之前我已经在 Python 2.7 的 Ubuntu 16.04 上从源代码安装了 tensorflow，并且运行良好。
对于 Python 3.5(我创建了一个新的 anaconda 环境)，我安装了其中一个二进制版本，但我收到了警告消息。
编辑，使用下载的二进制轮，我收到的消息是...
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
...而且代码运行速度似乎比我使用 native 编译的 py27 运行要慢得多。
因此，我卸载了“旧的”tensorflow 二进制文件(用于 py35)，并按照 the Tensorflow Install from Sources page 中的说明进行了操作。 ，安装所有包，运行 ./configure，将其指向正确版本的 python(~/anaconda2/envs/py35/bin/python，及其相应的库路径)，选择我的 CUDA 选项，一路向下到最后，构建中没有错误...
在最后一步，它告诉我它“不是这个平台上受支持的轮子”——你是说我刚刚构建的平台？
$ ./configure
...
$ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
...
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
Thu Mar 9 23:17:35 CST 2017 : === Using tmpdir: /tmp/tmp.RrsUxPKxFg
~/tmp/tensorflow/bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles ~/tmp/tensorflow
~/tmp/tensorflow
/tmp/tmp.RrsUxPKxFg ~/tmp/tensorflow
Thu Mar 9 23:17:36 CST 2017 : === Building wheel
~/tmp/tensorflow
Thu Mar 9 23:17:45 CST 2017 : === Output wheel file is in: /tmp/tensorflow_pkg
$ sudo -H pip install /tmp/tensorflow_pkg/tensorflow-1.0.1-cp35-cp35m-linux_x86_64.whl
tensorflow-1.0.1-cp35-cp35m-linux_x86_64.whl is not a supported wheel on this platform.
这里有一些检查...
$ python --version
Python 3.5.3 :: Continuum Analytics, Inc.
$ which python
/home/myusername/anaconda2/envs/py35/bin/python
$ which pip
/home/myusername/anaconda2/envs/py35/bin/pip
$ pip install --upgrade pip
Requirement already up-to-date: pip in /home/myusername/anaconda2/envs/py35/lib/python3.5/site-packages
$
有人可以提供建议吗？
我看过各种关于问题的帖子。 “not a supported wheel”，人们使用下载的与正确版本的 python 不匹配的二进制轮子(例如 cp34 vs cp35)，以及关于在 Windows 上从源代码安装的帖子，但没有看到像我这样的帖子:involved installing从源代码开始，在 Linux 上正确“匹配”Python 版本。检查了 tensorflow 的 Github 问题，也没有看到这个。
最佳答案
这个错误是运行sudo -H pip install ...安装编译好的PIP包引起的。 Anaconda 使用虚拟环境，它会覆盖 pip 可执行文件的路径。但是，sudo 命令 does not preserve the user's $PATH environment variable ，所以 sudo -H pip install ... 将使用系统安装的 pip，在本例中对应于不同版本的 Python。
幸运的是，如果您使用的是 Anaconda，则无需使用 sudo 来运行 pip，因为整个环境都存储在您的用户拥有的目录中。
关于python - Tensorflow py3.5 从源代码 : "not a supported wheel"? 安装，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/42711014/