在IBM power8上安装TensorFlow

我可以访问一台大型IBMPOWER8机器，并希望在其上安装TensorFlow。当然，我尝试了快速pip安装，但失败了：

sudo pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.6.0-cp27-none-linux_x86_64.whl
tensorflow-0.6.0-cp27-none-linux_x86_64.whl is not a supported wheel on this platform.
Storing debug log for failure in /home/pv/.pip/pip.log

不幸的是，pip.log包含的有用信息很少

/usr/bin/pip run on Sat Feb  6 17:29:34 2016
tensorflow-0.6.0-cp27-none-linux_x86_64.whl is not a supported wheel on this platform.
Exception information:
Traceback (most recent call last):
  File "/usr/lib/python2.7/dist-packages/pip/basecommand.py", line 122, in main
    status = self.run(options, args)
  File "/usr/lib/python2.7/dist-packages/pip/commands/install.py", line 283, in run
    InstallRequirement.from_line(name, None))
  File "/usr/lib/python2.7/dist-packages/pip/req.py", line 168, in from_line
    raise UnsupportedWheel("%s is not a supported wheel on this platform." % wheel.filename)
UnsupportedWheel: tensorflow-0.6.0-cp27-none-linux_x86_64.whl is not a supported wheel on this platform.

接下来我尝试的是从源代码构建TensorFlow。无效，我所有的尝试都以一些

无法执行二进制文件：Exec format error

消息结束，例如：

/usr/local/bin/bazel: line 86: /usr/local/lib/bazel/bin/bazel-real: cannot execute binary file: Exec format error

然后我试图从源代码编译Bazel，这也导致了类似的硬错误

me@machine:~/bazel-0.1.5$ ./compile.sh
INFO: You can skip this first step by providing a path to the bazel binary as second argument:
INFO:    ./compile.sh compile /path/to/bazel
Install bazel 0.2.0-ppc

tf@ubuntu16:~$ git clone https://github.com/ibmsoe/bazel
tf@ubuntu16:~/bazel$ git checkout v0.2.0-ppc
tf@ubuntu16:~/bazel$ ./compile.sh

me@machine：~/bazel-0.1.5$/compile.sh
信息：通过提供bazel二进制文件的路径作为第二个参数，可以跳过第一步：
信息：./compile.sh compile/path/to/bazel
安装bazel 0.2.0-ppc
安装tensorflow
在这里，您将遇到一个错误，类似这样的错误

错误：/home/tf/.cache/bazel/_bazel_tf/b2f766da603b0bed56d4dc1d0b178456a/external/farmhash_archive/BUILD:5:1:执行genrule@farmhash_archive/:configure失败：bash失败：执行命令/bin/bash-c时出错。。。（跳过其余1个参数）：com.google.devtools.build.lib.shell.badexitstatuseException:进程已退出，状态为1。
/home/tf/.cache/bazel/_-bazel\u tf/b2f766da603b0bed56d4c1d0b178456a/tensorflow/external/farmhash\u archive/farmhash-34c13ddfab0e35422f4c3979f36035a8c0520260/home/tf/.cache/bazel/_-bazel\u-tf/b2f766da603b0bed4d4c1d0b178456a/tensorflow
/tmp/tmp.XdCPQefJyZ/home/tf/.cache/bazel/_bazel_tf/b2f766da603b0bed56d4c1d0b178456a/tensorflow/external/farmhash_archive/farmhash-34c13dfab0e35422f4c3979f3603a8c50260/home/tf/.cache/bazel/_bazel/.b2f766da603b0bed56d4c1d0b178456a/tensorflow

您必须按如下所示编辑config.guess以插入ppc64le的节

    *:BSD/OS:*:*)
        echo ${UNAME_MACHINE}-unknown-bsdi${UNAME_RELEASE}
        exit ;;
+    ppc64le:Linux:*:*)
+      echo powerpc64le-unknown-linux-gnu
+      exit ;;
    *:FreeBSD:*:*)
        case ${UNAME_MACHINE} in

使用miniconda：

rm miniconda.sh
echo export IBM_POWERAI_LICENSE_ACCEPT=yes >> ~/.bashrc
source ~/.bashrc

安装miniconda：

rm miniconda.sh
echo export IBM_POWERAI_LICENSE_ACCEPT=yes >> ~/.bashrc
source ~/.bashrc

接受条件并允许将conda添加到路径

conda config --add default_channels https://repo.anaconda.com/pkgs/main
conda config --prepend channels https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-ai/conda/

这需要在终端上添加（基本）。添加正确的频道作为第一优先级

conda create -n ai python=3.7
conda activate ai
conda install --strict-channel-priority tensorflow-gpu

创建环境（最好不要在基础上安装软件包）

有关IBM Power 8和Anaconda上的miniconda的更多信息：&
这意味着您拥有的二进制文件与您尝试运行它的位置不兼容，请尝试从源代码构建bazel。但我做到了！我在上面添加了明确的错误消息，也许这会给你更多信息？对我来说，似乎我也需要编译protobuf，因为
第三方/protobuf/protoc-linux-x86_32.exe
可能不是正确的编译器。我使用了的说明，还尝试了源代码的tar.gz。那么我该怎么办呢？另外，我尝试的另一件事是用我的发行版附带的/usr/local/bin/protoc的符号链接替换protoc-linux-x86_32.exe（版本是
libprotoc 3.0.0
，根据
protoc--version
）。然而，这也导致了类似的错误：我在这里专门推出了一个关于Bazel的新主题：可能最好先解决这一部分。IBM已经为Tensorflow、torch和其他深度学习平台预构建了二进制文件。说明可在其网站上查阅。
rm miniconda.sh
echo export IBM_POWERAI_LICENSE_ACCEPT=yes >> ~/.bashrc
source ~/.bashrc

conda config --add default_channels https://repo.anaconda.com/pkgs/main
conda config --prepend channels https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-ai/conda/

conda create -n ai python=3.7
conda activate ai
conda install --strict-channel-priority tensorflow-gpu