YunKan-Trainer (云瞰本地自训练) — an optional NVIDIA-GPU sidecar for YunKan that fine-tunes the on-device object-detection model on YOUR cameras' own footage, entirely on your LAN. It is a separate, single-purpose container: install it only when you want a detector that is more accurate for the specific people / vehicles / packages at your door, in your lighting.

How it works: in the YunKan web admin you confirm or correct the detector's pre-labels (active-learning). When you have enough samples you start a training job — the main YunKan container ships the dataset to this trainer over HTTP, the trainer fine-tunes with ultralytics on your NVIDIA GPU, exports an ONNX model, and hands it back. Nothing leaves your network.

This container is not a full app — it exposes a small HTTP API on port 28900 (a /health endpoint reports GPU / device / busy). It does the training; the main YunKan container drives it. You do not open a normal web UI here.

Requirements:

1. An NVIDIA GPU + the Unraid "NVIDIA Driver" plugin (reboot, confirm nvidia-smi works on the host). This template already sets --runtime=nvidia and the NVIDIA_* env vars — no extra GPU config needed.
2. The main YunKan container running on your LAN (any variant). In its Settings → Training page, enter this machine's trainer URL http://this-host-ip:28900 and the same TRAINER_TOKEN you set below.
3. The image is several GB (it bundles torch + ultralytics + the base weights), so the first pull takes a while.

Training is serial (one job at a time; the GPU is used exclusively while a job runs). Each job runs in a short-lived subprocess that fully releases GPU memory and RAM the moment it finishes — when idle the container just polls and sleeps, using almost nothing, so it can safely share a GPU with other workloads between jobs.

No NVIDIA GPU? You do not need this container — you can still fine-tune on on-demand cloud compute from within the YunKan admin.

Keywords: YunKan, 云瞰, self-training, fine-tuning, active learning, custom model, on-device AI, edge AI, object detection, NVIDIA, CUDA, GPU, ultralytics, YOLO, trainer, local training, privacy, self-hosted, NVR AI.

Install the Unraid "NVIDIA Driver" plugin so `nvidia-smi` works on the host, otherwise the trainer cannot use the GPU. Also install the main YunKan container (any variant) on your LAN and point its Settings → Training page at this trainer's URL + token.