Python





```python
import copy
import logging
import sys
class ColoredFormatter(logging.Formatter):
    COLORS = {
        "DEBUG": "\033[0;36m",  # CYAN
        "INFO": "\033[0;32m",  # GREEN
        "WARNING": "\033[0;33m",  # YELLOW
        "ERROR": "\033[0;31m",  # RED
        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
        "RESET": "\033[0m",  # RESET COLOR
    }
    def format(self, record):
        colored_record = copy.copy(record)
        levelname = colored_record.levelname
        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
        return super().format(colored_record)
# Create a new logger
logger = logging.getLogger("user_server")
logger.propagate = False
# Add handler if we don't have one.
if not logger.handlers:
    handler = logging.StreamHandler(sys.stdout)
    handler.setFormatter(
        ColoredFormatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
    )
    logger.addHandler(handler)
# Configure logger
loglevel = getattr(logging, "INFO", "info")
logger.setLevel(loglevel)
logger.info("sdfasdasdas")
logger.warning("sdfasdasdas")
logger.error("sdfasdasdas")
```


![在这里插入图片描述](/static/img/images/03790.png)

【python】【logging】python如何使用logging模块，一个完美的logging模块

pip install pillow





示例代码：
```python
from PIL import Image, ImageSequence
# 图片文件名列表
image_files = ['car.png', 'detected_map.png', 'base64_image_out.png']
# 打开图片
images = [Image.open(filename) for filename in image_files]
# 设置输出 GIF 文件名
output_gif = 'output.gif'
# 将图片保存为 GIF
images[0].save(
    output_gif,
    save_all=True,
    append_images=images[1:],
    duration=2000,  # 设置每张图片的显示时间（毫秒）
    loop=0,  # 设置循环次数，0 表示不循环
)
print(f'GIF 文件已创建: {output_gif}')
```

将某个路径的所有图片按名称排序后，转为gif文件：

```clike
import os
from PIL import Image, ImageSequence
# 目标路径
target_path = './images'
# 获取目录下所有文件
all_files = os.listdir(target_path)
# 筛选出图片文件
image_files = [os.path.join(target_path, filename) for filename in all_files if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.gif'))]
# 按照文件名排序
sorted_image_files = sorted(image_files)
# 打开图片并调整为相同的模式
images = [Image.open(filename).convert('RGBA') for filename in sorted_image_files]
# 设置输出 GIF 文件名
output_gif = 'output.gif'
# 将图片保存为 GIF
images[0].save(
    output_gif,
    save_all=True,
    append_images=images[1:],
    duration=2000,  # 设置每张图片的显示时间（毫秒）
    loop=0,  # 设置循环次数，0 表示不循环
)
print(f'GIF 文件已创建: {output_gif}')
```

![在这里插入图片描述](/static/img/images/03791.gif)

【python】Python生成GIF动图，多张图片转动态图，pillow

原则：





1. 输入输出都基于BaseModel

2. 依靠JSONResponse制定返回错误的json信息

3. 依靠装饰器中@app.post制定responses字典从而让docs文档更丰富


```python
import uvicorn
from pydantic import BaseModel, Field
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from transformers import BlipProcessor, BlipForConditionalGeneration
from fastapi.responses import JSONResponse
from PIL import Image
import io
import base64
class UpscalerRequest(BaseModel):
    base64_image: str = Field(
        ...,
        title="Base64 Encoded Image",
        description="The base64-encoded image that you want to upscale."
    )
    outscale: float = Field(
        ...,
        ge=1.0,  # 大于等于1.0
        le=5.0,  # 小于等于5.0
        title="Upscale Factor",
        description="The scaling factor for image upscaling. Should be between 1.0 and 5.0."
    )
class UpscalerResponse(BaseModel):
    base64_image_out: str = Field(
        ...,
        title="Base64 Encoded Upscaled Image",
        description="The base64-encoded image after upscaling."
    )
class CustomErrorResponse:
    def __init__(self, description: str, error_code: int, detail: str):
        self.description = description
        self.error_code = error_code
        self.detail = detail
    def to_response_dict(self):
        return {
            "description": self.description,
            "content": {
                "application/json": {
                    "example": {"error_code": self.error_code, "detail": self.detail}
                }
            },
        }
    def __call__(self, extra_detail=None):
        if extra_detail is not None:
            self.detail = f"detail:{self.detail}, extra_detail:{extra_detail}"
        return JSONResponse(content={"error_code": self.error_code, "detail": self.detail}, status_code=self.error_code)
image_upscaler_CustomErrorResponse = CustomErrorResponse("超分执行错误", 501, "upscale error")
@app.post("/image_upscaler", response_model=UpscalerResponse, summary="Image Upscaler",
          responses={image_upscaler_CustomErrorResponse.error_code: image_upscaler_CustomErrorResponse.to_response_dict()})
def image_upscaler(request: UpscalerRequest):
    """
    Image Upscaler.
    Parameters:
    - `base64_image`: The base64-encoded image.
    - `outscale`: The scaling factor for image upscaling (between 1.0 and 5.0).
    Returns:
    - `output`: The base64-encoded upscaled image.
    Example:
    ```python

import requests

import base64

from PIL import Image

from io import BytesIO

# 1. 读取图像文件并转换为base64字符串

image_path = "car.png"

with open(image_path, "rb") as image_file:

    base64_image = base64.b64encode(image_file.read()).decode("utf-8")

# 2. 构造请求数据

outpainting_request = {

    "base64_image": base64_image,

    "outscale": 3,

}

# 3. 发送HTTP POST请求

api_url = "http://home.elvisiky.com:7862/image_upscaler"

response = requests.post(api_url, json=outpainting_request)

# 4. 处理响应

if response.status_code == 200:

    result_data = response.json()

    # 5. 保存base64编码的图像为文件

    detected_map_base64 = result_data["base64_image_out"]

    detected_map_image = Image.open(BytesIO(base64.b64decode(detected_map_base64)))

    detected_map_image.save("base64_image_out.png")

    print("图像保存成功！")

else:

    print(f"API调用失败，HTTP状态码：{response.status_code}")

    print(response.json())

    ```
    """
    try:
        base64_image = request.base64_image
        logging.info(f"image_upscaler, image length {len(base64_image)}")
        img = decode_image_from_base64(base64_image)
        img_cv2 = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
        output = enhance_image(img_cv2, outscale=request.outscale)
        if output is None:
            return image_upscaler_CustomErrorResponse()
        else:
            output_base64 = base64.b64encode(cv2.imencode('.png', output)[1]).decode('utf-8')
            return {"base64_image_out": output_base64}
    except Exception as e:
        return image_upscaler_CustomErrorResponse(str(e))
if __name__ == '__main__':
    uvicorn.run(f'{os.path.basename(__file__).split(".")[0]}:app',
                host='0.0.0.0',
                port=7862,
                reload=False,
                workers=1)
```

![在这里插入图片描述](/static/img/images/03792.png)

【python FastAPI】fastapi中如何限制输入参数，如何让docs更好看，如何自定义错误码json返回

![在这里插入图片描述](/static/img/images/03819.png)






```python
import tkinter as tk
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['FangSong']  # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
# 函数用于执行最小二乘法线性拟合
def linear_regression():
    x_values = [float(x) for x in x_entry.get().split()]
    y_values = [float(y) for y in y_entry.get().split()]
    A = np.vstack([x_values, np.ones(len(x_values))]).T
    m, c = np.linalg.lstsq(A, y_values, rcond=None)[0]
    # 清空画布
    ax[0].clear()
    ax[1].clear()
    # 绘制数据点
    ax[0].scatter(x_values, y_values, label='数据点')
    # 绘制最小二乘法拟合线
    ax[0].plot(x_values, m * np.array(x_values) + c, 'r', label='自己计算的')
    ax[0].set_title(f'自己计算的: Y = {m:.5f} * X + {c:.5f}')
    # 从斜率和截距输入框获取备选直线的参数
    lines_m = [float(m) for m in m_entry.get().split()]
    lines_c = [float(c) for c in c_entry.get().split()]
    # 绘制备选直线
    for i in range(len(lines_m)):
        ax[0].plot(x_values, lines_m[i] * np.array(x_values) + lines_c[i], label=f'直线 {i + 1}')
    # 计算备选直线中最好的拟合直线，得到是哪一条直线最好
    best_line_index = np.argmin([np.sum(np.square(y_values - (m * np.array(x_values) + c))) for m, c in
                                 zip(lines_m, lines_c)])
    ax[0].set_xlabel('X')
    ax[0].set_ylabel('Y')
    ax[0].legend()
    # 在ax[1]绘制最好的直线
    ax[1].scatter(x_values, y_values, label='数据点')
    ax[1].plot(x_values, lines_m[best_line_index] * np.array(x_values) + lines_c[best_line_index], 'r',
               label=f'直线 {best_line_index + 1}')
    ax[1].set_title(
        f'最佳备选线: Y = {lines_m[best_line_index]} * X + {lines_c[best_line_index]},Index: {best_line_index + 1}')
    ax[1].set_xlabel('X')
    ax[1].set_ylabel('Y')
    ax[1].legend()
    canvas.draw()
# 创建Tkinter窗口
root = tk.Tk()
root.title('最小二乘法线性拟合')
frame = tk.Frame(root)
frame.pack(padx=10, pady=10)
# 创建输入框和标签
x_label = tk.Label(frame, text='输入x值（用空格分隔）:')
x_label.pack()
x_entry = tk.Entry(frame)
x_entry.insert(0, '2 4 6 8 10')  # 设置默认值
x_entry.pack()
y_label = tk.Label(frame, text='输入y值（用空格分隔）:')
y_label.pack()
y_entry = tk.Entry(frame)
y_entry.insert(0, '10.046 20.090 30.155 40.125 50.074')  # 设置默认值
y_entry.pack()
m_label = tk.Label(frame, text='输入备选直线的斜率（用空格分隔）:')
m_label.pack()
m_entry = tk.Entry(frame)
m_entry.insert(0, '5.0 5.01 4.97 4.95 5.08')  # 设置默认值
m_entry.pack()
c_label = tk.Label(frame, text='输入备选直线的截距（用空格分隔）:')
c_label.pack()
c_entry = tk.Entry(frame)
c_entry.insert(0, '0.08 0.07 0.12 0.15 0.06')  # 设置默认值
c_entry.pack()
# 创建确认按钮
calculate_button = tk.Button(frame, text='确认', command=linear_regression)
calculate_button.pack()
# 创建大画布
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
canvas = FigureCanvasTkAgg(fig, master=frame)
canvas.get_tk_widget().pack()
canvas.draw()
# 运行Tkinter主循环
root.mainloop()
```

最小二乘法，可视化UI界面

![在这里插入图片描述](/static/img/images/03841.png)






python

```python
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.staticfiles import StaticFiles
from fastapi.responses import HTMLResponse
from typing import List
import requests
app = FastAPI()
# 配置静态文件目录
app.mount("/static", StaticFiles(directory="static"), name="static")
# HTML页面路由
@app.get("/", response_class=HTMLResponse)
async def get_upload_page():
    return """
    <!DOCTYPE html>
    <html>
    <head>
        <title>Image Upload</title>
    </head>
    <body>
        <h1>Image Upload</h1>
        <form method="post" enctype="multipart/form-data">
            <input type="file" name="file">
            <button type="submit">Upload</button>
        </form>
        <div id="descriptions"></div>
        <script src="/static/main.js"></script>
    </body>
    </html>
    """
# 文件上传端点
@app.post("/upload")
async def file_(file: bytes = File(...)):  # 如果要上传多个文件 files: List[bytes] = File(...)
    """使用File类 文件内容会以bytes的形式读入内存 适合于上传小文件"""
    return {"file_size": len(file)}  # 返回文件大小
if __name__ == "__main__":
    import uvicorn
    uvicorn.run('main:app', host='0.0.0.0', port=8000, reload=True, workers=1)
```


js

```js
document.addEventListener('DOMContentLoaded', () => {
  const form = document.querySelector('form');
  const fileInput = document.querySelector('input[type="file"]');
  const descriptions = document.getElementById('descriptions');
  form.addEventListener('submit', async (e) => {
    e.preventDefault();
    const formData = new FormData();
    formData.append('file', fileInput.files[0]);
    try {
      const response = await fetch('/upload', {
        method: 'POST',
        body: formData,
      });
      if (response.ok) {
        const data = await response.json();
        descriptions.innerHTML = `File size: ${data.file_size} bytes`;
      } else {
        descriptions.innerHTML = 'Upload failed';
      }
    } catch (error) {
      console.error('Error:', error);
      descriptions.innerHTML = 'An error occurred';
    }
  });
});
```


结果网页：

![在这里插入图片描述](/static/img/images/03842.png)

【Python Fastapi】js上传文件，fastapi处理，js显示回传信息

我是windows系统，pip install cython_bbox无法编译。





解决：

安装`pip install cpython`

安装VS2019企业版，为了windows能编译一些乱七八糟的，我一般都装个这个。

```shell
key
BF8Y8-GN2QH-T84XB-QVY3B-RC4DF
```

下载源码：`https://github.com/samson-wang/cython_bbox/tree/master`

执行安装：

```
cd C:\Users\xjsd\Downloads\cython_bbox-master\cython_bbox-master
pip install .
```

日志：

```
(py38) C:\Users\xjsd\Downloads\lap-master\lap-master>cd C:\Users\xjsd\Downloads\cython_bbox-master\cython_bbox-master
(py38) C:\Users\xjsd\Downloads\cython_bbox-master\cython_bbox-master>pip install .
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing c:\users\xjsd\downloads\cython_bbox-master\cython_bbox-master
  Preparing metadata (setup.py) ... done
Requirement already satisfied: Cython in c:\users\xjsd\.conda\envs\py38\lib\site-packages (from cython-bbox==0.1.3) (3.0.2)
Requirement already satisfied: numpy in c:\users\xjsd\.conda\envs\py38\lib\site-packages (from cython-bbox==0.1.3) (1.24.4)
Building wheels for collected packages: cython-bbox
  Building wheel for cython-bbox (setup.py) ... done
  Created wheel for cython-bbox: filename=cython_bbox-0.1.3-cp38-cp38-win_amd64.whl size=29288 sha256=ecd828eb5c0cdba3e3b4931d9acb1719cd0869ba675321ae59eba43b85f1368c
  Stored in directory: c:\users\xjsd\appdata\local\pip\cache\wheels\a2\15\10\68bac8f727391b0efe795d418657f56c18299c9404c06ad0ae
Successfully built cython-bbox
Installing collected packages: cython-bbox
Successfully installed cython-bbox-0.1.3
```


![在这里插入图片描述](/static/img/images/03934.png)

numpy装1.23版本，重启pycharm。

Python，如何安装cython_bbox

Linux可以：





```shell
pip install cpython
pip install git+git://github.com/gatagat/lap.git
```

windows可以：

下载`https://github.com/gatagat/lap` 后解压，

安装`pip install cpython`

安装VS2019企业版：

```shell
key
BF8Y8-GN2QH-T84XB-QVY3B-RC4DF
```

执行安装：

```
(py38) C:\Users\xjsd>cd C:\Users\xjsd\Downloads\lap-master\lap-master
(py38) C:\Users\xjsd\Downloads\lap-master\lap-master>python setup.py install
```

![在这里插入图片描述](/static/img/images/03935.png)

也可以：

```
pip install . # 安装当前路径的包
```

完成后：

![在这里插入图片描述](/static/img/images/03936.png)

又发现新版不好用，下个`https://github.com/gatagat/lap/tree/v0.4.0` 老版本的。

Python，如何安装lap，pip安装lap出现问题

CUDA可用，共有 1 个GPU设备可用。





当前使用的GPU设备索引：0

当前使用的GPU设备名称：NVIDIA T1000

GPU显存总量：4.00 GB

已使用的GPU显存：0.00 GB

剩余GPU显存：4.00 GB

PyTorch版本：1.10.1+cu102

```python
import torch
# 检查CUDA是否可用
cuda_available = torch.cuda.is_available()
if cuda_available:
    # 获取GPU设备数量
    num_gpu = torch.cuda.device_count()
    # 获取当前使用的GPU索引
    current_gpu_index = torch.cuda.current_device()
    # 获取当前GPU的名称
    current_gpu_name = torch.cuda.get_device_name(current_gpu_index)
    # 获取GPU显存的总量和已使用量
    total_memory = torch.cuda.get_device_properties(current_gpu_index).total_memory / (1024 ** 3)  # 显存总量(GB)
    used_memory = torch.cuda.memory_allocated(current_gpu_index) / (1024 ** 3)  # 已使用显存(GB)
    free_memory = total_memory - used_memory  # 剩余显存(GB)
    print(f"CUDA可用，共有 {num_gpu} 个GPU设备可用。")
    print(f"当前使用的GPU设备索引：{current_gpu_index}")
    print(f"当前使用的GPU设备名称：{current_gpu_name}")
    print(f"GPU显存总量：{total_memory:.2f} GB")
    print(f"已使用的GPU显存：{used_memory:.2f} GB")
    print(f"剩余GPU显存：{free_memory:.2f} GB")
else:
    print("CUDA不可用。")
# 检查PyTorch版本
print(f"PyTorch版本：{torch.__version__}")
import torch
print(f"CUDA版本：{torch.version.cuda}")
```


windows先装显卡驱动，再装CUDA10.2，最后装了pytorch。

pip install torch==1.10.1+cu102 torchvision==0.13.1+cu102 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu102


![在这里插入图片描述](/static/img/images/03938.png)



```python
import torch
import os
import subprocess

def check_gpu_availability():
    print("==== Checking GPU Availability ====")
    if torch.cuda.is_available():
        print(f"GPU is available. Number of GPUs: {torch.cuda.device_count()}")
        for i in range(torch.cuda.device_count()):
            print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
    else:
        print("No GPU available. Please check your hardware or drivers.")
        return False
    return True

def check_cuda_and_cudnn():
    print("\n==== Checking CUDA and cuDNN Availability ====")
    if torch.cuda.is_available():
        print(f"CUDA version: {torch.version.cuda}")
        print(f"cuDNN version: {torch.backends.cudnn.version()}")
        print(f"cuDNN enabled: {torch.backends.cudnn.enabled}")
    else:
        print("CUDA or cuDNN not available. Please check the installation.")

def test_gpu_computation():
    print("\n==== Testing Simple Computation on GPU ====")
    try:
        
        device = torch.device("cuda:2")
        a = torch.randn(50, 50, device=device)
        b = torch.randn(50, 50, device=device)
        c = torch.matmul(a, b)
        print("Matrix multiplication on GPU successful.")
    except Exception as e:
        print(f"Error during computation on GPU: {e}")

def check_gpu_memory():
    print("\n==== Checking GPU Memory Usage ====")
    try:
        gpu_memory = torch.cuda.memory_summary()
        print(gpu_memory)
    except Exception as e:
        print(f"Error retrieving GPU memory info: {e}")

def run_nvidia_smi():
    print("\n==== Running nvidia-smi Command ====")
    try:
        # Run the nvidia-smi command and capture the output
        result = subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
        if result.returncode == 0:
            print(result.stdout)
        else:
            print(f"Error running nvidia-smi: {result.stderr}")
    except Exception as e:
        print(f"Error running nvidia-smi command: {e}")

def main():
    print("==== Starting GPU Health Check ====")
    
    # Step 1: Check GPU availability
    if not check_gpu_availability():
        return
    
    # Step 2: Check CUDA and cuDNN versions
    check_cuda_and_cudnn()
    
    # Step 3: Test GPU computation
    test_gpu_computation()
    
    # Step 4: Check GPU memory usage
    check_gpu_memory()
    
    # Step 5: Run nvidia-smi to check GPU status
    run_nvidia_smi()
    
    print("\n==== GPU Health Check Completed ====")

if __name__ == "__main__":
    main()

```

【Python】pytorch，CUDA是否可用，查看显卡显存剩余容量

# conda





```c
conda config --set ssl_verify no
conda config --set proxy_servers.http <VPN 服务器地址>:<VPN 服务器端口>
conda config --set proxy_servers.https <VPN 服务器地址>:<VPN 服务器端口>
```


```c
conda install <package_name>
```


# pip

```c
pip --proxy <VPN 服务器地址>:<VPN 服务器端口> install requests
```

example.


```c
 pip install --proxy http://127.0.0.1:10809 -r req.txt
```

给conda、pip提提速，给他一个vpn，还世界清静

# 环境





```shell
pip install jupyter 
```

# 使用

在你想访问的目录下执行：


```c
jupyter notebook --ip=0.0.0.0
```

jupyter 给出提示：

```c
[I 2023-07-28 14:32:43.589 ServerApp] Package notebook took 0.0000s to import
[I 2023-07-28 14:32:43.597 ServerApp] Package jupyter_lsp took 0.0081s to import
[W 2023-07-28 14:32:43.597 ServerApp] A `_jupyter_server_extension_points` function was not found in jupyter_lsp. Instead, a `_jupyter_server_extension_paths` function was found and will be used for now. This function name will be deprecated in future releases of Jupyter Server.
[I 2023-07-28 14:32:43.601 ServerApp] Package jupyter_server_terminals took 0.0034s to import
[I 2023-07-28 14:32:43.601 ServerApp] Package jupyterlab took 0.0000s to import
[I 2023-07-28 14:32:43.639 ServerApp] Package notebook_shim took 0.0000s to import
[W 2023-07-28 14:32:43.640 ServerApp] A `_jupyter_server_extension_points` function was not found in notebook_shim. Instead, a `_jupyter_server_extension_paths` function was found and will be used for now. This function name will be deprecated in future releases of Jupyter Server.
[I 2023-07-28 14:32:43.640 ServerApp] jupyter_lsp | extension was successfully linked.
[I 2023-07-28 14:32:43.643 ServerApp] jupyter_server_terminals | extension was successfully linked.
[I 2023-07-28 14:32:43.647 ServerApp] jupyterlab | extension was successfully linked.
[I 2023-07-28 14:32:43.651 ServerApp] notebook | extension was successfully linked.
[I 2023-07-28 14:32:43.654 ServerApp] Writing Jupyter server cookie secret to /home/xd/.local/share/jupyter/runtime/jupyter_cookie_secret
[I 2023-07-28 14:32:43.827 ServerApp] notebook_shim | extension was successfully linked.
[I 2023-07-28 14:32:43.930 ServerApp] notebook_shim | extension was successfully loaded.
[I 2023-07-28 14:32:43.932 ServerApp] jupyter_lsp | extension was successfully loaded.
[I 2023-07-28 14:32:43.933 ServerApp] jupyter_server_terminals | extension was successfully loaded.
[I 2023-07-28 14:32:43.934 LabApp] JupyterLab extension loaded from /ssd/xd/miniconda3/envs/py38_diffusers_1/lib/python3.8/site-packages/jupyterlab
[I 2023-07-28 14:32:43.934 LabApp] JupyterLab application directory is /ssd/xd/miniconda3/envs/py38_diffusers_1/share/jupyter/lab
[I 2023-07-28 14:32:43.935 LabApp] Extension Manager is 'pypi'.
[I 2023-07-28 14:32:43.937 ServerApp] jupyterlab | extension was successfully loaded.
[I 2023-07-28 14:32:43.939 ServerApp] notebook | extension was successfully loaded.
[I 2023-07-28 14:32:43.939 ServerApp] Serving notebooks from local directory: /ssd/xd/workplace
[I 2023-07-28 14:32:43.939 ServerApp] Jupyter Server 2.7.0 is running at:
[I 2023-07-28 14:32:43.939 ServerApp] http://gpu20:8888/tree?token=490e738359e9e3a73247822c61620bacdfcae0038a9a721e
[I 2023-07-28 14:32:43.939 ServerApp]     http://127.0.0.1:8888/tree?token=490e738359e9e3a73247822c61620bacdfcae0038a9a721e
[I 2023-07-28 14:32:43.939 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 2023-07-28 14:32:43.948 ServerApp] No web browser found: Error('could not locate runnable browser').
```


记住这提示里的token：

490e738359e9e3a73247822c61620bacdfcae0038a9a721e

# 访问

windows电脑浏览器访问：

http://101.20.31.20:8888


默认端口8888，前面的ip需要填写你Linux服务器的IP。