Use devel image for compilation, runtime for final image.
Keeps image smaller while enabling NVDEC decode.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Build decord with -DUSE_CUDA=ON for true NVDEC hardware decode
- Use DLPack for zero-copy transfer from decord to CuPy
- Frames stay on GPU throughout: decode -> process -> encode
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Replace decord (CPU-only pip package) with PyNvCodec which provides
direct NVDEC access. Frames decode straight to GPU memory without
any CPU transfer, eliminating the memory bandwidth bottleneck.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Install decord in GPU Dockerfile for hardware video decode
- Update GPUVideoSource to use decord with GPU context
- Decord decodes on GPU via NVDEC, avoiding CPU memory copies
- Falls back to FFmpeg pipe if decord unavailable
- Enable STREAMING_GPU_PERSIST=1 for full GPU pipeline
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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>
