torch-rechub

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Installation Guide ​

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

System Requirements ​

Before installing Torch-RecHub, ensure your system meets the following requirements:

  • Python 3.9+
  • PyTorch 1.10+ (choose the CPU, NVIDIA CUDA, AMD ROCm, or Huawei Ascend NPU build for your device)
  • NumPy
  • Pandas
  • SciPy
  • Scikit-learn

Installation Methods ​

PyTorch builds are tightly coupled with your hardware, driver, and runtime versions. Before installing, check the official compatibility references for NVIDIA CUDA / PyTorch versions, Huawei Ascend NPU / PyTorch versions, and AMD ROCm / PyTorch versions.

The simplest way to install is via pip:

bash
# Install one PyTorch build matching your device
pip install torch                                                     # CPU
pip install torch --index-url https://download.pytorch.org/whl/cu121  # NVIDIA GPU (CUDA 12.1)
pip install torch torch-npu                                           # Huawei Ascend NPU (requires torch-npu >= 2.5.1)
pip install --index-url https://repo.amd.com/rocm/whl/gfx1151/ "rocm[libraries,devel]" torch torchvision torchaudio  # AMD GPU (ROCm, gfx1151 = Ryzen AI Max+ 395/390/385)

pip install torch-rechub

Latest Development Version ​

To install the development version with the latest 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

# Install one PyTorch build matching your device
uv pip install torch                                                     # CPU
uv pip install torch --index-url https://download.pytorch.org/whl/cu121  # NVIDIA GPU (CUDA 12.1)
uv pip install torch torch-npu                                           # Huawei Ascend NPU (requires torch-npu >= 2.5.1)
uv pip install --index-url https://repo.amd.com/rocm/whl/gfx1151/ "rocm[libraries,devel]" torch torchvision torchaudio  # AMD GPU (ROCm, gfx1151 = Ryzen AI Max+ 395/390/385)

uv sync

Development Environment 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 set up environment
uv sync

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

Verify Installation ​

To verify that Torch-RecHub is correctly installed:

python
import torch_rechub
print(torch_rechub.__version__)

Or run a simple example:

bash
# cd into the script directory first (scripts use relative data paths)
cd examples/matching
python run_ml_dssm.py

Troubleshooting ​

PyTorch Installation ​

If you need to install PyTorch with a specific CUDA version, see NVIDIA CUDA / PyTorch versions.

NVIDIA GPU Support ​

For NVIDIA GPU acceleration, ensure you have:

  • NVIDIA GPU with compute capability 3.5 or higher
  • CUDA Toolkit installed
  • cuDNN library installed

AMD GPU Support (ROCm) ​

For AMD ROCm acceleration on gfx1151 devices such as Ryzen AI Max+ 395/390/385, install ROCm and PyTorch from AMD's wheel index:

bash
uv pip install --index-url https://repo.amd.com/rocm/whl/gfx1151/ "rocm[libraries,devel]" torch torchvision torchaudio

NPU Support (Huawei Ascend) ​

Torch-RecHub supports Huawei Ascend NPU devices, tested on Huawei Ascend 910B.

Please install Ascend-compatible PyTorch and torch-npu versions. For version compatibility details, refer to Huawei Ascend NPU / PyTorch versions.

After installation, import torch_npu in your code, then specify the device in the Trainer:

python
import torch
import torch_npu

trainer = CTRTrainer(model, device='npu:0')

Common Issues ​

If you encounter any installation issues:

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