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Installation

This page describes how to install Fujitsu One Compression (OneComp).

Requirements

  • Python 3.12 or later (< 3.14)
  • PyTorch (CPU or CUDA)

For Users (pip)

Step 1: Install PyTorch

Install the appropriate version of PyTorch for your system.

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

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

Check your CUDA version:

nvcc --version
# or
nvidia-smi

Verify PyTorch GPU support:

import torch
print(torch.cuda.is_available())

Step 2: Install OneComp

pip install onecomp

To enable visualization features (matplotlib), install with the visualize extra:

pip install onecomp[visualize]

uv is a fast Python package and project manager written in Rust. It provides deterministic, reproducible environments via its lockfile.

# Install uv (macOS or Linux)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and set up
git clone https://github.com/FujitsuResearch/OneCompression.git
cd OneCompression
uv sync --extra cu128 --extra dev --extra visualize

The uv sync command creates a virtual environment and installs all dependencies (including torchvision from the same CUDA index as PyTorch). Replace cu128 with the appropriate CUDA variant for your system: cpu, cu118, cu121, cu124, cu126, or cu128.

Adding --extra dev installs development tools (black, pytest, pylint). Adding --extra visualize installs matplotlib for visualization features.

To use vLLM for serving quantized models, add --extra vllm:

uv sync --extra cu128 --extra dev --extra visualize --extra vllm

Warning

Do not install vLLM with uv pip install vllm after uv sync. Packages installed via uv pip are not tracked by the lockfile and will be removed or overwritten by subsequent uv sync or uv run commands. Always use --extra vllm instead.

Running Commands

uv run onecomp --version
uv run onecomp TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
uv run pytest tests/ -v
uv run python example/example1.py

source .venv/bin/activate
onecomp --version
onecomp TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
pytest tests/ -v
python example/example1.py

For Developers (pip)

git clone https://github.com/FujitsuResearch/OneCompression.git
cd OneCompression

# Install PyTorch with CUDA support
pip install torch --index-url https://download.pytorch.org/whl/cu128

# Install onecomp with development dependencies
pip install -e ".[dev]"

Building Documentation Locally

uv sync --extra docs
uv run mkdocs serve

Then open http://127.0.0.1:8000 in your browser.