win10+cuda10.0+tensorflow-gpu==1.15安装教程

这篇教程详细介绍了在Win10环境下,如何安装CUDA10.0和cuDNN,以支持TensorFlow-GPU1.15的配置。主要内容包括CUDA的下载、安装以及环境变量设置,cuDNN的下载、解压和拷贝到CUDA安装目录,最后通过命令行测试CUDA和TensorFlow的安装是否成功。

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背景简介

作为刚入门深度学习的小白,准备兴高采烈的开始用GPU跑深度学习算法.已经安装好了tensorflow的CPU版本,都是想要加速,搭建tensorflow-gpu的版本。

我的环境是python==3.7, tensorflow==1.15,这两个的安装可以去参考其他教程,为什么在2021年还选择1.15的版本,因为我要用的代码还是tensorflow 版本1的,tensorflow 2.0的和原来的不一样,容易出现其他问题,为了早日上手深度学习,就不去弄2.0了。这里主要是讲解如何安装GPU加速CUDA和cuDnn。

tensorflow-gpu-1.15.0,tensorflow-gpu安装直接在命令行窗口输入:pip install tensorflow-gpu==1.15,注意要加上版本号,不然直接会安装最新版的。会和原来安装的版本不匹配。

显卡支持:

明确的一点是,我已经假定大家的显卡都能支持cuda了,基本现在的显卡都支持cuda了,除非你的机子非常老了,如果你仍然不放心,就去这个网址去查吧https://developer.nvidia.com/cuda-gpus;如果没找到自己的显卡型号,那就可能是不支持了。

cuda安装:

下载地址:https://developer.nvidia.com/cuda-toolkit-archive

这里要特别注意,不知道为什么一开始我一直下

自编译tensorflow1.python3.5,tensorflow1.12; 2.支持cuda10.0,cudnn7.3.1,TensorRT-5.0.2.6-cuda10.0-cudnn7.3; 3.支持mkl,无MPI; 软硬件硬件环境:Ubuntu16.04,GeForce GTX 1080 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: hp@dla:~/work/ts_compile/tensorflow$ bazel build --config=opt --config=mkl --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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