- Add IPFSHLSOutput class that uploads segments to IPFS as they're created
- Update streaming task to use IPFS HLS output for distributed streaming
- Add /ipfs-stream endpoint to get IPFS playlist URL
- Update /stream endpoint to redirect to IPFS when available
- Add GPU persistence mode (STREAMING_GPU_PERSIST=1) to keep frames on GPU
- Add hardware video decoding (NVDEC) support for faster video processing
- Add GPU-accelerated primitive libraries: blending_gpu, color_ops_gpu, geometry_gpu
- Add streaming_gpu module with GPUFrame class for tracking CPU/GPU data location
- Add Dockerfile.gpu for building GPU-enabled worker image
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Update save_run_cache to also update actor_id, recipe, inputs on conflict
- Add logging for actor_id when saving runs to run_cache
- Add admin endpoint DELETE /runs/admin/purge-failed to delete all failed runs
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
All callers were passing str(path) but the function expected Path objects,
causing 'str' object has no attribute 'parent' errors when fetching from IPFS.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Major changes:
- Add execute_recipe task that uses S-expression planner
- Recipe S-expression unfolds into plan S-expression with code-addressed cache IDs
- Cache IDs computed from Merkle tree of plan structure (before execution)
- Add ipfs_client.add_string() for storing S-expression plans
- Update run_service.create_run() to use execute_recipe when recipe_sexp available
- Add _sexp_to_steps() to parse S-expression plans for UI visualization
- Plan endpoint now returns both sexp content and parsed steps
The code-addressed hashing means each plan step's cache_id is:
sha3_256({node_type, config, sorted(input_cache_ids)})
This creates deterministic "buckets" for computation results computed
entirely from the plan structure, enabling automatic cache reuse.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add add_json() to ipfs_client for storing JSON data
- Update render_effect task to store provenance on IPFS
- Update execute_dag task to store DAG provenance on IPFS
- Add provenance_cid field to RunStatus model
- Extract provenance_cid from task results
Provenance is now immutable and content-addressed, enabling:
- Cross-L2 verification
- Bitcoin timestamping for dispute resolution
- Complete audit trail on IPFS
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Replace ipfshttpclient library with direct HTTP requests to IPFS API.
This fixes compatibility with newer Kubo versions (0.39.0+) which are
not supported by the outdated ipfshttpclient library.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add PostgreSQL database for cache metadata storage with schema for
cache_items, item_types, pin_reasons, and l2_shares tables
- Add IPFS integration as durable backing store (local cache as hot storage)
- Add postgres and ipfs services to docker-compose.yml
- Update cache_manager to upload to IPFS and track CIDs
- Rename all config references to recipe throughout server.py
- Update API endpoints: /configs/* -> /recipes/*
- Update models: ConfigStatus -> RecipeStatus, ConfigRunRequest -> RecipeRunRequest
- Update UI tabs and pages to show Recipes instead of Configs
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
