcr.io/tensorflow/tensorflow 启动 CPU 版 Tensorflow：

docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow

验证安装

$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!
>>>

GPU 版

注意：从 1.2 版本开始，Mac OSX 不再支持 GPU 版本（CPU 版仍继续支持）。

pip

最简单的方法是使用 pip 安装：

# Python 2.7
pip install --upgrade tensorflow-gpu
# Python 3.x
pip3 install --upgrade tensorflow-gpu

Docker

首先安装 nvidia-docker：

# Install nvidia-docker and nvidia-docker-plugin
wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
# Test nvidia-smi
nvidia-docker run --rm nvidia/cuda nvidia-smi

然后可以使用 gcr.io/tensorflow/tensorflow:latest-gpu 镜像启动 GPU 版 Tensorflow：

nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu

CUDA 和 cuDNN

安装 CUDA：

# Check for CUDA and try to install.
if ! dpkg-query -W cuda; then
  # 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
  rm -f ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
  apt-get update
  apt-get install libcupti-dev cuda -y
fi

安装 cuDNN：

首先到网站 https://developer.nvidia.com/cudnn 注册，并下载 cuDNN v5.1，然后运行命令安装

wget https://www.dropbox.com/s/xdak8t60lzk11zb/cudnn-8.0-linux-x64-v5.1.tgz?dl=1 -O cudnn-8.0-linux-x64-v5.1.tgz
tar zxvf cudnn-8.0-linux-x64-v5.1.tgz
ln -s /usr/local/cuda-8.0 /usr/local/cuda
sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

安装完成后，可以运行 nvidia-smi 查看 GPU 设备的状态

$ nvidia-smi
Fri Jun 16 19:33:35 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.66                 Driver Version: 375.66                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | 0000:00:04.0     Off |                    0 |
| N/A   74C    P0    80W / 149W |      0MiB / 11439MiB |    100%      Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

验证安装

$ python
>>> from tensorflow.python.client import device_lib
>>> print device_lib.list_local_devices()
...
[name: "/cpu:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 9675741273569321173
, name: "/gpu:0"
device_type: "GPU"
memory_limit: 11332668621
locality {
  bus_id: 1
}
incarnation: 7807115828340118187
physical_device_desc: "device: 0, name: Tesla K80, pci bus id: 0000:00:04.0"
]
>>>