超详细||深度学习环境搭建记录cuda+anaconda+pytorch+pycharm

本文用来记录windows系统上深度学习的环境搭建，目录如下

一、安装显卡驱动

首先为装有NVIDIA gpu的电脑安装显卡驱动，如果安装过了，或者想使用cpu的，可以跳过这一步。（其实这一步可以跳过，因为显卡驱动好想和深度学习环境没什么关系，保险起见还是安装上吧）

1. 去官网下载对应的显卡驱动：官方驱动 | NVIDIA

2. 完成下载，选择文件开始安装，直接解压在默认地址

1. 选择自定义安装选项，执行清洁安装（按情况选择）

1. 一直点下一步即可。

二、安装Visual Studio

可以跳过，但是很多深度学习环境需要用到，建议安装

1. 官网下载 Visual Studio Tools - 免费安装 Windows、Mac、Linux (microsoft.com)

1. 选择python开发和c++桌面开发，开始安装

三、cuda+cudnn安装

1. 打开cmd，输入nvidia-smi查看GPU的CUDA版本，可以看到CUDA版本12.1，意味着只能安装小于12.1的CUDA版本。

CUDA Toolkit Archive | NVIDIA Developer2. 去网站下载适合的CUDA版本：CUDA Toolkit Archive | NVIDIA Developer

要根据两个条件选择：

• 1. CUDA版本要小于上面的版本信息
• 1. 先去pytorch官网Start Locally | PyTorch，看一眼自己需要的pytorch版本对应的CUDA版本

例如：pytorch1.12.0只支持CUDA10.2，11.3，11.6

1. 然后去官网下载：CUDA Toolkit Archive | NVIDIA Developer，以11.6版本举例，选好系统参数，点击下载，

1. 下载完成，直接默认解压位置

1. 默认勾选即可，点下一步

1. 安装cudnn

去官网下载CUDA Deep Neural Network (cuDNN) | NVIDIA Developer，需要注册一个NVIDIA账号，然后选择CUDA对应版本的cudnn即可。

1. 下载cudnn后直接将其解开压缩包，然后需要将解压后的bin,include,lib文件夹复制粘贴到cuda安装时的默认路径文件夹下

1. 检查环境变量

1. 在cmd输入nvcc -V，显示CUDA版本即安装成功

安装多个CUDA环境：

如果要安装多个CUDA环境的话，和上述步骤基本相同，其中在安装时只需要选中cuda即可

如果要在各个CUDA间进行转换，只需要修改环境变量即可。

例如：把CUDA11.6改为CUDA11.8，只需要把CUDA_PATH改为v11.8，并把Path中v11.6的路径上移到v11.8之前，然后重启电脑即可

四、anaconda安装

1. 去官网下载安装包：Free Download | Anaconda

2. 下载完成，进行安装，建议安装在非系统盘，以后会创建比较多的环境的话，剩下的默认安装即可

五、pycharm安装

1. 官网下载安装包：PyCharm: the Python IDE for Professional Developers by JetBrains，可申请教育账号免费下载专业版，如果没有，下载社区版即可

2. 自行安装即可

六、pytorch安装，及在pycharm中选择conda环境

1.首先创建一个conda环境，打开Anaconda prompt，输入：conda create --name yourname python=3.10 -y

删除环境输入： conda remove -n env_name --all

1. 输入conda activate yourname 激活环境

1. 去官网找自己需要的pytorch版本：Start Locally | PyTorch 。选择电脑参数，并复制pip install命令，也可以去找之前的版本。

1. 将复制的pip install命令，粘贴在Anaconda prompt中，等待安装即可

4.打开anaconda prompt命令行测试代码

python 
import torch 
print(torch.__version__)
print(torch.cuda.is_available())

5.打开pycharm，选择添加新的解释器→添加本地解释器→conda环境→/yourname/python.exe (这里不同版本的pycharm可能有点不一样，只要能识别就行)

x

至此，环境搭建结束。。。。

安装了yolov8的，可以试着训练一下yolov8

from ultralytics import YOLO
 
if __name__ == '__main__':
    # Load a model
    model = YOLO(r'\ultralytics\detection\yolov8n\yolov8n.yaml')  # 不使用预训练权重训练
    # model = YOLO(r'yolov8p.yaml').load("yolov8n.pt")  # 使用预训练权重训练
    # Trainparameters ----------------------------------------------------------------------------------------------
    model.train(
        data=r'\ultralytics\detection\dataset\appledata.yaml',
        epochs= 30 , # (int) number of epochs to train for
        patience= 50 , # (int) epochs to wait for no observable improvement for early stopping of training
        batch= 8 , # (int) number of images per batch (-1 for AutoBatch)
        imgsz= 320 , # (int) size of input images as integer or w,h
        save= True , # (bool) save train checkpoints and predict results
        save_period= -1, # (int) Save checkpoint every x epochs (disabled if < 1)
        cache= False , # (bool) True/ram, disk or False. Use cache for data loading
        device= 0 , # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
        workers= 16 , # (int) number of worker threads for data loading (per RANK if DDP)
        project= 'result', # (str, optional) project name
        name= 'yolov8n' ,# (str, optional) experiment name, results saved to 'project/name' directory
        exist_ok= False , # (bool) whether to overwrite existing experiment
        pretrained= False , # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
        optimizer= 'SGD',  # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
        verbose= True ,# (bool) whether to print verbose output
        seed= 0 , # (int) random seed for reproducibility
        deterministic= True , # (bool) whether to enable deterministic mode
        single_cls= True , # (bool) train multi-class data as single-class
        rect= False  ,# (bool) rectangular training if mode='train' or rectangular validation if mode='val'
        cos_lr= False , # (bool) use cosine learning rate scheduler
        close_mosaic= 0,  # (int) disable mosaic augmentation for final epochs
        resume= False , # (bool) resume training from last checkpoint
        amp= False,  # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
        fraction= 1.0 , # (float) dataset fraction to train on (default is 1.0, all images in train set)
        profile= False,  # (bool) profile ONNX and TensorRT speeds during training for loggers
        # Segmentation
        overlap_mask= True , # (bool) masks should overlap during training (segment train only)
        mask_ratio= 4,  # (int) mask downsample ratio (segment train only)
        # Classification
        dropout= 0.0,  # (float) use dropout regularization (classify train only)
        # Hyperparameters ----------------------------------------------------------------------------------------------
        lr0=0.01,  # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
        lrf=0.01,  # (float) final learning rate (lr0 * lrf)
        momentum=0.937,  # (float) SGD momentum/Adam beta1
        weight_decay=0.0005,  # (float) optimizer weight decay 5e-4
        warmup_epochs=3.0,  # (float) warmup epochs (fractions ok)
        warmup_momentum=0.8,  # (float) warmup initial momentum
        warmup_bias_lr=0.1,  # (float) warmup initial bias lr
        box=7.5,  # (float) box loss gain
        cls=0.5,  # (float) cls loss gain (scale with pixels)
        dfl=1.5,  # (float) dfl loss gain
        pose=12.0,  # (float) pose loss gain
        kobj=1.0,  # (float) keypoint obj loss gain
        label_smoothing=0.0,  # (float) label smoothing (fraction)
        nbs=64,  # (int) nominal batch size
        hsv_h=0.015,  # (float) image HSV-Hue augmentation (fraction)
        hsv_s=0.7,  # (float) image HSV-Saturation augmentation (fraction)
        hsv_v=0.4,  # (float) image HSV-Value augmentation (fraction)
        degrees=0.0,  # (float) image rotation (+/- deg)
        translate=0.1,  # (float) image translation (+/- fraction)
        scale=0.5,  # (float) image scale (+/- gain)
        shear=0.0,  # (float) image shear (+/- deg)
        perspective=0.0,  # (float) image perspective (+/- fraction), range 0-0.001
        flipud=0.0,  # (float) image flip up-down (probability)
        fliplr=0.5,  # (float) image flip left-right (probability)
        mosaic=1.0,  # (float) image mosaic (probability)
        mixup=0.0,  # (float) image mixup (probability)
        copy_paste=0.0,  # (float) segment copy-paste (probability)
                )