Windows 10 mmcv-full 1.3.6 安装记录

2022-08-05 15:36:48 浏览数 (1)

之前记录过mmcv-full 1.2.7 在Win 10 下的安装记录，当时的环境版本太低，现在需要升级，重新安装了mmcv-full 1.3.6，本文记录安装过程。

环境记录

  • Win 10 专业版
  • Python 3.8.5 （Anaconda3-2020.11）
  • 显卡型号 GTX 1660
  • 显卡驱动 461.92
  • CUDA 11.1
  • cuDNN 8.0.4
  • PyTorch 1.8.1 cu11.1
  • torchvision 0.9.1 cu11.1
  • VS 2019 Community
  • MMCV 1.3.6

环境配置

按照记录的环境逐步搭建，这是我安装mmcv时的环境，可以根据个人情况酌情调整，注意各个环境之间的版本依赖

Python 3.8.5
  • 安装 Anaconda3-2020.11 可以得到配套的 python 3.8.5

其他版本也可以，建议 3.7 以上 python

显卡驱动

可以参考之前的链接

  • 我们的目标是安装CUDA 11.1
  • 根据官网CUDA版本与驱动版本的关系表

​

Toolkit Driver Version

​

Minimum Required Driver Version*

​

CUDA Toolkit

Linux x86_64 Driver Version

Windows x86_64 Driver Version

Linux x86_64 Driver Version

Windows x86_64 Driver Version

CUDA 11.3.0 GA

=465.19.01

=465.89

= 450.80.02

= 456.38

CUDA 11.2.2 Update 2

=460.32.03

=461.33

= 450.80.02

= 456.38

CUDA 11.2.1 Update 1

=460.32.03

=461.09

= 450.80.02

= 456.38

CUDA 11.2.0 GA

=460.27.03

=460.82

= 450.80.02

= 456.38

CUDA 11.1.1 Update 1

=455.32

=456.81

= 450.80.02

= 456.38

CUDA 11.1 GA

=455.23

=456.38

= 450.80.02

= 456.38

CUDA 11.0.3 Update 1

= 450.51.06

= 451.82

= 450.51.06

= 451.82

CUDA 11.0.2 GA

= 450.51.05

= 451.48

= 450.51.06

= 451.48

CUDA 11.0.1 RC

= 450.36.06

= 451.22

= 450.51.06

= 451.22

CUDA 10.2.89

= 440.33

= 441.22

= 440.33

= 441.22

CUDA 10.1 (10.1.105 general release, and updates)

= 418.39

= 418.96

= 418.39

= 418.96

CUDA 10.0.130

= 410.48

= 411.31

= 410.48

= 411.31

CUDA 9.2 (9.2.148 Update 1)

= 396.37

= 398.26

= 396.37

= 398.26

CUDA 9.2 (9.2.88)

= 396.26

= 397.44

= 396.26

= 397.44

CUDA 9.1 (9.1.85)

= 390.46

= 391.29

= 390.46

= 391.29

CUDA 9.0 (9.0.76)

= 384.81

= 385.54

= 384.81

= 385.54

CUDA 8.0 (8.0.61 GA2)

= 375.26

= 376.51

= 375.26

= 376.51

CUDA 8.0 (8.0.44)

= 367.48

= 369.30

= 367.48

= 369.30

CUDA 7.5 (7.5.16)

= 352.31

= 353.66

= 352.31

= 353.66

CUDA 7.0 (7.0.28)

= 346.46

= 347.62

= 346.46

= 347.62

  • 我们需要在Win 10 安装 456.81 以上版本的显卡驱动
  • 在官网搜索自己的显卡，选择合适的版本下载安装即可
  • 我下载的是 461.92 版本的显卡驱动
代码语言：javascript复制
C:UsersAdministrator>nvidia-smi
Wed Jun 16 09:46:55 2021
 ----------------------------------------------------------------------------- 
| NVIDIA-SMI 461.92       Driver Version: 461.92       CUDA Version: 11.2     |
|------------------------------- ---------------------- ---------------------- 
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|=============================== ====================== ======================|
|   0  GeForce GTX 1660   WDDM  | 00000000:01:00.0  On |                  N/A |
| 27%   38C    P8     4W / 120W |    288MiB /  6144MiB |     12%      Default |
|                               |                      |                  N/A |
 ------------------------------- ---------------------- ---------------------- 

 ----------------------------------------------------------------------------- 
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1236    C G   Insufficient Permissions        N/A      |
 -----------------------------------------------------------------------------

​

CUDA 11.1

当前 （2021.06.16）最新 cuda 版本 11.3，pytorch 和 mmcv 还不支持，因此建议当前最高cuda版本11.1

  • 在官网下载即可

https://developer.nvidia.com/cuda-downloads

  • 具体流程可以参考之前的链接
  • 注意安装时勾掉 Visual Studio Intgration
  • 可能会报Nsight Visual Studio Edition 安装失败的错误
代码语言：txt复制
- [解决方案](https://www.zywvvd.com/notes/environment/cuda/cuda-install-error/cuda-install-error/)
代码语言：javascript复制
C:UsersAdministrator>nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Tue_Sep_15_19:12:04_Pacific_Daylight_Time_2020
Cuda compilation tools, release 11.1, V11.1.74
Build cuda_11.1.relgpu_drvr455TC455_06.29069683_0

​

cuDNN 8.0.4
  • 针对CUDA 11.1， cuDnn 可选择的版本其实有好几个
    • 8.0.4 支持 11.1
    • 8.1.1 支持11.0、11.1、11.2
    • 8.2.1 支持 11.X
  • 以上几个版本应该都可以用，稳妥起见我选择了 8.0.4，其他的没有亲测过
  • 具体步骤参考之前的链接
  • 官网链接（有时比较慢）：https://developer.nvidia.com/zh-cn/cudnn
  • 下载后直接粘贴到CUDA文件夹即可
PyTorch 1.8.1 cu11.1 / torchvision 0.9.1 cu11.1
  • 此处一定要安装 1.8 以上的torch
  • 因为 mmcv 在CUDA 11.1 下仅支持 1.8 版本的 torch

CUDA

torch 1.8

torch 1.7

torch 1.6

torch 1.5

torch 1.4

torch 1.3

11.1

install

​

​

​

​

​

11.0

​

install

​

​

​

​

10.2

install

install

install

install

​

​

10.1

install

install

install

install

install

install

9.2

​

install

install

install

install

install

cpu

install

install

install

install

install

install

  • 可以使用官方命令安装
代码语言：txt复制
- conda
代码语言：javascript复制
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge

​

代码语言：txt复制
- pip
代码语言：javascript复制
pip3 install torch==1.9.0 cu111 torchvision==0.10.0 cu111 torchaudio===0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

​

  • 也可以直接去下载目录选择自己需要的版本：

https://download.pytorch.org/whl/torch_stable.html

VS 2019 Community
  • 可以参考之前的链接
  • 当时下载的专业版，事实证明社区版就足够了
  • 安装C 桌面开发功能即可
  • 将 C:Program Files (x86)Microsoft Visual Studio2019CommunityVCToolsMSVC14.27.29110binHostx86x64 添加到环境变量 PATH, 这样 cl.exe 可以在控制台中所有路径中被调用。
代码语言：javascript复制
C:UsersAdministrator>cl
用于 x64 的 Microsoft (R) C/C   优化编译器 19.29.30037 版
版权所有(C) Microsoft Corporation。保留所有权利。

用法: cl [ 选项... ] 文件名... [ /link 链接选项... ]

​

网上有说仅安装VS C build tools 也可以，我尝试的时候没有成功

安装 MMCV 1.3.6

  • github 链接： https://github.com/open-mmlab/mmcv

可以重新构建 anaconda 环境

  • 下载代码，调整到指定tag，构建基础依赖环境
代码语言：javascript复制
git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
git checkout v1.3.6
pip3 install -r requirements.txt

​

配置环境变量

变量名称

值（需要根据个人情况调整）

CUDA_HOME

C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.1

CUDA_PATH

C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.1

MMCV_WITH_OPS

1

MAX_JOBS

4

path

记得添加 cl.exe 所在文件夹

TORCH_CUDA_ARCH_LIST

7.5

  • 其中TORCH_CUDA_ARCH_LIST一项表示的是显卡算力，可以在官网查询，我的查不到，可以用CUDA工具查询：

执行： C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.1extrasdemo_suitedeviceQuery.exe

代码语言：javascript复制
C:>"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.1extrasdemo_suitedeviceQuery.exe"
C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.1extrasdemo_suitedeviceQuery.exe Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1660"
  CUDA Driver Version / Runtime Version          11.2 / 11.1
  CUDA Capability Major/Minor version number:    7.5
  Total amount of global memory:                 6144 MBytes (6442450944 bytes)
  (22) Multiprocessors, ( 64) CUDA Cores/MP:     1408 CUDA Cores
  GPU Max Clock rate:                            1815 MHz (1.81 GHz)
  Memory Clock rate:                             4001 Mhz
  Memory Bus Width:                              192-bit
  L2 Cache Size:                                 1572864 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               zu bytes
  Total amount of shared memory per block:       zu bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  1024
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          zu bytes
  Texture alignment:                             zu bytes
  Concurrent copy and kernel execution:          Yes with 6 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.2, CUDA Runtime Version = 11.1, NumDevs = 1, Device0 = GeForce GTX 1660
Result = PASS

​

  • 将CUDA Capability Major/Minor version number: 7.5里的值填进TORCH_CUDA_ARCH_LIST中
编译安装
代码语言：javascript复制
# build
python setup.py build_ext # if success, cl will be launched to compile ops
# install
python setup.py develop

​

检查安装情况
  • 查看pip包列表
代码语言：javascript复制
C:>pip show mmcv-full
Name: mmcv-full
Version: 1.3.6
Summary: OpenMMLab Computer Vision Foundation
Home-page: https://github.com/open-mmlab/mmcv
Author: MMCV Authors
Author-email: openmmlab@gmail.com
License: UNKNOWN
Location: f:mmcv
Requires: addict, numpy, Pillow, pyyaml, yapf, regex
Required-by:

​

  • 查看编译文件，55个
  • 查看 _ext.cp38-win_amd64.pyd文件是否生成
  • 上述文件生成正常说明安装成功

正常情况下整个过程纵享丝滑，不需要改任何源码

未解决的问题

  • 其实上述步骤我重复了很多遍，一直提示 error: Microsoft Visual C 14.0 is required 尝试各种办法，装了啥都不行
  • 最终重装系统解决的问题
  • 最后分享我编译的_ext.cp38-win_amd64.pyd文件，可以安装mmcv-full之后直接放在安装目录中，不需要编译直接可用

前提是CUDA、python、torch等版本需要和我一致才能用

参考资料

  • https://blog.csdn.net/lebusini/article/details/117525764
  • http://allophane.com/index.php/2021/05/26/win10_build_mmcv-full_from_source/
  • https://github.com/open-mmlab/mmcv
深度学习 pytorch https 网络安全

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