Install PyTorch development environment on Windows 10 and verify YOLOv8

Recently, I built a Windows machine and planned to install a deep learning development environment on it, and build and deploy YOLOv8 for training and testing;

Environment:
OS: Windows 10
Graphics card: RTX 3090

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Install NVIDIA driver

Find the corresponding driver according to the graphics card model and install it

GeForce® Driver

verify

Enter in the terminal: nvidia-smicheck whether it is installed correctly

PS F:\workspace\notebook> nvidia-smi
Tue Aug 15 09:23:21 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 528.24       Driver Version: 528.24       CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| 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  NVIDIA GeForce ... WDDM  | 00000000:01:00.0  On |                  N/A |
| 30%   38C    P8    19W / 350W |    782MiB / 24576MiB |      4%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1620    C+G   C:\Windows\System32\dwm.exe     N/A      |
|    0   N/A  N/A      1908    C+G   ...ge\Application\msedge.exe    N/A      |

Install Visual Studio 2019 Community

Install VS2019Visual Studio Community 2019

verify

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Install Git, CMake, Anaconda

install git ,

tortoisegit can see file status

Install cmake , used for cross-platform compilation;

Install Anaconda , which integrates many pythondevelopment environments

verify

Download and install OpenCV

OpenCV download address

VC version number	VS corresponding version
vc6	VC6.0
vc7	VS2002
vc7.1	VS2003
vc8	VS2005
vc9	VS2008
vc10	VS2010
vc11	VS2012
vc12	VS2013
vc13	VS2014
vc14	VS2015
vc15	VS2017
vc16	VS2019

Since the above installation is VS 2019, then we install the VC16 version of OpenCV, so as not to compile it by ourselves;

After decompressing and installing, add buildthe directory under the directory x64\vc16\binto the environment variable.

Install CUDA and CUDNN

Some people here may not know what version of cuda needs to be installed. Because my GPU here is an N card 3090, it is still relatively good, so I can install a relatively high-end version of the software, but it cannot be too new. I will directly refer to which one of the latest version of the framework in PyTorch depends on?

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Ok, then install CUDA 11.8 and the corresponding CUDNN 8;

cuda11.8-exe_local-3GB

cudnn download corresponding version

Note: cudnn needs to register an account

After decompression, copy all the folders under the cudnn folder to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\the directory.

verify

(base) D:\>nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:41:10_Pacific_Daylight_Time_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0

Go to the installation directory C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\extras\demo_suite, run.\deviceQuery.exe

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Install PyTorch

PyTorch

conda install

conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

pip install

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

verify

(base) D:\>python
Python 3.11.4 | packaged by Anaconda, Inc. | (main, Jul  5 2023, 13:38:37) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.__version__)
2.0.1
>>> torch.cuda.is_available()
True
>>>

ultralytics/YOLOv8

Create a virtual environment

conda create --name yolov8 --clone base

Activate the virtual environment

conda activate yolov8

Install

pip install ultralytics

Code https://github.com/ultralytics/ultralytics

Weights https://github.com/ultralytics/assets/releases

verify

yolo predict model=yolov8n.pt imgsz=640 conf=0.25

(yolov8) F:\workspace\yolov8>yolo predict model=yolov8n.pt imgsz=640 conf=0.25
WARNING  'source' is missing. Using default 'source=D:\anaconda3\envs\yolov8\Lib\site-packages\ultralytics\assets'.
Ultralytics YOLOv8.0.154  Python-3.11.4 torch-2.0.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24576MiB)
YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients

image 1/2 D:\anaconda3\envs\yolov8\Lib\site-packages\ultralytics\assets\bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 160.2ms
image 2/2 D:\anaconda3\envs\yolov8\Lib\site-packages\ultralytics\assets\zidane.jpg: 384x640 2 persons, 1 tie, 154.0ms
Speed: 41.6ms preprocess, 157.1ms inference, 72.6ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs\detect\predict