![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

我是一个专注技术分享的博主,尤其爱搞深度学习和单片机电子。你现在看到的是我的个人博客网站。