Linux & Windows TensorFlow 1.14 升级 2.2

我会带着你远行 2023-02-19 09:26 1阅读 0赞

> 准备尝试升级TensorFlow 1.14 到2.2，需要同时升级本地和服务器的环境，本文记录主要过程。

## 环境需求 ##

> 当前TensorFlow最高版本 2.2.+ ，需要CUDA 10.1，cudnn 7.6
> 
> 官网下载 ：https://developer.nvidia.com/
> 
> 显卡驱动需要满足CUDA版本要求

## Linux ##

*  下载 cuda\_10.1.243\_418.87.00\_linux.run 并安装（之前装过并运行过TensorFlow，相关库不过时的话可以不特意去装 cudnn）

> 过程中不需要安装驱动
> 
> 将 /usr/local/cuda 软链接 指向 cuda-10.1
> 
> 在 ~/.bashrc 中添加环境变量：

export PATH="$PATH:/usr/local/cuda/bin"
    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64/"
    export LIBRARY_PATH="$LIBRARY_PATH:/usr/local/cuda/lib64"

*  下载 TensorFlow ：tensorflow\_gpu-2.2.0-cp37-cp37m-manylinux2010\_x86\_64.whl

> 或使用pip安装

pip install tensorflow-gpu

> 会自动安装最新版 2.2.0 版本tf

## Windows ##

*  下载 cuda\_10.1.243\_426.00\_win10.exe 并安装（之前装过并运行过TensorFlow，相关库不过时的话可以不特意去装 cudnn）

> 我之前安装了CUDA10.0，不需要卸载原始版本，直接使用默认配置安装新版CUDA即可

*  下载 TensorFlow ：tensorflow\_gpu-2.2.0-cp37-cp37m-win\_amd64.whl

> 或使用pip安装

pip install tensorflow-gpu

> 会自动安装最新版 2.2.0 版本tf

## 测试 ##

*  python 环境下测试

>>> import tensorflow as tf
    >>> tf.__version__
    '2.2.0'
    >>> tf.test.is_gpu_available()
    WARNING:tensorflow:From <stdin>:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
    Instructions for updating:
    Use `tf.config.list_physical_devices('GPU')` instead.
    2020-06-18 04:15:11.406300: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
    2020-06-18 04:15:11.416107: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2498530000 Hz
    2020-06-18 04:15:11.417069: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55e39cc09290 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
    2020-06-18 04:15:11.417085: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
    2020-06-18 04:15:11.420284: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
    2020-06-18 04:15:11.593401: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55e39ccc83a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
    2020-06-18 04:15:11.593427: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
    2020-06-18 04:15:11.594463: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
    pciBusID: 0000:0a:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
    coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
    2020-06-18 04:15:11.594765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
    2020-06-18 04:15:11.596879: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
    2020-06-18 04:15:11.598737: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
    2020-06-18 04:15:11.599064: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
    2020-06-18 04:15:11.601174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
    2020-06-18 04:15:11.602319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
    2020-06-18 04:15:11.606633: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
    2020-06-18 04:15:11.608814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
    2020-06-18 04:15:11.608929: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
    2020-06-18 04:15:11.610584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
    2020-06-18 04:15:11.610608: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
    2020-06-18 04:15:11.610618: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
    2020-06-18 04:15:11.612706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/device:GPU:0 with 1958 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:0a:00.0, compute capability: 7.5)
    True

> 当最后一行输出True表示GPU可用，如果是False请查看错误信息。