win10+cuda9.2+TensorFlow安装

TensorFlow的cpu安装非常简单,只要输入命令pip install tensorflow即可。下面介绍的是安装gpu版本的TensorFlow。

  1. 安装cuda
    在安装cuda之前,首先得确定你的显卡驱动安装正确,而且要下对应的版本,打开NVIDIA控制面板,点击帮助,在点击系统信息,点击组件,我的版本是cuda9.2,网上很多教程都没有说到这一块,所以我在这研究了一天才解决,你得根据你电脑的显卡下载对应的版本。下载好之后直接点击安装默认路径就行,接着在下载cuddn v7.4
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cuddn下载后解压得到如下三个文件夹
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三个目录的文件分别为
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将相应的bin、include、lib分别放于自己的cuda下面的相应目录中,cuda默认目录为C:\ProgramFiles\NVIDIA GPU Computing Toolkit\CUDA\v7.5,因此将刚才解压的文件放在这个目录下面的bin、include、lib文件夹下。例如将cudnn64_7.dll复制到bin目录之下:
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至此,CUDA安装完毕。
2. 安装Anaconda2
我安装的版本是python2.7版本,如果你的电脑是python3.6的话也可以,这里有一篇文章,Windows10下python3和python2同时安装。安装比较简单就不在叙述。
3. 安装TensorFlow-gpu
打开命令提示符,使用命令pip install tensorflow-gpu 进行安装。这时你会发现 安装很慢,而且很容易出现安装出错,所以我这里换一种安装方法。下载tensorflow_gpu-1.8.0-cp27-cp27m-win_amd64.whl文件,这个安装包只能用python2.7版本安装,其他的版本网上有很多下载链接就不在提供地址了。
下载好之后将目录指定到tensorflow_gpu-1.8.0-cp27-cp27m-win_amd64.whl文件目录下,记住一定要是英文目录,不能出现中文字。输入命令pip install tensorflow_gpu-1.8.0-cp27-cp27m-win_amd64.whl
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4. 这时候你可能会出现如下错误
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这个错误是由于futures版本过低的原因,需要重新安装,但是你会发现想要卸载futures都会出错,这里教一个小技巧,找到你python的安装路径D:\mysoft\Anaconda2\Lib\site-packages(这是我安装的路径),在此路径下删除futures文件
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然后重新打开命令窗口输入命令pip install futures==3.1.1
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安装成功后再重复第3步重新安装TensorFlow。到此安装结束。
5. 测试TensorFlow是否安装成功
重新打开命令窗口输入python命令,再输入import tensorflow,如果没报错就证明安装成功了。
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自编译tensorflow: 1.python3.5,tensorflow1.12; 2.支持cuda10.0,cudnn7.3.1,TensorRT-5.0.2.6-cuda10.0-cudnn7.3; 3.无mkl支持; 软硬件硬件环境:Ubuntu16.04,GeForce GTX 1080 TI 配置信息: hp@dla:~/work/ts_compile/tensorflow$ ./configure WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown". You have bazel 0.19.1 installed. Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3 Found possible Python library paths: /usr/local/lib/python3.5/dist-packages /usr/lib/python3/dist-packages Please input the desired Python library path to use. Default is [/usr/local/lib/python3.5/dist-packages] Do you wish to build TensorFlow with XLA JIT support? [Y/n]: XLA JIT support will be enabled for TensorFlow. Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: No OpenCL SYCL support will be enabled for TensorFlow. Do you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow. Do you wish to build TensorFlow with CUDA support? [y/N]: y CUDA support will be enabled for TensorFlow. Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 10.0]: Please specify the location where CUDA 10.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-10.0 Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 7.3.1 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-10.0]: Do you wish to build TensorFlow with TensorRT support? [y/N]: y TensorRT support will be enabled for TensorFlow. Please specify the location where TensorRT is installed. [Default is /usr/lib/x86_64-linux-gnu]://home/hp/bin/TensorRT-5.0.2.6-cuda10.0-cudnn7.3/targets/x86_64-linux-gnu Please specify the locally installed NCCL version you want to use. [Default is to use https://github.com/nvidia/nccl]: Please specify a list of comma-separated Cuda compute capabilities you want to build with. You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1,6.1,6.1]: Do you want to use clang as CUDA compiler? [y/N]: nvcc will be used as CUDA compiler. Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: Do you wish to build TensorFlow with MPI support? [y/N]: No MPI support will be enabled for TensorFlow. Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]: Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: Not configuring the WORKSPACE for Android builds. Preconfigured Bazel build configs. You can use any of the below by adding "--config=" to your build command. See .bazelrc for more details. --config=mkl # Build with MKL support. --config=monolithic # Config for mostly static monolithic build. --config=gdr # Build with GDR support. --config=verbs # Build with libverbs support. --config=ngraph # Build with Intel nGraph support. --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects. Preconfigured Bazel build configs to DISABLE default on features: --config=noaws # Disable AWS S3 filesystem support. --config=nogcp # Disable GCP support. --config=nohdfs # Disable HDFS support. --config=noignite # Disable Apacha Ignite support. --config=nokafka # Disable Apache Kafka support. --config=nonccl # Disable NVIDIA NCCL support. Configuration finished 编译: bazel build --config=opt --verbose_failures //tensorflow/tools/pip_package:build_pip_package 卸载已有tensorflow: hp@dla:~/temp$ sudo pip3 uninstall tensorflow 安装自己编译的成果: hp@dla:~/temp$ sudo pip3 install tensorflow-1.12.0-cp35-cp35m-linux_x86_64.whl
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