tensorflow-gpu版本相对应的CUDA版本

最新推荐文章于 2025-05-19 15:05:00 发布
原创 最新推荐文章于 2025-05-19 15:05:00 发布 · 6.8k 阅读 · 15 ·
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Linux

**tensorflow 版本 ** Python 版本 编译器 编译工具 cuDNN CUDA
tensorflow_gpu-1.13.1 2.7、3.3-3.6 GCC 4.8 Bazel 0.19.2 7.4 10.0
tensorflow_gpu-1.12.0 2.7、3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.11.0 2.7、3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.10.0 2.7、3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.9.0 2.7、3.3-3.6 GCC 4.8 Bazel 0.11.0 7<
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• tensorflow 与 cuda版本对照表• cudnn 清华源查看版本错误方法：nvidia-smi 可驱动的最高版本为11.5 下载的cuda的版本应低于11.5正确方法：nvcc --version检查它们是否安装成功。1 .首先 win+R 进入cmd，在命令行输入nvcc -V，如下图所示，则表示cuda已经安装成功。
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一、WIndows安装GPU版本tensorflow注意一下几个问题一般就不会出错 （1）确定自己要安装哪个版本的tensorflow-gpu； （1）根据自己要装的tensorflow-gpu版本确定要下载的CUDA版本； （2）根据要安装的CUDA版本确定要下载的Cudnn版本。 二、tensorflow-gpu版本与CUDA版本对应关系 不同版本的tensorflow-gpu与CUDA对应关...
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在Windows 11系统上，使用NVIDIA GeForce GTX 1650显卡，通过安装CUDA Toolkit 11.0和cuDNN 8.0.5，成功配置了TensorFlow GPU环境。
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https://tensorflow.google.cn/install/source_windows#gpu
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在这个案例中，提供的 cuDNN 版本是 8.1.1.33，而对应的 CUDA 版本是 11.2。 **安装与配置** 1. **下载 cuDNN**: 首先，你需要从 NVIDIA 官方网站下载 cuDNN 8.1.1.33，并确保它是针对 CUDA 11.2 版本的。压缩包中...
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确保你的系统安装的CUDA和cuDNN版本与TensorFlow-GPU版本相匹配。如果不匹配，更新到兼容的版本。 4. **TensorRT库缺失**： 错误信息中提到无法加载TensorRT的动态库，可能是因为缺少TensorRT库。确保已经安装了...
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1. **兼容性**：确保你的GPU型号和驱动程序与TensorFlow支持的CUDA版本相匹配。 2. **显存管理**：根据模型大小调整TensorFlow的显存分配策略，防止显存溢出。 3. **日志与调试**：启用TensorFlow的日志输出，可以...
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首先，查看你的 GPU 的兼容性，然后访问 NVIDIA 的 CUDA Toolkit Archive 网站 (https://developer.nvidia.com/cuda-toolkit-archive) 下载相应的 `cudatoolkit` 版本。例如，如果你的 GPU 支持 CUDA 11.5，但你选择...
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参考链接：https://blog.csdn.net/happywlg123/article/details/111749867
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深度学习-环境配置一、Linux1. CPU2. GPU二、macOS1. CPU2. GPU三、Windows1. CPU2. GPU四、下载网址 一、Linux Tested build configurations Linux 1. CPU 2. GPU 二、macOS 1. CPU 2. GPU 三、Windows 1. CPU 2. GPU 四、下载网址 CUDA 下载地址 点击打开链接 cudnn 下载地址 需要注册账号 https://developer.nvidia.com/cudnn tensorflow-cpu 下载地址 阿里镜像，下载速度快 http://mi
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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
tensorflow-1.12支持cuda10.0
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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.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
cudnn cuda tensorflow 版本对应
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linux下： windows下： 上面两张图是在这里找到的：https://tensorflow.google.cn/install/source（右上角language选English）
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CUDA 12.0 对应的 TensorFlow-GPU 版本是 TensorFlow 2.7 或更高版本。您可以使用 pip 命令来安装适配 CUDA 12.0 的 TensorFlow-GPU： ``` pip install tensorflow-gpu==2.7 ``` 请注意，安装 TensorFlow-GPU 之前...
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