本文通过AMD开发ROCm平台，Ubuntu 18.04系统中，TensorFlow也能使用AMD GPU实现GPU加速。现在把具体搭建流程呈上。

电脑硬件信息

CPU：AMD Ryzen 1700x

GPU：AMD Radeon RX580

内存：32G

硬盘：SSD 256GB + HDD 2TB

系统安装的是Ubuntu 18.04

本文针对的是Ubuntu 18.04，我选的是最小安装方式，当然也可以安装优麒麟Ubuntu Kylin 18.04，可以参考教程：https://ywnz.com/linuxaz/1443.html

安装AMD GPU驱动程序

1.下载最新的驱动程序，我使用的是18.20版本。

里面包含for RHEL 7.5/CentOS 7.5、RHEL 6.9/CentOS 6.9、Ubuntu 18.04、Ubuntu 16.04.4、SLED/SLES 12 SP3版本，选择Radeon Software for Linux version 18.20 for Ubuntu 18.04版本下载。

2.以下载到Downloads目录为例：

cd ~/Downloads

tar -Jxvf amdgpu-pro-18.20-606296.tar.xz

cd ~/Downloads/amdgpu-pro-18.20-606296

./amdgpu-pro-install --opencl=legacy

安装ROCm

1.增加ROCm的仓库

wget -qO - http://repo.radeon.com/rocm/apt/debian/rocm.gpg.key | sudo apt-key add -

sudo sh -c 'echo deb [arch=amd64] http://repo.radeon.com/rocm/apt/debian/ xenial main > /etc/apt/sources.list.d/rocm.list'

2.然后运行

sudo apt update

sudo apt install rocm-dkms

3.安装会报错，因为amdgpu这个AMD GPU的驱动程序在使用同一DKMS，我们强制安装这个包：

sudo dpkg -i --force-overwrite /var/cache/apt/archives/rock-dkms_1.8-192_all.deb

sudo apt install -f

4.重新启动

sudo reboot

至此安装完毕。

5.可以使用rocminfo测试一下是否安装成功。

/opt/rocm/bin/rocminfo

安装TensorFlow(ROCm port)

1.下载TensorFlow的ROCm专用轮子

2.运行以下命令安装相关软件包

sudo apt-get update && \

sudo apt-get install -y --allow-unauthenticated \

rocm-dkms rocm-dev rocm-libs \

rocm-device-libs \

hsa-ext-rocr-dev hsakmt-roct-dev hsa-rocr-dev \

rocm-opencl rocm-opencl-dev \

rocm-utils \

rocm-profiler cxlactivitylogger \

miopen-hip miopengemm

3.然后安装python相关软件包

sudo apt-get update && sudo apt-get install -y \

python3-numpy \

python3-dev \

python3-wheel \

python3-mock \

python3-future \

python3-pip \

python3-yaml \

python3-setuptools

4.安装之后安装我们的轮子(以Downloads目录为例)

sudo pip3 install ~/Downloads/tensorflow-1.8.0-cp35-cp35m-manylinux1_x86_64.whl

估计你安装不上。因为会报错，Ubuntu 18.04系统已经自动升级python为3.6了。没关系，把文件名里的35改成36，可以正常安装。不过在每次运行TensorFlow时会报错，但不影响使用。

5.测试一下吧

Python 3.6.5 (default, Apr  1 2018, 05:46:30)

[GCC 7.3.0] on linux

Type "help", "copyright", "credits" or "license" for more information.

>>> import tensorflow as tf

/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6

return f(*args, **kwds)

>>> hello = tf.constant('Hello, TensorFlow!')

>>> sess = tf.Session()

2018-07-23 8:59:14.289004: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

2018-07-23 8:59:14.296182: W tensorflow/stream_executor/rocm/rocm_driver.cc:404] creating context when one is currently active; existing: 0x7fa28910d130

2018-07-23 8:59:14.296312: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1451] Found device 0 with properties:

name: Ellesmere [Radeon RX 470/480]

AMDGPU ISA: gfx803

memoryClockRate (GHz) 1.266

pciBusID 0000:09:00.0

Total memory: 8.00GiB

Free memory: 7.75GiB

2018-07-23 8:59:14.296337: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1562] Adding visible gpu devices: 0

2018-07-23 8:59:14.296360: I tensorflow/core/common_runtime/gpu/gpu_device.cc:989] Device interconnect StreamExecutor with strength 1 edge matrix:

2018-07-23 8:59:14.296372: I tensorflow/core/common_runtime/gpu/gpu_device.cc:995]      0

2018-07-23 8:59:14.296384: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1008] 0:   N

2018-07-23 8:59:14.296429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1124] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7539 MB memory) -> physical GPU (device: 0, name: Ellesmere [Radeon RX 470/480], pci bus id: 0000:09:00.0)

>>> sess.run(hello)

b'Hello, TensorFlow!'

>>> b = tf.constant(32)

>>> sess.run(a+b)

42

>>> sess.close()

>>> exit()

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