From c8ab4522f28a49a9249d2ef65457bbd265a001ab Mon Sep 17 00:00:00 2001 From: Evgeny Demchenko Date: Thu, 14 May 2026 21:17:34 +0100 Subject: [PATCH] initial commit: design specification + repo scaffolding MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit cuframes — open-source FFmpeg-плагин и runtime library для zero-copy sharing декодированных видеокадров между процессами через CUDA IPC. Содержимое initial commit: - docs/architecture.md — полная design-spec (418 строк) с prior art, protocol design, API draft, phase plan, acceptance criteria - README.md — landing с описанием идеи, состава, quickstart-tease, roadmap, ссылки на community-discussions подтверждающие спрос - CONTRIBUTING.md — guidelines, code style, commit message convention - CHANGELOG.md — Keep a Changelog format, Unreleased / 0.0.1 - LICENSE — LGPL-2.1+ (compatibility с FFmpeg) - .gitignore — build/CMake/Docker/Python/CUDA-specific Следующие шаги (отдельные коммиты): - docker/Dockerfile.dev (CUDA 12.x dev environment) - tools/spike/ (Phase 0 PoC код для measurement CUDA IPC latency) --- .gitignore | 78 +++++++ CHANGELOG.md | 20 ++ CONTRIBUTING.md | 78 +++++++ LICENSE | 501 +++++++++++++++++++++++++++++++++++++++++++ README.md | 88 ++++++++ docs/architecture.md | 418 ++++++++++++++++++++++++++++++++++++ 6 files changed, 1183 insertions(+) create mode 100644 .gitignore create mode 100644 CHANGELOG.md create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 README.md create mode 100644 docs/architecture.md diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..22e5c4d --- /dev/null +++ b/.gitignore @@ -0,0 +1,78 @@ +# Build artefacts +build/ +out/ +*.o +*.a +*.so +*.so.* +*.dylib +*.dll +*.obj + +# CMake +CMakeFiles/ +CMakeCache.txt +cmake_install.cmake +compile_commands.json +CTestTestfile.cmake +install_manifest.txt +Testing/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ +.DS_Store +.cache/ +.ccache/ + +# Python +__pycache__/ +*.py[cod] +*$py.class +*.egg-info/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.venv/ +venv/ +dist/ +*.whl + +# Docker +.dockerignore.local + +# Local config / secrets +.env +.env.local +local_settings.json + +# Profiling +*.prof +*.svg +perf.data* +nsys-rep/ +*.nvprof +*.qdrep + +# Test outputs +test-results/ +coverage/ +*.gcov +*.gcda +*.gcno + +# Build artefacts: FFmpeg source tree (выкачивается build-скриптом) +third_party/ffmpeg/ +ffmpeg-*/ +ffmpeg-build/ + +# CUDA artefacts +*.cubin +*.fatbin +*.ptx + +# Spike measurement output +tools/spike/measurements/ diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..b6f98f4 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,20 @@ +# Changelog + +Все заметные изменения в этом проекте документируются в этом файле. + +Формат основан на [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), +проект следует [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [Unreleased] + +### Added +- Design specification (docs/architecture.md) +- Prior art analysis — подтверждено что ниша свободна +- Roadmap v0.1 — 6 phases, ~6-8 недель работы + +## [0.0.1] - 2026-05-14 + +### Added +- Initial repository +- LICENSE (LGPL-2.1+) +- README.md, CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..e931bf4 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,78 @@ +# Contributing to cuframes + +Спасибо за интерес. Проект на ранней стадии — каждый вклад ценен. + +## Перед началом + +1. Прочитайте [docs/architecture.md](docs/architecture.md) — там подробный + design document с обоснованием решений +2. Проверьте [open issues](https://git.goldix.org/gx/cuframes/issues) — + возможно ваша идея уже обсуждается +3. Большие изменения — сначала RFC-issue, чтобы синхронизировать с design + +## Структура репо + +``` +cuframes/ +├── docs/ архитектура, протокол, benchmarks +├── filter/ FFmpeg filter (vf_cuda_ipc_export.c) +├── libcuframes/ producer/consumer core (C) +├── bindings/ C++ и Python wrappers +├── examples/ working examples (CV, PyTorch, Frigate) +├── docker/ Dockerfile'ы для dev и distribution +├── scripts/ build, patch, packaging +├── tools/spike/ PoC код для Phase 0 +└── tests/ integration + stress +``` + +## Development setup + +Всё собирается **в Docker**, чтобы не зависеть от системного CUDA toolkit. + +```bash +# Dev-контейнер с CUDA 12.x + build tools +docker compose -f docker/docker-compose.dev.yml up -d +docker exec -it cuframes-dev bash + +# Внутри контейнера +cmake -B build -S . -G Ninja +cmake --build build +ctest --test-dir build --output-on-failure +``` + +См. [docker/README.md](docker/README.md) для деталей. + +## Code style + +- **C** — стиль FFmpeg (для filter), K&R + 4 spaces +- **C++** — C++17, Google C++ style (clang-format с проектным `.clang-format`) +- **Python** — PEP 8, black + ruff + +CI прогоняет formatter checks автоматически. + +## Commit messages + +Conventional Commits: + +``` +feat(filter): add cuda_ipc_export FFmpeg filter +fix(consumer): handle producer reconnect race +docs(arch): clarify backpressure semantics +test: add 24-hour stress test +chore: bump CMake to 3.25 +``` + +## Phase 0 contributors + +Если хотите помочь с самым ранним этапом (PoC spike) — +см. [tools/spike/README.md](tools/spike/README.md) (когда появится). + +Главные вопросы Phase 0: +- Latency producer→consumer на разных GPU (Ampere/Ada/Blackwell) +- CUDA IPC behavior cross-container (docker → docker, host → docker) +- Sync через `cudaStreamSynchronize` vs CUDA IPC events +- Memory leak detection при random producer/consumer kill + +## License + +Contributions accepted under LGPL-2.1+ (см. [LICENSE](LICENSE)). diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f6683e7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,501 @@ + GNU LESSER GENERAL PUBLIC LICENSE + Version 2.1, February 1999 + + Copyright (C) 1991, 1999 Free Software Foundation, Inc. + + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + +[This is the first released version of the Lesser GPL. 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See the GNU + Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public + License along with this library; if not, see . + +Also add information on how to contact you by electronic and paper mail. + +You should also get your employer (if you work as a programmer) or your +school, if any, to sign a "copyright disclaimer" for the library, if +necessary. Here is a sample; alter the names: + + Yoyodyne, Inc., hereby disclaims all copyright interest in the + library `Frob' (a library for tweaking knobs) written by James Random Hacker. + + , 1 April 1990 + Moe Ghoul, President of Vice + +That's all there is to it! diff --git a/README.md b/README.md new file mode 100644 index 0000000..6009ceb --- /dev/null +++ b/README.md @@ -0,0 +1,88 @@ +# cuframes + +Zero-copy sharing декодированных видеокадров между процессами через CUDA IPC. + +**Статус:** ⚠️ Design phase. Дизайн-спецификация готова, реализация в процессе. +**Лицензия:** LGPL-2.1+ +**Платформы:** Linux, NVIDIA CUDA ≥ 12.0 + +## Идея в одну минуту + +Типичный setup с несколькими сервисами видеоаналитики: + +``` +Camera ─► ffmpeg #1 (NVR, decode + record) + ─► ffmpeg #2 (AI-detector, decode + inference) + ─► ffmpeg #3 (custom analytics, decode + ...) +``` + +Каждый сервис делает **свой** decode на GPU. На 16 cameras × 25 fps × 3 consumers +это сотни лишних NVDEC operations и GB/s VRAM-bandwidth впустую. + +`cuframes` устраняет дублирование: + +``` +Camera ─► ffmpeg + cuframes filter ─► VRAM ─┬─► consumer 1 (NVR) + ├─► consumer 2 (AI) + └─► consumer 3 (analytics) +``` + +Один decode, frame остаётся в VRAM, любое количество consumers читают +**zero-copy** через CUDA IPC. + +## Состав + +- **FFmpeg filter `cuda_ipc_export`** — добавляется в любой ffmpeg-pipeline +- **`libcuframes`** — C library + C++/Python bindings для consumers +- **Docker images** — drop-in replacement для существующих setups (включая Frigate) + +## Quickstart + +> 🚧 в разработке + +```bash +# Producer (после v0.1) +docker run --gpus all -v /run/cuframes:/run/cuframes ghcr.io//cuframes-ffmpeg:N \ + ffmpeg -hwaccel cuda -i rtsp://camera/stream \ + -vf "scale_cuda=1920:1080,cuda_ipc_export=key=cam1" \ + -c:v copy -f segment recording.mp4 + +# Consumer (C++) +#include +cuframes::Subscriber sub("cam1"); +while (auto frame = sub.next()) { + // frame->cuda_ptr — device pointer, zero-copy + process_on_cuda(frame->cuda_ptr, frame->width, frame->height); +} +``` + +## Документация + +- [docs/architecture.md](docs/architecture.md) — полный design document +- [docs/protocol.md](docs/protocol.md) — wire protocol *(в разработке)* +- [docs/quickstart.md](docs/quickstart.md) — *(в разработке)* + +## Why + +Подтверждённый спрос в Frigate community ([#17033](https://github.com/blakeblackshear/frigate/discussions/17033), +[#20191](https://github.com/blakeblackshear/frigate/discussions/20191), [#21559](https://github.com/blakeblackshear/frigate/discussions/21559)) +— люди тычатся в NVDEC saturation при многокамерных setup'ах. Решения уровня +NVIDIA DeepStream закрытые / vendor-locked. Open source FFmpeg-plugin для этой +ниши **не существует**. См. подробный prior-art analysis в `docs/architecture.md`. + +## Roadmap + +| Phase | Что | Срок | +|---|---|---| +| 0 | PoC spike, CUDA IPC latency measurements | 3 дня | +| 1 | `libcuframes` (producer/consumer ring buffer + handshake protocol) | 1 неделя | +| 2 | FFmpeg filter `vf_cuda_ipc_export` + patched FFmpeg build | 1-2 недели | +| 3 | C++ / Python bindings | 1 неделя | +| 4 | Docker packaging + Frigate drop-in image | 3-5 дней | +| 5 | Reference consumer: 16-камерный mosaic compositor | 1 неделя | +| 6 | OSS launch (HN, reddit, FFmpeg upstream PR) | 3-5 дней | + +## Contributing + +Проект на ранней стадии. Перед PR сверьтесь с [docs/architecture.md](docs/architecture.md) +и [CONTRIBUTING.md](CONTRIBUTING.md). diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 0000000..87a0c7b --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,418 @@ +# CUDA IPC Frame Bus — open-source FFmpeg-плагин + +**Working name:** `cuframes` (рабочий, finalize при release) +**License:** LGPL-2.1+ (compatibility с FFmpeg) +**Статус:** дизайн-спецификация v0.1 +**Дата:** 2026-05-14 + +## TL;DR + +FFmpeg-плагин и C/C++ библиотека для zero-copy sharing декодированных +видеокадров между процессами через CUDA IPC. Один процесс делает decode на +GPU, кадр остаётся в VRAM, любое количество других процессов читает его без +копирования. Distributed как open-source. + +Use case 0 (наш): Frigate decode'ит видео, `cctv-companion` consume'ит те же +кадры для mosaic-композиции на TV. Без двойного decode. + +Use case N (общий): любая комбинация AI-pipeline + NVR + аналитика на одной +машине с NVIDIA GPU. + +## 1. Контекст и мотивация + +Сейчас типичный setup: + +``` +Camera → ffmpeg #1 (NVR, decode + record) + → ffmpeg #2 (AI-detector, decode + inference) + → ffmpeg #3 (custom analytics, decode + ...) + +Каждый decode = NVDEC operation + VRAM. Inter-process передача +требует cudaMemcpy в host RAM и обратно. На 16 cameras × 25 fps × +3 consumers = 75% VRAM-bandwidth впустую. +``` + +Существующие решения: + +| | Что | Почему не подходит | +|---|---|---| +| NVIDIA DeepStream | Production-grade SDK, в т.ч. shared GPU buffers | Закрытое, vendor lock-in, требует переписи pipeline | +| GStreamer NVMM | NVIDIA Multimedia memory plugin | Inter-process только через DMA-buf, сложная интеграция | +| Custom CUDA IPC | Прямой `cudaIpcGetMemHandle` | Требует написать с нуля у каждого; нет интеграции с FFmpeg | +| Frigate +1 ffmpeg | Каждый сервис свой decode | Дублирование, что и побудило проект | + +Пустая ниша: **простой FFmpeg-filter** который добавляется в любой +ffmpeg-pipeline и публикует декодированные кадры через CUDA IPC. Без +рерайта пайплайна, без vendor SDK, без специальной библиотеки на стороне +producer'а — просто extra-flag к ffmpeg. + +## 1.bis Prior art / competitive analysis + +Research проведён 2026-05-14, документирует что **ниша свободна**. + +### Существующие технологии (building blocks, не plug-and-play) + +| Проект | License | Что | Почему не аналог | +|---|---|---|---| +| **NVIDIA CUDA IPC API** | Proprietary headers, free use | Core `cudaIpcGetMemHandle`/`cudaIpcOpenMemHandle` | Сырой API, не плагин для FFmpeg — наш фундамент | +| **NVIDIA DeepStream** | Closed-source SDK | `NvDsBufSurface` shared-buffers + ML pipeline | Vendor lock-in, требует переписать pipeline в DeepStream API. Не FFmpeg-filter. | +| **NVIDIA VPF** ([github](https://github.com/NVIDIA/VideoProcessingFramework)) | Apache 2.0 | Python bindings вокруг NvCodec | **Single-process только**, inter-process не делает | +| **PyNvVideoCodec** | NVIDIA-proprietary, free | Python decoder с PyTorch zero-copy interop | Single-process | +| **dora-rs CUDA IPC** ([dora-rs.ai](https://dora-rs.ai/docs/guides/Development/Cuda/)) | Apache 2.0 | `torch_to_ipc_buffer()` / `ipc_buffer_to_ipc_handle()` для dataflow framework | Robotics-focused, работает на уровне torch tensors. Не FFmpeg-плагин. **Можем переиспользовать low-level подходы**. | +| **GStreamer NVMM** | LGPL | NVIDIA Multimedia Memory plugins | Inter-thread (intra-process); inter-process через DMA-buf только на Jetson | +| **BabitMF** (Bytedance) | Apache 2.0 | GPU filtering framework | Single-process intra-pipeline, не FFmpeg-filter | +| **Frigate go2rtc relay** | MIT | Re-stream compressed bytes между consumers | Не zero-copy — каждый consumer декодит снова | + +### Подтверждение спроса в community + +Threads в `blakeblackshear/frigate`, ровно описывающие нашу проблему: + +- **#17033** — «Recommended GPU for Video Decoding with ~80 Cameras» — обсуждение saturation NVDEC engines при наивном double-decode +- **#20191** — «As soon as I add a second camera, nothing works» — пользователи натыкаются на decode-related issues +- **#21559** — «System with 25 cameras — which GPU» — все ответы про "купите больше железа", решения через shared decode нет +- **#21865** — «Combining Nvidia GPU and integrated GPU» — workaround через split decoders, что лишний раз доказывает что shared decode был бы полезен + +### Проверка FFmpeg upstream + +- FFmpeg-devel mailing list 2024-2026: **ноль** patches содержащих CUDA IPC inter-process filter +- FFmpeg master tree (8.0.1): CUDA-фильтры (`scale_cuda`, `bilateral_cuda`, `bwdif_cuda`, `libvmaf_cuda`, `overlay_cuda`) — все intra-process +- Search GitHub `vf_cuda_ipc | cudaipc | cuda_ipc_export`: zero forks/patches с этой функциональностью + +### Вывод + +**Ниша свободна.** Подтверждённый спрос (Frigate community), стабильные building-blocks (NVIDIA CUDA IPC API + dora-rs reference). Нет risk'а дублирования усилий. Sources проверены 2026-05-14. + +## 2. Архитектура + +### 2.1 Компоненты репозитория + +``` +cuframes/ ← OSS repo +├── filter/ +│ └── vf_cuda_ipc_export.c FFmpeg filter (C, ~300 строк) +├── libcuframes/ producer/consumer ядро +│ ├── include/cuframes.h публичный C API +│ ├── src/ +│ │ ├── producer.c ring buffer + Unix socket server +│ │ ├── consumer.c subscribe + cudaIpcOpenMemHandle +│ │ └── protocol.c wire format, версионирование +│ └── tests/ +│ ├── pingpong.c smoke test: 1 producer ↔ 1 consumer +│ ├── multi_consumer.c 1 → N +│ ├── stress.c 24h, leak detection +│ └── bench.c latency, throughput +├── bindings/ +│ ├── cpp/ C++ RAII wrapper +│ └── python/ pybind11 binding (numpy/torch interop) +├── examples/ +│ ├── ffmpeg-basic/ минимальный FFmpeg → consumer +│ ├── opencv-consumer/ cv::cuda::GpuMat из shared frame +│ ├── pytorch-consumer/ torch.Tensor zero-copy +│ ├── frigate-integration/ Frigate config + Docker compose +│ └── mosaic-compositor/ наш cctv-companion (как demo) +├── docker/ +│ ├── ffmpeg-cuframes/ FFmpeg с встроенным filter +│ └── ffmpeg-cuframes-frigate/ Drop-in replacement для Frigate's ffmpeg +├── docs/ +│ ├── architecture.md эта спека после редактирования для OSS +│ ├── protocol.md wire format +│ ├── quickstart.md 5-минутный пример +│ ├── building-ffmpeg.md как пересобрать FFmpeg с filter +│ ├── frigate-howto.md конкретно под Frigate +│ ├── benchmarks.md numbers +│ └── faq.md +├── scripts/ +│ ├── build-ffmpeg-patched.sh автоматизация upstream build +│ ├── package-deb.sh .deb для Ubuntu +│ └── ci/ +└── .github/ + └── workflows/ CI: build matrix Ubuntu 22.04/24.04, CUDA 12.x +``` + +### 2.2 Wire protocol (краткое) + +Producer: +- Аллоцирует ring buffer (default 4 slots) в VRAM через `cudaMalloc` +- На каждый decoded frame — пишет в next slot, делает `cudaStreamSynchronize` +- Публикует `seq` атомарно +- Сигналит consumers через eventfd + +Consumer: +- Connect к `/run/cuframes/.sock`, handshake (negotiate format, ring size) +- Mmap shared memory header `/dev/shm/cuframes.` +- Wait eventfd → read latest slot → `cudaIpcOpenMemHandle` → CUDA pointer +- Использует frame zero-copy → ACK через atomic bit в bitmap + +Backpressure: drop oldest (newest-wins). Multi-consumer независимы — медленный +не блокирует быстрых. + +Полный формат — в `docs/protocol.md`. Здесь — концепт. + +### 2.3 FFmpeg filter API + +``` +-vf "[any pipeline before],cuda_ipc_export=key=NAME[:ring=N][:sync_event=1],[pipeline after]" +``` + +Опции: +- `key` *(required)* — имя shared resource. Должно совпадать у producer и consumer. +- `ring` — глубина ring buffer (default 4) +- `sync_event` — использовать `cudaIpcEventHandle_t` вместо `cudaStreamSynchronize` + (zero-copy sync, но experimental) + +Filter: +- Принимает frame в CUDA hwframe-context +- Side-effect: публикует через `libcuframes` +- Passthrough — frame идёт дальше в pipeline без модификации (refcount++) + +Пример: + +```bash +ffmpeg -hwaccel cuda -hwaccel_output_format cuda \ + -i rtsp://camera/stream \ + -vf "scale_cuda=1920:1080,cuda_ipc_export=key=cam1" \ + -c:v copy -f segment recording.mp4 +``` + +В этой команде frame и публикуется через CUDA IPC, и пишется в архив. Один +decode для всего. + +### 2.4 Consumer API + +#### C +```c +#include + +cuframes_subscriber_t* sub = cuframes_subscribe("cam1", "my-app"); + +cuframes_frame_t frame; +while (cuframes_next(sub, &frame, /*timeout_ms=*/100) == 0) { + // frame.cuda_ptr — device pointer, zero-copy + // frame.width, height, pitch_y, pitch_uv, format + // frame.pts_ns + + do_inference_on_cuda(frame.cuda_ptr, frame.width, frame.height); + + cuframes_release(&frame); // ACK обратно producer'у +} +cuframes_unsubscribe(sub); +``` + +#### C++ (RAII) +```cpp +#include + +cuframes::Subscriber sub("cam1", "my-app"); + +while (auto frame = sub.next(std::chrono::milliseconds(100))) { + // frame->cuda_ptr — zero-copy + cv::cuda::GpuMat mat(frame->height, frame->width, CV_8UC2, // NV12 + frame->cuda_ptr, frame->pitch_y); + // ... use mat + // frame destructor → ACK +} +``` + +#### Python (numpy/torch interop) +```python +import cuframes + +with cuframes.Subscriber("cam1", "my-app") as sub: + for frame in sub: + # frame.tensor: torch.Tensor on CUDA, zero-copy + # frame.dlpack: __dlpack__ для numpy/torch/jax interop + output = model(frame.tensor) +``` + +### 2.5 Distribution и установка + +#### Уровень 1 — FFmpeg patch + +Минимальный патч к FFmpeg `libavfilter/`: +- `vf_cuda_ipc_export.c` (новый файл) +- `Makefile`: добавить filter +- `allfilters.c`: регистрация + +Это **один commit** к FFmpeg-репо. Возможен upstream PR (надеемся примут; +прецедент есть — Nvidia donated `scale_cuda` и др.). + +#### Уровень 2 — `libcuframes` runtime + +Stand-alone shared library. Зависимости: +- `libcuda.so.1` (NVIDIA driver, и так у всех) +- `cudart` (CUDA runtime) +- Linux только (POSIX SHM, eventfd, AF_UNIX) + +#### Уровень 3 — упаковка + +- **Debian/Ubuntu**: `.deb` через `dpkg-buildpackage` +- **Arch**: AUR package +- **Docker**: pre-built image `ghcr.io//cuframes-ffmpeg:N` — drop-in + замена `/usr/lib/ffmpeg/N/bin/ffmpeg` в Frigate image +- **Frigate-specific image**: `ghcr.io//cuframes-frigate:0.17` — + Frigate stable-tensorrt с pre-installed cuframes ffmpeg + +## 3. Use cases для документации + +### 3.1 Наш кейс — `cctv-companion` (mosaic) + +``` +Frigate (ffmpeg с cuda_ipc_export filter): + Camera 1..16 → decode CUDA → publish key=cam_N + ↘ свой Frigate pipeline (detect, record) + +cctv-companion: + - подписан на cam_1..cam_16 через cuframes + - подписан на frigate/events через MQTT + - decision logic: какую сетку показать (full/2x2/4x4) + - composite через CUDA-ядра (CUB / OpenCV CUDA / custom kernels) + - encode → RTSP → TV +``` + +### 3.2 Multi-AI на одном decode + +``` +ffmpeg decode → publish key=cam_main + +Subscriber A: YOLOv8 object detection +Subscriber B: face recognition (ArcFace) +Subscriber C: license plate OCR +Subscriber D: motion-triggered cloud upload +``` + +Все читают **тот же** decoded frame. Один NVDEC operation вместо четырёх. + +### 3.3 Замена RAM-based mmstreams + +Существующие custom-решения часто строят бypass через FIFO/pipe в RAM с +encoding между процессами. cuframes делает это zero-copy на CUDA. + +## 4. План работ + +### Phase 0 — Spike (3 дня) +- [ ] Прототип producer + consumer без FFmpeg: голый CUDA IPC + Unix socket +- [ ] Замерить bandwidth, latency на RTX 5090 +- [ ] Verify cross-container (docker → docker) и host → docker +- [ ] Решить: sync через `cudaStreamSynchronize` или CUDA IPC events +- **Deliverable:** `tools/spike/pingpong.cpp` + измерения + +### Phase 1 — libcuframes (1 неделя) +- [ ] Producer C API +- [ ] Consumer C API +- [ ] Wire-protocol implementation +- [ ] Unit tests (pingpong, multi-consumer, crash-recovery) +- [ ] Stress test rig (24h-runner) +- **Deliverable:** Self-contained library + tests passing + +### Phase 2 — FFmpeg filter (1-2 недели) +- [ ] `vf_cuda_ipc_export.c` в стиле FFmpeg +- [ ] Patch script для применения к FFmpeg N.x (4.4, 5.x, 6.x, 7.x) +- [ ] Build на CUDA 12.0+ matrix +- [ ] Integration test: `ffmpeg → cuframes filter → consumer` end-to-end +- **Deliverable:** Patched FFmpeg builds (Ubuntu 22.04, 24.04, in Docker) + +### Phase 3 — Bindings (1 неделя) +- [ ] C++ RAII wrapper +- [ ] Python binding через pybind11 +- [ ] PyTorch tensor interop (DLPack) +- **Deliverable:** `pip install cuframes` (через `pyproject.toml`) + +### Phase 4 — Docker / packaging (3-5 дней) +- [ ] `ghcr.io//cuframes-ffmpeg:N.x-cuda12.X` +- [ ] `ghcr.io//cuframes-frigate:0.17` (Frigate с patched ffmpeg) +- [ ] `.deb` packages для Ubuntu 22.04/24.04 +- [ ] CI matrix: build всех вариантов на каждый push +- **Deliverable:** `docker pull` ready-to-use + +### Phase 5 — Naш use case: cctv-companion (1 неделя) +- [ ] Минимальный C++ daemon +- [ ] Subscribe на N камер +- [ ] MQTT subscriber на Frigate events +- [ ] Smart-grid logic (existing в проекте, переиспользовать) +- [ ] Mosaic compositor на CUDA (cv::cuda::warpAffine для placement) +- [ ] Output через `nvenc` → RTSP сервер для TV +- **Deliverable:** Working setup на R9 со всеми 16 камерами + +### Phase 6 — OSS launch (3-5 дней) +- [ ] README с GIF (visual что это даёт) +- [ ] Quickstart (5 минут от `docker pull` до working demo) +- [ ] Benchmarks page с numbers +- [ ] HackerNews / r/selfhosted / r/homeassistant анонс +- [ ] Submit upstream PR в FFmpeg + +**Итого: ~6-8 недель** для одного разработчика, **3-4 недели** для двоих. + +## 5. Acceptance criteria для v1.0 + +- [ ] FFmpeg-filter работает на 4.4, 5.x, 6.x, 7.x (Frigate diversity) +- [ ] Latency producer→consumer ≤ 1 ms (p99) +- [ ] 16 cameras × 4 consumers × 25 fps без drops 24h +- [ ] Frigate (stable-tensorrt) drop-in replacement работает +- [ ] PyTorch consumer: `frame.tensor` без копий на CUDA +- [ ] Docker images < 4 GB сжатые +- [ ] CI green на Ubuntu 22.04, 24.04 +- [ ] `pip install cuframes` works на Python 3.10/3.11/3.12 +- [ ] README с benchmark vs naive double-decode + +## 6. Open questions для discussion + +1. **Name.** `cuframes` рабочий, варианты: `vramshare`, `cudapipe`, + `nvframe-bus`, `cuda-frame-bridge`, `framesink`. Финализируем перед public. + +2. **License.** LGPL-2.1+ для совместимости с FFmpeg LGPL и spirit (linkable + с MIT/Apache). Sourcing — на LGPL. + +3. **Upstream FFmpeg PR vs custom build.** Best-effort вклад в FFmpeg, но не + блокер — distrubution через own Docker images работает независимо. + +4. **AMD/ROCm support.** Out of scope для v1. После v1 — если будет + community-interest, добавить через HIP IPC. + +5. **Multi-GPU.** Producer и consumer на разных CUDA devices — CUDA IPC + ограничивает same device. В v1 — ограничение, документировать. + +6. **Sync semantics.** `cudaStreamSynchronize` гарантирует correctness но + serialize'ит producer. CUDA IPC events дают overlap, но complicate API. + Default: stream sync. Advanced flag: events. + +7. **Где хостить.** GitHub `/cuframes`. Возможно перенос в + `frigate-community/cuframes` после adoption. + +## 7. Риски + +| Риск | Митигация | +|---|---| +| Upstream FFmpeg PR не примут | Distributable через custom builds, не блокирует release | +| CUDA IPC subtle bugs | Phase 0 spike до commit'а; Stress test 24h | +| Frigate не пропустит custom filter | `frigate-cuframes` docker image — pre-patched, готов к использованию | +| Conflict с NVIDIA DeepStream APIs | None expected (разные scope) | +| Community не подхватит | Это secondary — primary value для нас (cctv-companion работает) | + +## 8. Зависимости (build-time) + +- CUDA Toolkit ≥ 12.0 +- FFmpeg sources (target versions 4.4, 5.x, 6.x, 7.x) +- pkg-config, autoconf, make +- Optional: OpenCV CUDA (для C++ examples) +- Optional: pybind11 (для Python binding) + +## 9. Связь с этим репозиторием (cctv) + +Текущий `cpp/apps/cctv-processor` — full-featured C++ NVR с своим decode + +mosaic + RTSP-server. После v1 cuframes: + +- `cuframes` выпускается **как отдельный** opensource-репо + (`/home/claude/projects/cuframes/`) +- В `cctv` создаётся новый `cpp/apps/cctv-companion/` — тонкий клиент к + Frigate + cuframes (не Replaces existing processor, а соседствует) +- Существующий cctv-processor — оставить как baseline / fallback для + setups без Frigate + +## 10. Готовность к next step + +После approval спеки: +1. `git init /home/claude/projects/cuframes/` — пустой OSS-репо +2. Phase 0 spike — 3 дня PoC +3. После Phase 0 — review результатов, корректировка дизайна (если CUDA IPC + повёл себя не как ожидали) +4. Phase 1+ по плану