CUDA cuDNN安装#
- 下载对应版本CUDA，cuDNN，安装CUDA
- 在cmd中输入
nvcc -V，返回CUDA版本则说明安装成功 - 安装时若遇到“You already have a newer version of the NVIDIA Frameview SDK installed”，先把电脑已经存在的FrameView SDK 卸载掉，把C:\Program Files\NVIDIA Corporation\FrameViewSDK文件夹删掉
- 将cuDNN解压，bin, include, lib文件夹复制到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vxx.x目录下并覆盖
TensorFlow安装#
CUDA 12.6 TensorFlow 2.6.0
conda create -n tf26 python=3.8.18
activate tf26
conda install cudatoolkit=11.2.0 cudnn=8.1.0.77
pip install tensorflow-gpu==2.6.0 keras==2.6.0 protobuf==3.20.0 -i https://pypi.tuna.tsinghua.edu.cn/simple/
conda install numpy=1.19.5 matplotlib=3.3.4 scipy scikit-learn pandasCUDA 12.6 TensorFlow 2.10.0
conda create -n tf210 python=3.8.18
activate tf210
conda install cudatoolkit=11.3 cudnn=8.2.1
pip install tensorflow-gpu==2.10.0 keras==2.10.0 -i https://pypi.tuna.tsinghua.edu.cn/simple/
conda install numpy=1.22 matplotlib=3.7.2 scipy scikit-learn pandas
pip install opencv-python==4.4.0.44 tqdm imutils PyYAML tensorboard seaborn chardet -i https://pypi.tuna.tsinghua.edu.cn/simple/检测TensorFlow能否使用GPU
import tensorflow as tf
tf.config.list_physical_devices('GPU')PyTorch安装#
CUDA 11.8 PyTorch2.3.1
conda create -n torch2 python=3.8.19
activate torch2
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=11.8 -c pytorch -c nvidia检测PyTorch能否使用GPU
import torch
print(torch.cuda.is_available())
print(torch.cuda.device_count())Tested build configurations on Windows#
GPU#
| Version | Python version | Compiler | Build tools | cuDNN | CUDA |
|---|---|---|---|---|---|
| tensorflow_gpu-2.10.0 | 3.7-3.10 | MSVC 2019 | Bazel 5.1.1 | 8.1 | 11.2 |
| tensorflow_gpu-2.9.0 | 3.7-3.10 | MSVC 2019 | Bazel 5.0.0 | 8.1 | 11.2 |
| tensorflow_gpu-2.8.0 | 3.7-3.10 | MSVC 2019 | Bazel 4.2.1 | 8.1 | 11.2 |
| tensorflow_gpu-2.7.0 | 3.7-3.9 | MSVC 2019 | Bazel 3.7.2 | 8.1 | 11.2 |
| tensorflow_gpu-2.6.0 | 3.6-3.9 | MSVC 2019 | Bazel 3.7.2 | 8.1 | 11.2 |
| tensorflow_gpu-2.5.0 | 3.6-3.9 | MSVC 2019 | Bazel 3.7.2 | 8.1 | 11.2 |
| tensorflow_gpu-2.4.0 | 3.6-3.8 | MSVC 2019 | Bazel 3.1.0 | 8.0 | 11.0 |
| tensorflow_gpu-2.3.0 | 3.5-3.8 | MSVC 2019 | Bazel 3.1.0 | 7.6 | 10.1 |
| tensorflow_gpu-2.2.0 | 3.5-3.8 | MSVC 2019 | Bazel 2.0.0 | 7.6 | 10.1 |
| tensorflow_gpu-2.1.0 | 3.5-3.7 | MSVC 2019 | Bazel 0.27.1-0.29.1 | 7.6 | 10.1 |
| tensorflow_gpu-2.0.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.26.1 | 7.4 | 10 |
| tensorflow_gpu-1.15.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.26.1 | 7.4 | 10 |
| tensorflow_gpu-1.14.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.24.1-0.25.2 | 7.4 | 10 |
| tensorflow_gpu-1.13.0 | 3.5-3.7 | MSVC 2015 update 3 | Bazel 0.19.0-0.21.0 | 7.4 | 10 |
| tensorflow_gpu-1.12.0 | 3.5-3.6 | MSVC 2015 update 3 | Bazel 0.15.0 | 7.2 | 9.0 |
| tensorflow_gpu-1.11.0 | 3.5-3.6 | MSVC 2015 update 3 | Bazel 0.15.0 | 7 | 9 |
| tensorflow_gpu-1.10.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.9.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.8.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.7.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.6.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.5.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.4.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 6 | 8 |
| tensorflow_gpu-1.3.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 6 | 8 |
| tensorflow_gpu-1.2.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |
| tensorflow_gpu-1.1.0 | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |
| tensorflow_gpu-1.0.0 | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |