Documentation Index
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All ways to install the Blindference node runtime
pip install blindference-node
pip install "blindference-node[gpu]"
pip install "blindference-node[dev]"
git clone https://github.com/baync180705/Blindference-node.git
cd Blindference-node
pip install -e .
pip install -e ".[gpu]"
docker build -t blindference-node .
docker run -e BLF_KEY_PASSWORD=secure_password \
-e BLF_ICL_ENDPOINT=https://icl.blindference.xyz \
blindference-node run
docker-compose up
| Resource | Requirement |
|---|---|
| Python | 3.10+ |
| CPU | 1 core |
| RAM | 512MB |
| Network | Outbound HTTPS |
| GPU | None |
| Resource | Requirement |
|---|---|
| Python | 3.11+ |
| CPU | 2+ cores |
| RAM | 2GB |
| Network | Stable internet |
| TPM | TPM 2.0 chip (for tier 1) |
| Resource | Requirement |
|---|---|
| GPU | NVIDIA with CUDA |
| VRAM | 4GB+ |
| vLLM | Installed |
blindference-node --version
# Expected: blindference-node, version 0.3.0
pip install fails with compilation errorscryptography or web3.
Fix (Ubuntu/Debian):
sudo apt-get update
sudo apt-get install -y python3-dev libssl-dev libffi-dev build-essential
brew install openssl libffi
blindference-node: command not found# Find the binary
python -m site --user-base
# Add to PATH:
export PATH="$PATH:$(python -m site --user-base)/bin"
python -m blindference_node.cli --help
CUDA not available# Check CUDA availability
python -c "import torch; print(torch.cuda.is_available())"
# If False, install NVIDIA drivers and CUDA toolkit
# Or run without GPU:
BLF_SUPPORTED_MODELS=qwen2.5-7b blindference-node init