Gensyn RL-Swarm Node Executor
Automated Gensyn RL-Swarm Node Setup with GPU Support
Problem
Gensyn’s RL-Swarm protocol coordinates distributed reinforcement learning training across operator-provided GPUs. Onboarding a node involves Python environment setup, optional Docker containerization, and correct NVIDIA runtime wiring for GPU passthrough — a stack that fragments badly across Ubuntu, Intel macOS, and Apple Silicon. Operators without a uniform bootstrap path routinely hit CUDA/driver mismatches that silently drop them from training rounds.
Approach
- Dual deployment modes selectable via
--local/--dockerflags: native venv for dev iteration, container for production isolation. - Automatic GPU detection against a known-good list (RTX 3090/4090, A100, H100) to short-circuit setup on unsupported hardware before wasting a training round.
- Per-OS package manager dispatch (apt on Ubuntu 18.04+, brew on macOS) with Docker + Docker Compose auto-provisioning.
- Docker runtime flags conditioned on GPU presence so the same script works for CPU-only experimentation and GPU production.
Implementation
Bash driver that detects OS and CUDA-capable GPU, then either clones the RL-Swarm repository into a Python virtual environment for local execution, or pulls and runs the container image with --gpus all when an NVIDIA device is detected. Docker Compose handles multi-service orchestration; nvidia-smi is used as the GPU probe. CLI flags let operators force a specific mode for CI or headless installs.
Outcome
- Single script covering Ubuntu + macOS (Intel and Apple Silicon) with uniform bring-up behavior.
- GPU fast-fail path eliminated the most common class of silent training dropouts — unsupported cards fail at setup, not mid-round.
- Node successfully participated in RL-Swarm training rounds with GPU acceleration on supported hardware.
Technologies
- Scripting: Shell (Bash)
- Container: Docker, Docker Compose
- Runtime: Python (venv)
- GPU: NVIDIA CUDA
- Network: Gensyn RL-Swarm