python3.9.21,cuda 12.7安装tensorflow
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tensorflow for python3.7安装包
python3.7 进行pip install tensorflow经常失败,这里直接给出适合python3.7版本用的tensorflow,以便安装
TensorFlow的环境配置与安装教程详解(win10+GeForce GTX1060+CUDA 9.0+cuDNN7.3+tensorflow-gpu 1.12.0+python3.5.5)
记录一下安装win10+GeForce GTX1060+CUDA 9.0+cuDNN7.3+tensorflow-gpu 1.12.0+python3.5.5 之前已经安装过pycharm、Anaconda以及VS2013,因此,安装记录从此后开始 总体步骤大致如下: 1、确认自己电脑显卡型号是否支持CUDA(此处有坑) 此处有坑!不要管NVIDIA控制面板组件中显示的是CUDA9.2.148。 你下载的CUDA不一定需要匹配,尤其是CUDA9.2,最好使用CUDA9.0,我就在此坑摔的比较惨。 2、下载CUDA以及cuDNN,注意版本对应①查看版本匹配: https://www.tenso
anaconda下基于CPU/GPU配置python3.6+tensorflow1.12.0+keras【包含在线/离线方法】
在有网络和无网络的电脑上,运用anaconda配置基于CPU和GPU下的tensorflow1.12.0/tensorflow-gpu1.12.0,同时搭建keras。
win10+python3.7+tensorflow安装*
1. 首先安装python3.7 官网下载地址 本人下载的是python3.7.3, 64位,如下图: 然后点击安装,在安装的时候记得将路径添加到环境变量中,按部就班安装即可。 2. 安装tensorflow 首先切到cmd.exe窗口; 在cmd窗口输入以下命令: pip install –index-url https://pypi.douban.com/simple tensorflow==1.15 回车,等待安装成功即可。 3. Pycharm python版本切换 另外如果使用的IDE是Pycharm,且安装了两个及以上版本的python版本,可以在 Pycharm里面一次点击:F
Win10下安装并使用tensorflow-gpu1.8.0+python3.6全过程(显卡MX250+CUDA9.0+cudnn)
—–最近从github上找了一个代码跑,但是cpu训练的时间实在是太长,所以想用gpu训练一下,经过了一天的折腾终于可以用gpu进行训练了,嘿嘿~ 首先先看一下自己电脑的显卡信息: 可以看到我的显卡为MX250 然后进入NVIDIA控制面板->系统信息->组件 查看可以使用的cuda版本 这里我先下载了cuda10.1的版本,不过后来我发现tensorflow-gpu 1.8.0仅支持cuda9.0的版本,所以之后我又重装了一遍cuda9.0,中间还经历了删除cuda10.0,两个版本的安装都是一样的。 进入官网:https://developer.nvidia.com/cuda-too
tensorflow python2.7 py27 windows版
tensorflow python2.7 py27 windows版 或者使用conda forge测试均可
Win10下安装并使用tensorflow-gpu1.8.0+python3.6全过程分析(显卡MX250+CUDA9.0+cudnn)
主要介绍了Win10下安装并使用tensorflow-gpu1.8.0+python3.6全过程(显卡MX250+CUDA9.0+cudnn),本文给大家介绍的非常详细,具有一定的参考借鉴价值,需要的朋友可以参考下
Win10+Tensorflow1.7.0+Python3.6+Spyder配置
解决Spyder下无法import tensorflow的问题,可在win10下方便使用tensorflow。
tensorflow2.3—python3.8离线安装完整依赖库,
适用tensorflow2.3—python3.8,含安装顺序和指令指令,仅需复制粘贴轻松安装。默认提前安装了anaconda,若另需依赖资源可从网站:https://pypi.org/上下载
tensorflow 1.7.0 linux for python3.6 cuda9.1 cudnn7.0
tensorflow 1.7.0的whl,linux,python版本3.6,cuda的版本为9.1,cudnn版本为7.0。解决tensorflow直接用pip安装不支持cuda 9.1的问题。安装方法: pip install tensorflow-1.7.0-cp36-cp36m-linux_x86_64_cuda_9_1.whl
Jetson tx2,tf1.7,python3.5,cuda7.0.cudnn9.0
Jetson tx2 conpile tf1.7 (python3.5,cuda7.0.cudnn9.0)
python安装tensorflow
首先,我说一种最简单最理想的情况,然后说一种步骤齐全有局限性但是很靠谱的情况。这种简单的情况百分之五十是不会成功的,取决于你的显卡驱动,你的CUDA版本,你的python版本,你安装的年份等天时地利人和,才会成功: 我们下载python 3.6版本。因为目前tensorflow只支持python3.6及以下版本。 1. 假设我们已经安装了其他版本的python,我们需要将其进行卸载,卸载方法: 首先在命令行 python -version,找到我们当前的python版本,然后使用该版本的安装程序进行卸载。 2. 安装好以后,在命令行更新一下pip: python -m pip install
win7下安装CUDA9.0+Cudnn7.0.4+tensorflow任意版
win7下安装CUDA9.0+Cudnn7.0.4+tensorflow1.4.0 亲测有效
tensorflow1.12支持cuda10
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
tensorflow1.12.0+gpu(cuda 9.0 )
win10编译源码后的tensorflow1.12.0,具有头文件include,lib,dll。并且包含所用的cuda库。完全可以用,并且实验成功。如果下载后,不会用的可以联系我,全程教你!
tensorflow-1.12支持cuda10.0
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.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
Tensorflow及CUDA安装
介绍了在ubuntu16.04中安装Tensorflow及CUDA7.5的安装方法
Tensorflow安装
成功安装tensoflow的记录过程,希望能帮助到朋友们。如有疑问,欢迎交流。
tensorflow的安装教程与pycharm的配置
tensorflow的最新安装 并且pycharm的配置 anaconda的下载,以及各种安装包的下载地址分享
查看已安装tensorflow版本的方法示例
主要介绍了查看已安装tensorflow版本的方法示例,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学习学习吧
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