CataPro本地安装教程--No GPU--cpu only模式--网络资料整理

如何在本地（windows10; WSL, version=2.0; Ubuntu 22.0.4）部署CataPro， 没有GPU加速，只是本地的cpu模式？

To clone and run the CataPro program from the GitHub repository (https://github.com/zchwang/CataPro) on your Windows 10 machine using WSL 2 (Windows Subsystem for Linux) and CPU mode, follow these step-by-step instructions. I'll assume you have basic familiarity with WSL and terminal commands, but I'll keep it clear and detailed for ease of use.PrerequisitesBefore proceeding, ensure you have the following set up:

• WSL 2 is installed and configured on your Windows 10 system (you mentioned version 2.0).
• A Linux distribution (e.g., Ubuntu) is installed in WSL 2. You can verify this by running wsl -l -v in a Windows Command Prompt or PowerShell to confirm your distro and version.
• Git is installed in your WSL Linux environment. If not, install it with:

bash

sudo apt update && sudo apt install git

• Python 3 and pip are installed in WSL. You can check with:

bash

python3 --version

pip3 --version

If not installed, run:

bash

sudo apt install python3 python3-pip

• Since you want to run in CPU mode (no GPU), the process will focus on ensuring compatibility without CUDA or GPU-specific dependencies.

Step-by-Step Instructions1. Open WSL Terminal

• Open a terminal in your WSL Linux distribution. You can do this by:
  • Opening Windows Terminal, Command Prompt, or PowerShell and typing wsl to enter the default Linux distro.
  • Alternatively, search for "Ubuntu" (or your installed distro) in the Windows Start menu and open it.

2. Clone the Repository

• Navigate to the directory where you want to store the project. For example, to place it in your home directory under a folder called projects:

bash

mkdir -p ~/projects

cd ~/projects

• Clone the CataPro repository from GitHub:

bash

git clone https://github.com/zchwang/CataPro.git

• This will create a folder named CataPro in your current directory containing the repository files.

3. Navigate to the Project Directory

• Move into the cloned repository:

bash

cd CataPro

4. Review the Repository

• Check the repository’s README or documentation for specific setup instructions. Based on the CataPro repository (as of my last update), it’s a Python-based project for protein structure prediction. It likely depends on libraries like PyTorch, NumPy, and others, but we’ll ensure it runs in CPU mode.
• If the README specifies hardware requirements or GPU usage, we’ll override any GPU-specific settings to use CPU.

5. Set Up a Virtual Environment (Recommended)To avoid conflicts with other Python packages, create a virtual environment:

bash

python3 -m venv venv

source venv/bin/activate

• After activation, your terminal prompt should show (venv) to indicate you’re in the virtual environment.

6. Install Dependencies

• The repository likely includes a requirements.txt file listing required Python packages. Install them with:

bash

pip3 install -r requirements.txt

• If there’s no requirements.txt, check the README or code for dependencies. Common dependencies for such projects include:

bash

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

pip3 install numpy pandas scipy

• • The --index-url https://download.pytorch.org/whl/cpu ensures you install the CPU-only version of PyTorch, which is critical since you’re not using a GPU.
• If additional dependencies are mentioned in the README (e.g., BioPython, ProDy), install them as needed:

bash

pip3 install biopython prody

7. Configure for CPU Mode

• Since you’re running without a GPU, ensure the code uses CPU. Most modern Python libraries like PyTorch automatically fall back to CPU if no GPU is detected, but you should verify:
  • Open key Python scripts (e.g., main.py or similar) in a text editor (e.g., nano, vim, or use VS Code from Windows accessing WSL files via \\wsl$\).
  • Look for lines specifying device settings, such as:

python

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

• • • If you see "cuda" hardcoded, change it to "cpu":

python

device = torch.device("cpu")

• • Save any changes.

8. Run the Program

• The README or repository should specify how to run the program. For example, if there’s a main.py script, you might run:

bash

python3 main.py

• If the repository requires specific commands or arguments (e.g., input files, configuration), follow the instructions in the README. For example:

bash

python3 main.py --input <path_to_input_file> --mode cpu

• If you encounter errors about missing files or configurations:
  • Check the README for required input data or pretrained models.
  • Download any necessary datasets or models as instructed (e.g., from links in the repository).

9. Troubleshooting Common Issues

• Missing Dependencies: If you get errors about missing modules, install them with pip3 install <module_name>.
• Python Version Issues: Ensure your Python version matches the repository’s requirements (e.g., Python 3.8+). Check with:

bash

python3 --version

• Path Issues: If the program can’t find files, ensure you’re in the correct directory (pwd to check) and that paths in the code or config files are correct.
• CPU Performance: Running on CPU may be slower than GPU, especially for deep learning tasks. Be patient, or check if the repository offers lighter models or settings for CPU use.
• WSL File Access: If you need to access files from Windows, they’re available in WSL at /mnt/c/ (e.g., C:\Users\YourName is /mnt/c/Users/YourName).

10. Verify the Program Runs

• After running the program, check for expected outputs (e.g., predicted protein structures, log files, or console output) as described in the README.
• If the program requires input data (e.g., protein sequences), ensure you’ve provided them correctly.

Additional Notes

• Disk Space: Ensure you have enough disk space in WSL for the repository and dependencies. Check with:

bash

df -h

• Updating WSL: If you encounter WSL-related issues, ensure it’s up to date:

bash

wsl --update

(Run this in a Windows Command Prompt or PowerShell.)

• Repository-Specific Issues: If the README or code mentions GPU-specific optimizations that you can’t modify, you may need to contact the repository maintainers (e.g., via GitHub Issues) for CPU-specific guidance.
• Accessing Files from Windows: You can edit files or view outputs in Windows by accessing your WSL filesystem at \\wsl$\<distro_name>\home\<username>\projects\CataPro in File Explorer.

Example WorkflowAssuming the repository has a requirements.txt and a main.py script:

bash

# Open WSL terminal

wsl

# Navigate and clone

mkdir -p ~/projects

cd ~/projects

git clone https://github.com/zchwang/CataPro.git

cd CataPro

# Set up virtual environment

python3 -m venv venv

source venv/bin/activate

# Install dependencies

pip3 install -r requirements.txt

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Run the program

python3 main.py

If You Need Help

• If the README is unclear or missing, let me know, and I can search the repository or related resources for specific instructions.
• If you encounter errors, share the error message, and I’ll help troubleshoot.
• If you want to confirm specific repository details (e.g., dependencies or run commands), I can check the GitHub page or search for additional context.

Let me know how it goes or if you hit any snags!