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Getting Started
Wiki Keeper (Grok Bot) edited this page Oct 22, 2026
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Quickstart for the CUDA lab on ShipOfTheseus (RTX 5080, sm_120, ~16 GB).
- Linux host with NVIDIA GPU, CUDA toolkit 13.3 at
/usr/local/cuda,nvcconPATH - Host GCC 6.x–15.x (CUDA 13.3 rejects gcc 16+; CMake auto-picks
g++-15when present) - For L1 engine recipes: the engine under test (e.g. Ollama + local model) already available
nvidia-smi # GPU 0 must show ≥2048 MiB free before train or GPU CI
nvcc --version # expect CUDA 13.3Serialize rule: training and GPU CI share this one card. Do not start both.
cmake -S kernels -B build/kernels -DBKL_ENABLE_CUDA=ON
cmake --build build/kernels -j"$(nproc)"CPU-only configure (CPU CI):
cmake -S kernels -B build/kernels -DBKL_ENABLE_CUDA=OFF./build/kernels/src/bkl_device_hello
# expect: device0: NVIDIA GeForce RTX 5080 compute 12.0
# bkl device_hello sm_120 ok
./build/kernels/src/bkl_graph_launch_bench --out results/graph-launch-bench.json
./build/kernels/src/bkl_green_ctx_bench --out results/green-ctx-bench.jsonOutputs under results/ (gitignored).
python3 tools/test_gpu_ci_policy.py
python3 tools/test_publish_gpu_validation.py
python3 tools/check_f0_gpu_sampler.py # F0 sampler against NVML fakes
# F0 validators — see docs/f0-*.md and fixtures/recipes/l1-prefix-kv-reuse.mdrecipes/l2-green-ctx-bench.mdrecipes/l3-device-hello.mdrecipes/l3-graph-launch-bench.mdrecipes/kernel-ablation.mdrecipes/compute-sanitizer.mdrecipes/f0-nvml-capability.md-
recipes/f0-gpu-sampler.md— per-run NVML sampler around a MiniCPM5 canary (F0-GPU-Sampler) -
recipes/wsl-gpu-runner.md— register the ShipOfTheseus runner inside WSL2 Ubuntu 24.04
- Architecture — L1/L2/L3, forge boundary, F0, CI
- F0-GPU-Sampler — per-run GPU sampling for Agoge runs
- Glossary — terminology
- Repo docs:
docs/KERNELS.md,docs/RELEASE_V0_2_0.md,docs/FORGE_CONSUME.md
Refreshed 2026-10-22 by Wiki Keeper (Grok Bot) from code at main @ 853a2fb (tagged v0.1.0; release target v0.2.0).