> For the complete documentation index, see [llms.txt](https://ai-candy2023.gitbook.io/introduction/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-candy2023.gitbook.io/introduction/ai-candy-on-youtube/ai-xiang-mu-ben-di-bu-shu/zai-python-xu-ni-huan-jing-xia-shi-yong-vs-code-or-pycharm.md).

# 在Python虚拟环境下使用 VS Code or PyCharm

## Check NVIDIA Driver and CUDA Version 

1. cmd
2. nvidia-smi

<figure><img src="/files/v8CQy1EnfDx6GUb02224" alt=""><figcaption></figcaption></figure>

## VS Code

1. Install VS Code
2. Open VS Code
3. Install extention (Python, Jupyter)&#x20;
4. Ctrl + Shift + P
5. Python: Select Interpreter  (Select virtual python environment)
6. Select pytorch (Python 3.11.9)

<figure><img src="/files/uUDAX5FaHRLSgCCkPpoI" alt=""><figcaption></figcaption></figure>

## PyCharm

* Install PyCharm
* Create a new project
* Select Custom environment
* Select existing
* Select Conda Type
* Select Environment

<figure><img src="/files/F205HtBAl1pz22GOrwJz" alt=""><figcaption></figcaption></figure>

## Check version and GPU (<mark style="color:red;">batch file</mark>)

<pre><code>## check_v.py

import torch
import sys

print("Python Version is",sys.version)
print("CUDA Version is", torch.version.cuda)
print("Pytorch Version is", torch.__version__)
print("Whether CUDA is supported by our system:", torch.cuda.is_available())

device_id =  torch.cuda.current_device()
print("Current Device name:", torch.cuda.get_device_name(device_id))

<strong>cuda_id = "GPU available for cuda:"+str(device_id)
</strong>device = cuda_id if torch.cuda.is_available() else 'Only CPU can be used'
print(device)

</code></pre>

```
## entryTorch.bat

call conda activate pytorch
python check_v.py
```

### Install cuDNN

1. <https://developer.nvidia.com/cudnn>
2. Download cuDNN Library
3. Select Windows -> x86\_64 -> Tarball -> 12 (CUDA  Version)
4. Download and Unzip
5. go to C:\Program Files\NVIDIA Corporation
6. Create new folder: CUDNN
7. Create a new folder, name it is v9.0 which in the folder CUDNN
8. copy unzip files into the v9.x folder
9. add path C:\Program Files\NVIDIA Corporation\CUDNN\v9.x\bin into **Environment Variables**