Root cause: render-worker and render-worker-light shared the same GPU,
causing contention. Complex TRB renders went from 17s → 36s (2x slower).
Changes:
- Thumbnails back to asset_pipeline queue (not asset_pipeline_light)
- Dispatch routing always uses asset_pipeline (no queue splitting)
- render-worker-light gated behind "multi-gpu" profile — only starts with:
docker compose --profile multi-gpu up -d
- For single-GPU setups: all rendering is sequential on one worker
The dual queue approach is correct for multi-GPU machines where each
worker gets its own GPU. On single-GPU, serial execution is faster
than concurrent GPU contention.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Task 4: Dual render queue
- render-worker: heavy (asset_pipeline, concurrency=1) — HQ 2048x2048, animations
- render-worker-light: light (asset_pipeline_light, concurrency=2) — thumbnails, <=1024
- Thumbnails routed to light queue automatically
- Order line renders routed by resolution at dispatch time
Task 5: GLB caching (skip re-tessellation)
- Before tessellating, check if gltf_geometry MediaAsset exists for the cad_file_id
- If found, copy to expected path — render_blender.py finds it and skips tessellation
- Saves 7-11s per re-render of the same product
Task 6: WebP output format
- New 'webp' option in output_format (OutputType admin)
- Blender renders PNG intermediate, Pillow converts to WebP (quality=90, method=4)
- 50-70% smaller files with no visible quality loss
- Correct MIME type (image/webp) in MediaAsset
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- @timed_step decorator with wall-clock + RSS tracking (pipeline_logger)
- Blender timing laps for sharp edges and material assignment
- MeshRegistry pattern: eliminate 13 scene.traverse() calls across viewers
- Lazy material cloning (clone-on-first-write in both viewers)
- _pipeline_session context manager: 7 create_engine() → 2 in render_thumbnail
- KD-tree spatial pre-filter for sharp edge marking (bbox-based pruning)
- Batch material library append: N bpy.ops.wm.append → single bpy.data.libraries.load
- GMSH single-session batching: compound all solids into one tessellation call
- Validate part-materials save endpoints against parsed_objects (prevents bogus keys)
- ROADMAP updated with completion status
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The queue handles far more than thumbnails: OCC tessellation, USD master
generation, GLB production, order line renders, and workflow renders.
asset_pipeline better reflects its role as the render-worker's primary queue.
Updated all references in: task decorators, celery_app.py, beat_tasks.py,
docker-compose.yml worker command, worker.py MONITORED_QUEUES, admin.py,
CLAUDE.md, LEARNINGS.md, Dockerfile, helpTexts.ts, test files,
and all .claude/commands/*.md skill files.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Hartmut