metadata
license: mit
language:
- en
- ar
tags:
- rocm
- amd
- gpu
- build-environment
- pytorch
- custom-build
- deep-learning
- machine-learning
- llm-enigneering
- arabic
- multilingual
- bilingual
Fame PyTorch ROCm Kit 🔥🛡️
Built with Courage. Built with Fire. Built with Fame.
🚀 About
This project provides a complete and clean build kit for compiling a working PyTorch ROCm version on AMD GPUs – without the usual headaches involving ROCm, HIP, OpenMP, or Git submodules.
You will find everything needed to build, install, and test PyTorch from source, generating a valid .whl package for your local machine.
📋 Project Structure
| File/Folder | Purpose |
|---|---|
build_torch.sh |
Script to build PyTorch from source |
install_torch.sh |
Script to install the generated Wheel file |
test_torch.sh |
Script to test PyTorch import and ROCm status |
fame_torch_freeze.txt |
Environment freeze (package versions) |
pytorch-wheel/ |
(Optional) Folder containing the .whl file |
MY_WHEELI_NOTES.md |
Notes related to the Wheel build |
PyTorch_ROCm_Build_Notes.md |
Extended ROCm build notes |
README.md |
This document – the Fame Masterplan |
🛠️ Build Instructions
- Activate your ROCm environment
source ~/rocm_env/bin/activate
- Navigate to the project folder
cd fame-pytorch-kit/
- Build PyTorch
bash build_torch.sh
The generated .whl file will be located in pytorch/dist/.
📦 Install Instructions
Quick Install
bash install_torch.sh
Manual Install
cd pytorch/dist/
pip install torch-2.8.0a0+gitc402b3b-cp312-cp312-linux_x86_64.whl
Make sure your Python environment (venv) is activated!
✅ Test Instructions
bash test_torch.sh
Or manually:
python -c "import torch; print(torch.cuda.is_available())"
python -c "import torch; print(torch.cuda.get_device_name(0))"
python -c "import torch; print(torch.version.hip)"
Expected: True, GPU Name, HIP version string.
📜 License
This project is licensed under the MIT License.
🤝 Credits
Fame Kit proudly built by sbeierle with 🛡️ Courage, 🔥 Fire, and 🚀 Fame.