# x86 Docker Migration Design

**Date:** 2026-04-08

**Goal:** Package the RTSP people-flow project for direct use on an Ubuntu 24.04 x86_64 host with an NVIDIA RTX 3080 by using Docker, bundled project files, and host-side model weights.

## Scope

- Target host: `xiaozheng@192.168.5.154`
- Target path: `/home/x/people`
- Runtime model: Docker with NVIDIA runtime
- Input source: RTSP
- Output: JSON window summaries under a mounted host directory
- Include required model weights on the target host

## Why Docker

The existing remote runtime was built on Jetson ARM64 and cannot be reused on an x86_64 RTX 3080 machine. The target host only has Python 3.12 installed, and a native port would need additional interpreter and CUDA-specific package work. Docker is the most reliable path because it isolates Python dependencies, preserves a reproducible runtime, and matches the user requirement of direct use on a new CUDA-capable machine.

## Packaging Strategy

### Host Layout

The target host will contain:

- `/home/x/people/people_flow_project/`
- `/home/x/people/people_flow_project/weights/yolo11n.pt`
- `/home/x/people/people_flow_project/weights/deepface/age_model_weights.h5`
- `/home/x/people/people_flow_project/weights/deepface/gender_model_weights.h5`
- `/home/x/people/people_flow_project/weights/deepface/retinaface.h5`
- `/home/x/people/output/`

### Container Layout

The container will:

- run on Python 3.12
- install GPU-enabled PyTorch wheels
- install the application dependencies
- read YOLO and DeepFace weights from deterministic in-container paths
- write outputs to a mounted host output directory

The project source will be copied into the image at build time. The host-side `weights/` directory will also be part of the build context so the final image does not need to download weights on first start.

## Runtime Contract

The image is intended to be built once on the target host and then started with a single `docker run` command using `--gpus all`.

The container command will remain the existing CLI:

`python main.py --config ... --output-dir ... --device cuda:0 rtsp --input ...`

## System Adaptation

The target host already has:

- Ubuntu 24.04
- Docker installed
- NVIDIA runtime registered in Docker

The adaptation work is therefore limited to:

- adding the project’s Docker packaging files
- transferring project code and model weights
- building the image on the target host
- validating the container entrypoint and GPU runtime path

## Risks

- The target GPU is currently heavily occupied by another process, so a full inference validation may need to avoid competing for memory.
- DeepFace and TensorFlow increase image size and build time.
- Network access is required during image build unless a wheel cache is prepared separately.

## Success Criteria

- The target host contains the project and all required weights under `/home/x/people`
- `docker build` completes successfully
- The container can run `main.py rtsp --help`
- The final run command is documented for direct RTSP use