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

3.0 KiB Raw Blame History Unescape Escape