在本地计算机上进行设置，以便在进行ML测试时将这些依赖项安装在虚拟环境中
tensorflow
keras
h5py
requests
pillow
tensorflow-hub
我有一些脚本将通过，制作一个venv，安装需求并进行培训。如果我可以在任何地方使用这些脚本（包括在Google深度学习平台VM上）都很好，但是当我尝试在venv中安装tensorflow时，它不再使用GPU，因此我假设它不再使用系统安装张量流。
我还尝试了--system-site-packages标志，并说已经安装了tensorflow，但是随后它停止使用GPU。
假设我执行以下操作
$ virtualenv --sysem-site-packages venv/
$ source venv/bin/activate
$ pip install -r requirements.txt
Collecting tensorflow (from -r requirements.txt (line 1))
Using cached https://files.pythonhosted.org/packages/1a/c4/8cb95df0bf06089014259b25997c3921a87aa08e2cd981417d91ca92f7e9/tensorflow-1.10.1-cp27-cp27mu-manylinux1_x86_64.whl
Requirement already satisfied: keras in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 2)) (2.2.2)
Requirement already satisfied: h5py in /usr/lib/python2.7/dist-packages (from -r requirements.txt (line 3)) (2.7.0)
Requirement already satisfied: requests in /usr/lib/python2.7/dist-packages (from -r requirements.txt (line 4)) (2.12.4)
Requirement already satisfied: pillow in /usr/lib/python2.7/dist-packages (from -r requirements.txt (line 5)) (4.0.0)
Requirement already satisfied: tensorflow-hub in /home/john/.local/lib/python2.7/site-packages (from -r requirements.txt (line 6)) (0.1.1)
Collecting numpy<=1.14.5,>=1.13.3 (from tensorflow->-r requirements.txt (line 1))
Using cached https://files.pythonhosted.org/packages/6a/a9/c01a2d5f7b045f508c8cefef3b079fe8c413d05498ca0ae877cffa230564/numpy-1.14.5-cp27-cp27mu-manylinux1_x86_64.whl
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (1.14.1)
Requirement already satisfied: protobuf>=3.6.0 in /home/john/.local/lib/python2.7/site-packages (from tensorflow->-r requirements.txt (line 1)) (3.6.1)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (1.1.0)
Requirement already satisfied: backports.weakref>=1.0rc1 in /usr/local/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (1.0.post1)
Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (0.3.0)
Requirement already satisfied: wheel in ./venv/lib/python2.7/site-packages (from tensorflow->-r requirements.txt (line 1)) (0.31.1)
Requirement already satisfied: tensorboard<1.11.0,>=1.10.0 in /usr/local/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (1.10.0)
Requirement already satisfied: six>=1.10.0 in /home/john/.local/lib/python2.7/site-packages (from tensorflow->-r requirements.txt (line 1)) (1.11.0)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (0.2.0)
Requirement already satisfied: mock>=2.0.0 in /usr/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (2.0.0)
Requirement already satisfied: enum34>=1.1.6 in /usr/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (1.1.6)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python2.7/dist-packages (from tensorflow->-r requirements.txt (line 1)) (0.7.1)
Collecting setuptools<=39.1.0 (from tensorflow->-r requirements.txt (line 1))
Using cached https://files.pythonhosted.org/packages/8c/10/79282747f9169f21c053c562a0baa21815a8c7879be97abd930dbcf862e8/setuptools-39.1.0-py2.py3-none-any.whl
Requirement already satisfied: pyyaml in /usr/lib/python2.7/dist-packages (from keras->-r requirements.txt (line 2)) (3.12)
Requirement already satisfied: scipy>=0.14 in /usr/lib/python2.7/dist-packages (from keras->-r requirements.txt (line 2)) (0.18.1)
Requirement already satisfied: keras-applications==1.0.4 in /usr/local/lib/python2.7/dist-packages (from keras->-r requirements.txt (line 2)) (1.0.4)
Requirement already satisfied: keras-preprocessing==1.0.2 in /usr/local/lib/python2.7/dist-packages (from keras->-r requirements.txt (line 2)) (1.0.2)
Requirement already satisfied: futures>=2.2.0 in /usr/local/lib/python2.7/dist-packages (from grpcio>=1.8.6->tensorflow->-r requirements.txt (line 1)) (3.2.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/lib/python2.7/dist-packages (from tensorboard<1.11.0,>=1.10.0->tensorflow->-r requirements.txt (line 1)) (2.6.8)
Requirement already satisfied: werkzeug>=0.11.10 in /usr/lib/python2.7/dist-packages (from tensorboard<1.11.0,>=1.10.0->tensorflow->-r requirements.txt (line 1)) (0.11.15)
tensorflow-serving-api 1.10.0 has requirement protobuf==3.6.0, but you'll have protobuf 3.6.1 which is incompatible.
Installing collected packages: numpy, setuptools, tensorflow
Found existing installation: numpy 1.15.1
Not uninstalling numpy at /home/john/.local/lib/python2.7/site-packages, outside environment /home/john/retrain/venv
Can't uninstall 'numpy'. No files were found to uninstall.
Found existing installation: setuptools 40.2.0
Uninstalling setuptools-40.2.0:
Successfully uninstalled setuptools-40.2.0
Successfully installed numpy-1.14.5 setuptools-39.1.0 tensorflow-1.10.1
对于所有依赖项tensorflow，它表明它在系统上已经存在，因此没有安装它们，但是无论如何它会继续并安装tensorflow。为什么会这样？
答案 0 :(得分：2)
在您的需求文件中，列出了tensorflow软件包，这是仅CPU的软件包。为了获得GPU支持，请改为安装tensorflow-gpu。
答案 1 :(得分：1)
不幸的是，没有针对CPU和GPU优化的Tensorflow的“胖”二进制文件。但是，在两个实例上都可以使用tensorflow-gpu。
实际上，可以在没有GPU的情况下在实例上使用tensoflow-gpu二进制文件。为了使用它，您需要在实例上安装CUDA和CuDNN（即使实例没有Nvidia GPU）。 CUDA内部有一个模拟（存根）Nvidia驱动程序，该驱动程序将允许CUDA和CuDNN在CPU上运行，为了在Linux上使用它，您需要运行以下命令：
sudo ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/libcuda.so.1
sudo ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/libcuda.so
假设/usr/local/cuda是CUDA的安装路径（在不同平台上可能有所不同）。一旦完成，就可以在仅CPU实例上实际安装和使用tensorflow-gpu。
我知道这看起来像是黑客，甚至可能无法在某些平台上使用，但至少以某种方式可以在GPU和非GPU实例上使用相同的requirenemts.txt甚至相同的二进制文件。