- Added Jetson-specific deployment instructions to `deploy_scripts.md`, detailing prerequisites and service management.
- Updated `deploy_status_report.md` to reflect the completion of the AZ-180 cycle and the readiness of Jetson support.
- Removed outdated task documentation for Jetson Orin Nano support from the todo list.
Made-with: Cursor
- Dockerfile.jetson: JetPack 6.x L4T base image (aarch64), TensorRT and PyCUDA from apt
- requirements-jetson.txt: derived from requirements.txt, no pip tensorrt/pycuda
- docker-compose.jetson.yml: runtime: nvidia for NVIDIA Container Runtime
- tensorrt_engine.pyx: convert_from_source accepts optional calib_cache_path; INT8 used when cache present, FP16 fallback; get_engine_filename encodes precision suffix to avoid engine cache confusion
- inference.pyx: init_ai tries INT8 engine then FP16 on lookup; downloads calibration cache before conversion thread; passes cache path through to convert_from_source
- constants_inf: add INT8_CALIB_CACHE_FILE constant
- Unit tests for AC-3 (INT8 flag set when cache provided) and AC-4 (FP16 when no cache)
Made-with: Cursor
- Update autopilot state to step 14 (Deploy) with status in progress.
- Mark step 12 (Security Audit) and step 13 (Performance Test) as skipped due to previous cycle completion.
- Update deployment status report date to 2026-04-01 and add notes on the implementation and testing of the `POST /detect/video` endpoint.
- Emphasize the need to address security findings before production deployment.
Made-with: Cursor
- Added `/detect/video` endpoint for true streaming video detection, allowing inference to start as upload bytes arrive.
- Introduced `run_detect_video_stream` method in the inference module to handle video processing from a file-like object.
- Updated media hashing to include a new function for computing hashes directly from files with minimal I/O.
- Enhanced documentation to reflect changes in video processing and API behavior.
Made-with: Cursor
Oleksandr Bezdieniezhnykh