vh
a2b5b58eee
Replaces a bare nohup on irv-ml1:8410 that would not have survived a reboot, handed over by waterland-dev. Tracks vh/waterland @ main (PR #4 merged; main HEAD is exactly the pinned 8025366). Build context is a checkout at /opt/waterland-studio/src, deliberately OUTSIDE the compose dir — deploy-stack.sh rsyncs stacks/<stack>/ with --delete and would otherwise eat it. The Dockerfile is passed out-of-context. Three landmines, all measured: 1. Both uv extras are load-bearing at build AND run. jobs.py shells the renderer out as a literal with no --extra flags, so uv would re-sync at runtime and prune cupy — silently dropping to the numpy path at ~21x wall time. UV_NO_SYNC pins it; UV_OFFLINE makes any failure loud instead of quietly slow. 2. cupy needs CUDA HEADERS for its NVRTC compile, not just the driver and the wheel's runtime libs. The host has a system CUDA toolkit so the nohup process found them by accident; a slim image does not, and every render died 1.7s in with 'Failed to find CUDA headers' printed through argparse's usage banner — which reads like a CLI bug, not a missing toolkit. Fixed with cupy-cuda12x[ctk] (hundreds of MB, vs ~6 GB for a -devel base). 3. The A6000 is host device 1 but container device 0, since compose exposes exactly one GPU. CUDA_VISIBLE_DEVICES_TARGET=0 inside; copying the host's value selects a device that does not exist. /root/.cupy is a volume because the NVRTC compile costs ~17s: verified at 23.3s cold vs 6.1s warm, and re-verified across a restart (23.2s on a fresh cache volume, 6.0s once populated). Warm 256^2+anim beats the 7.4s recorded against bare metal, so containerising cost nothing. Job store seeded with the 4 jobs from the displaced instance. Serial by design (one replica, one card) and unauthenticated, so it stays LAN/WireGuard-only.
12 lines
510 B
Bash
12 lines
510 B
Bash
# waterland-studio on irv-ml1. Real .env lives on the host; every value below
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# is the compose default, so an absent .env is a working configuration.
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WLS_PORT=8410
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WLS_CONTAINER=waterland-studio
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WLS_BACKEND=cupy
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# Docker's device index for the A6000 on this host. Device 0 is the 3090 and
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# hosts the TTS zoo — do not point this at it.
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WLS_GPU_ID=1
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# The A6000's index INSIDE the container. Exactly one GPU is exposed, so it is
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# 0 here even though it is 1 on the host. See the Dockerfile.
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WLS_CUDA_TARGET=0
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