Frank
9bfa0be85d
- 31 new managed skills (anubis/arr/authentik/hermes/freetoken/podman/llm-swap runbooks etc.) now tracked. - Removed superseded skills: omp_harness_config, peon_ping_integration (replaced by user-authored peon-ping-* managed skills), rocmfp4-strix-benchmark-orchestration. - New memory-root skills: niri_compositor_ops, omp_git_workflow, omp_model_config, quadlet-media-migration, homelab-service-triage, hermes-dankclaw-ops, frank-default-large-ops, caddy-auth-dns-playbook.
56 lines
2.0 KiB
Markdown
56 lines
2.0 KiB
Markdown
---
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name: freetoken-build-dankclaw
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description: "Build FreeToken on Dankclaw (Ubuntu 24.04) with CUDA — installs nvidia-cuda-toolkit, sets CUDA_HOME=/usr, overrides nvcc/CUDA version mismatch"
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---
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# FreeToken Build on Dankclaw
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Dankclaw is Ubuntu 24.04 with ROCm already installed. FreeToken builds for **CUDA** (not ROCm) because the kernel package (`_pinned_tensor`) links against `cudart`.
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## Prerequisites (install if missing)
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```bash
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export PATH="/home/dank/.local/bin:$PATH"
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export CUDA_HOME=/usr
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export FREETOKEN_ALLOW_CUDA_MISMATCH=1
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# CUDA toolkit (if not present):
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sudo apt-get install -y nvidia-cuda-toolkit
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# UV (if not present):
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curl -LsSf https://astral.sh/uv/install.sh | sh
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```
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## Key environment variables
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|Variable|Value|Why|
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|`CUDA_HOME`|`/usr`|Ubuntu CUDA toolkit package places nvcc at `/usr/bin/nvcc` and libcudart at `/usr/lib/x86_64-linux-gnu/`|
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|`FREETOKEN_ALLOW_CUDA_MISMATCH=1`|Set|nvcc is 12.4 but torch 2.11.0+cu130 ships CUDA 13.0 — safe to override|
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|`PATH`|`/home/dank/.local/bin:$PATH`|uv installs here|
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## Build procedure
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```bash
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cd /home/dank
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git clone https://github.com/FlashML-org/FreeToken.git
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cd FreeToken
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uv venv && source .venv/bin/activate
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uv pip install -e ".[accel]"
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```
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## Verification
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```bash
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ft --version # → freetoken version 0.1.2
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python3 -c "import torch; print(torch.__version__)" # → 2.11.0+cu130
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python3 -c "from freetoken.kernel import _pinned_tensor; print(_pinned_tensor.__file__)"
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python3 -c "import flashinfer; print(flashinfer.__version__)" # → 0.6.18
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python3 -c "import sgl_kernel; print(sgl_kernel.__version__)" # → 0.4.5
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```
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## Notes
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- `torch.cuda.is_available()` may return `False` with "Can't initialize NVML" warning — this is the management library, not CUDA runtime. The kernels are built and linked correctly.
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- The NVIDIA driver modules may or may not be loaded; they don't affect the build.
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- ROCm is present on Dankclaw but FreeToken's kernel build specifically requires CUDA (links `cudart`).
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