PKL导入模型时报错 'ascii' codec can't decode byte

pkl.load(f,encoding='iso-8859-1')

vscode 无法导入自定义包,无法import No module named

1. 在python安装路径的Lib\site-packages文件夹下新建一个任意命名的 .pth文件，如pythonwork.pth
2. 在文件中加上python项目的目录，如：H:\python\test
3. 重启vscode，问题得到解决

Nginx 使用UpStream进行SNI分流

        
stream {
                log_format stream '$remote_addr [$time_local] [$ssl_preread_server_name] [$upstream] $status $bytes_sent $bytes_received $session_time';
                access_log /opt/nginx/logs/stream.log stream;
                map $ssl_preread_server_name $upstream {
            default xxxxxxx;
        }
            upstream xxxx{
            server xxxxx;
        }
        upstream xxxxxxxxx{
            server xxxxx;
        }

        server {
          listen 443 reuseport;
                listen [::]:443 reuseport;
                proxy_pass $upstream;
                ssl_preread on;
                ssl_certificate      server.pem;
                ssl_certificate_key  server.key;
                ssl_protocols TLSv1 TLSv1.1 TLSv1.2;
                ssl_ciphers ECDHE-RSA-AES256-GCM-SHA384:ECDHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:ECDHE-RSA-AES256-SHA384:ECDHE-RSA-AES128-SHA256:ECDHE-RSA-AES256-SHA:ECDHE-RSA-AES128-SHA:DHE-RSA-AES256-SHA256:DHE-RSA-AES128-SHA256:DHE-RSA-AES256-SHA:DHE-RSA-AES128-SHA:ECDHE-RSA-DES-CBC3-SHA:EDH-RSA-DES-CBC3-SHA:AES256-GCM-SHA384:AES128-GCM-SHA256:AES256-SHA256:AES128-SHA256:AES256-SHA:AES128-SHA:DES-CBC3-SHA:HIGH:!aNULL:!eNULL:!EXPORT:!DES:!MD5:!PSK:!RC4;
                ssl_prefer_server_ciphers   on;
        }
}

MacBook M1 安装 pytorch_geometric

pip install --verbose torch-scatter torch-sparse torch-geometric

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

# Generate sample data
X, y_true = make_blobs(n_samples=300, centers=4,
                       cluster_std=0.60, random_state=0)

# Apply K-Means++
kmeans = KMeans(n_clusters=4, init='k-means++', max_iter=300, n_init=10, random_state=0)
pred_y = kmeans.fit_predict(X)

# Visualize the result
plt.scatter(X[:, 0], X[:, 1], c=pred_y)