# 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**


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET 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?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.