组装深度学习机器 +RTX2070 + tensorflow1.13 +cuda10

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最近由于自己开始搞深度学习的东西,搞这个肯定是要烧硬件的,单位虽然有服务器,但是机器都是内部网络,而且说道很多,对于自己学习来说,实在是很不方便,于是自己准备入手一台机器,在网上看了看品牌机,惊讶的发现做为一个上班族来说实在是买不起,中等配置还要10000+,于是衡量了一下,准备自己组装一台机器。首先确定了自己的预算:10000-,在网上看了一些关于深度学习机器配置的帖子,然后根据最近英伟达显卡的发展确定了自己的机器配置:
主板:华硕B360 plus
CPU:intell i7-8700
内存: 金士顿16G内存条,DDR4 2666
GPU显卡:七彩虹 RTX2070 8G
固态硬盘:三星250g
机械硬盘:希捷2T机械硬盘
电源:EVGA(注意额定功率,单显卡650左右)
机箱和风扇我都是选的相对便宜的,先用着,后续可能会把风扇换好的,毕竟散热也很重要。大概算下来不到9千。
配件到了就开始组装,在组装过程中唯一出问题的就是开机的正负极搞错了,找到客服要了一张接线的图片,就搞定了。放几张图片看看:
在这里插入图片描述
装完机器,就开始做系统,用U盘启动盘,这里出现的问题是,在装系统时,选择iso系统镜像时,总是跳出一个Z盘,而且总是默认安装的是Z盘的镜像文件,后来百度了一下,原来是iso文件没有解压,启动盘会将iso文件自动解压并创建一个Z盘,正常装机就行了。一切搞定,开始安装机器的环境。
tensorflow+cuda+cudnn
这里要注意的是RTX2070是nvidia出的新一代20系列显卡,都是图灵架构。要配合cuda10的版本,而且tensorflow 也要选择1.13版本。于是装了cuda最新的10.1,安装完毕,在import tensorflo时,报importError:DLL load failed:找不到指定的模块“这个错误,在另一篇文章中我已经说过,这个错误基本都是版本不对应的问题。于是想着是不是cuda版本太高了,卸载cuda10.1,重新安装cuda10.0,问题解决。
这里在提醒大家一下,cuda10.0对应tensorflow1.13,不是cuda10.1,切记。
然后就开始跑代码了,哈哈哈哈哈哈哈。
在这里插入图片描述

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.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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