mirror of
https://github.com/azaion/detections.git
synced 2026-04-22 21:46:31 +00:00
Oleksandr Bezdieniezhnykh
2149cd6c08
- 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
150 lines
4.5 KiB
Markdown
150 lines
4.5 KiB
Markdown
# Containerization Plan
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## Image Variants
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### detections-cpu (Dockerfile)
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| Aspect | Specification |
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|--------|--------------|
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| Base image | `python:3.11-slim` (pinned digest recommended) |
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| Build stages | Single stage (Cython compile requires gcc at runtime for setup.py) |
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| Non-root user | `adduser --disabled-password --gecos '' appuser` + `USER appuser` |
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| Health check | `HEALTHCHECK --interval=30s --timeout=5s CMD curl -f http://localhost:8080/health \|\| exit 1` |
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| Exposed ports | 8080 |
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| Entrypoint | `uvicorn main:app --host 0.0.0.0 --port 8080` |
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**Changes needed to existing Dockerfile**:
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1. Add non-root user (security finding F7)
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2. Add HEALTHCHECK directive
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3. Pin `python:3.11-slim` to specific digest
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4. Add `curl` to apt-get install (for health check)
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### detections-gpu (Dockerfile.gpu)
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| Aspect | Specification |
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|--------|--------------|
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| Base image | `nvidia/cuda:12.2.0-runtime-ubuntu22.04` |
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| Build stages | Single stage |
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| Non-root user | `adduser --disabled-password --gecos '' appuser` + `USER appuser` |
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| Health check | `HEALTHCHECK --interval=30s --timeout=5s CMD curl -f http://localhost:8080/health \|\| exit 1` |
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| Exposed ports | 8080 |
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| Entrypoint | `uvicorn main:app --host 0.0.0.0 --port 8080` |
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| Runtime | Requires `--runtime=nvidia` or `nvidia` runtime in Docker |
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**Changes needed to existing Dockerfile.gpu**:
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1. Add non-root user
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2. Add HEALTHCHECK directive
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3. Add `curl` to apt-get install
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### .dockerignore
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```
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.git
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.gitignore
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_docs/
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_standalone/
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e2e/
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tests/
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*.md
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.env
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.env.*
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.cursor/
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.venv/
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venv/
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__pycache__/
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*.pyc
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build/
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dist/
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*.egg-info
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Logs/
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```
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## Docker Compose — Local Development
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`docker-compose.yml` (already partially exists as `e2e/docker-compose.mocks.yml`):
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```yaml
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name: detections-dev
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services:
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mock-loader:
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build: ./e2e/mocks/loader
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ports:
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- "18080:8080"
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volumes:
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- ./e2e/fixtures:/models
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networks:
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- dev-net
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mock-annotations:
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build: ./e2e/mocks/annotations
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ports:
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- "18081:8081"
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networks:
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- dev-net
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detections:
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build:
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context: .
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dockerfile: Dockerfile
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ports:
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- "8080:8080"
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depends_on:
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- mock-loader
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- mock-annotations
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env_file: .env
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environment:
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LOADER_URL: http://mock-loader:8080
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ANNOTATIONS_URL: http://mock-annotations:8081
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volumes:
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- ./e2e/fixtures/classes.json:/app/classes.json:ro
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- detections-logs:/app/Logs
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shm_size: 512m
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networks:
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- dev-net
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volumes:
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detections-logs:
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networks:
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dev-net:
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driver: bridge
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```
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## Docker Compose — Blackbox Tests
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Already exists: `e2e/docker-compose.test.yml`. No changes needed — supports both `cpu` and `gpu` profiles with mock services and test runner.
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### detections-jetson (Dockerfile.jetson)
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| Aspect | Specification |
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|--------|--------------|
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| Base image | `nvcr.io/nvidia/l4t-base:r36.3.0` (JetPack 6.x, aarch64) |
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| TensorRT | Pre-installed via JetPack — `python3-libnvinfer` apt package (NOT pip) |
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| PyCUDA | Pre-installed via JetPack — `python3-pycuda` apt package (NOT pip) |
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| Build stages | Single stage (Cython compile requires gcc) |
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| Non-root user | `adduser --disabled-password --gecos '' appuser` + `USER appuser` |
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| Exposed ports | 8080 |
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| Entrypoint | `uvicorn main:app --host 0.0.0.0 --port 8080` |
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| Runtime | Requires NVIDIA Container Runtime (`runtime: nvidia` in docker-compose) |
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**Jetson-specific behaviour**:
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- `requirements-jetson.txt` derives from `requirements.txt` — `tensorrt` and `pycuda` are excluded from pip and provided by JetPack
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- Engine filename auto-encodes CC+SM (e.g. `azaion.cc_8.7_sm_16.engine` for Orin Nano), ensuring the Jetson engine is distinct from any x86-cached engine
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- INT8 is used when `azaion.int8_calib.cache` is available on the Loader service; precision suffix appended to engine filename (`*.int8.engine`); FP16 fallback when cache is absent
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- `docker-compose.jetson.yml` uses `runtime: nvidia` for the NVIDIA Container Runtime
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**Compose usage on Jetson**:
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```bash
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docker compose -f docker-compose.jetson.yml up
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```
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## Image Tagging Strategy
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| Context | Tag Format | Example |
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|---------|------------|---------|
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| CI builds | `<registry>/azaion/detections-cpu:<git-sha>` | `registry.example.com/azaion/detections-cpu:a1b2c3d` |
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| CI builds (GPU) | `<registry>/azaion/detections-gpu:<git-sha>` | `registry.example.com/azaion/detections-gpu:a1b2c3d` |
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| Local development | `detections-cpu:dev` | — |
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| Latest stable | `<registry>/azaion/detections-cpu:latest` | Updated on merge to main |
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