torch-rechub

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Installation Guide ​

This document provides detailed installation instructions for Torch-RecHub, including both stable and development versions.

Requirements ​

Before installing Torch-RecHub, ensure you have the following prerequisites:

  • Python 3.9+
  • PyTorch 1.7+ (CUDA-enabled version recommended for GPU acceleration)
  • NumPy
  • Pandas
  • SciPy
  • Scikit-learn

Installation Methods ​

The easiest way to install Torch-RecHub is via pip:

bash
pip install torch-rechub

Latest Development Version ​

To install the latest development version with the most recent features:

bash
# Install uv first (if not already installed)
pip install uv

# Clone and install
git clone https://github.com/datawhalechina/torch-rechub.git
cd torch-rechub
uv sync

Development Setup ​

If you want to contribute to Torch-RecHub or work with the source code:

bash
# 1. Fork and clone the repository
git clone https://github.com/YOUR_USERNAME/torch-rechub.git
cd torch-rechub

# 2. Install dependencies and setup environment
uv sync

# 3. Install the package in development mode
uv pip install -e .

Verification ​

To verify that Torch-RecHub is installed correctly, you can run:

python
import torch_rechub
print(torch_rechub.__version__)

Or run a simple example:

bash
python examples/matching/run_ml_dssm.py

Troubleshooting ​

PyTorch Installation ​

If you need to install PyTorch with specific CUDA support, visit PyTorch's official website for installation instructions tailored to your system.

GPU Support ​

For GPU acceleration, ensure you have:

  • NVIDIA GPU with CUDA Compute Capability 3.5 or higher
  • CUDA Toolkit installed
  • cuDNN library installed

Common Issues ​

If you encounter any installation issues, please:

  1. Check the GitHub Issues
  2. Create a new issue with detailed error messages and system information
  3. Refer to the FAQ section