AI Lab — Rockchip 版エッジ AI モデルハブ — デプロイガイド

プリセット

Object Detection

Deploy YOLO 11 object detection to a reComputer RK3576 or RK3588, with an image detection REST API and an MJPEG live video feed covering the 80 COCO object classes.

Devices: reComputer RK3576 or RK3588; a USB camera for live video detection (optional).
Software: Docker installed on the device, reachable over SSH.
Models: choose Nano, Small or Medium at deployment.

Deploy YOLO 11

Deploy the object detection container to the device.

デプロイ方法

デバイス

Deploy to the RK3576 over SSH.

配線

Connect the RK3576 to the same network as your computer
Plug in a USB camera for live video detection
Select the model size (start with Nano)
Fill in device IP, SSH username, and password
Click Deploy

入力が必要な項目（SenseCraft Solution アプリで入力）

Model*既定値 yolo11n
3 件の選択肢
- · YOLO 11n (Nano - fastest, ~5MB)
- · YOLO 11s (Small - balanced, ~10MB)
- · YOLO 11m (Medium - most accurate, ~20MB)

トラブルシューティング

Symptom	Fix
SSH connection failed	Verify IP address, username, password
NPU not detected	Ensure device is RK3576 with RKNPU kernel module loaded
No camera detected	Check USB camera is connected. Detection still works with image upload API
Image pull slow	Check the network. Image is about 1-2GB

デプロイ後

Detection API: http://<device-ip>:8000/api/models/yolo11/predict
Live video feed: http://<device-ip>:8000/api/video_feed (requires camera)

Try Detection

Verify the detection service is working.

モードモード

Upload an image to test object detection.

トラブルシューティング

Symptom	Fix
No detections	Use an image with people or vehicles
Connection refused	Wait 15-30 seconds for service to start

プリセット

LLM Chat

Deploy the DeepSeek-R1 large language model to a reComputer RK3576, with an OpenAI-compatible chat API running on the device.

Devices: reComputer RK3576; 7B variants need 8GB+ RAM.
Software: Docker installed on the device, reachable over SSH.
Models: choose from 5 variants (1.5B/7B, different quantizations) at deployment.

Deploy DeepSeek-R1

Deploy the LLM container to the RK3576.

デプロイ方法

Deploy to the RK3576 over SSH.

配線

Connect the RK3576 to the same network as your computer
Select the model variant
Fill in device IP, SSH username, and password
Click Deploy

入力が必要な項目（SenseCraft Solution アプリで入力）

Model*既定値 1.5b-w4a16-latest
5 件の選択肢
- · DeepSeek-R1 1.5B W4A16 (1.2GB, recommended)
- · DeepSeek-R1 1.5B FP16 (2.7GB, highest accuracy)
- · DeepSeek-R1 1.5B W4A16-G128 (1.2GB, most compressed)
- · DeepSeek-R1 7B W4A16 (4.1GB, stronger reasoning)
- · DeepSeek-R1 7B W4A16-G128 (4.1GB, compressed)

トラブルシューティング

Symptom	Fix
SSH connection failed	Verify IP address, username, password
NPU not detected	Ensure device is RK3576 with RKNPU kernel module loaded
Out of memory (7B model)	7B variants require 8GB+ RAM. Try a 1.5B variant instead
Image pull slow	Check the network. Image size is 1-4GB depending on variant

デプロイ後

Chat API: http://<device-ip>:8001/v1/chat/completions. Connect with any OpenAI-compatible client.

Try Chat

Test the LLM by sending a message.

トラブルシューティング

Symptom	Fix
Connection refused	Wait 30-60 seconds for model to load
Timeout	7B models take longer. Wait up to 2 minutes
Empty response	Check container logs: `docker logs ai_lab_llm`

デプロイ後

Object Detection — Image Upload

curl -X POST http://<device-ip>:8000/api/models/yolo11/predict \
  -F "file=@photo.jpg" \
  -F "conf=0.5"

Object Detection — Live Video Feed

Open in browser: http://<device-ip>:8000/api/video_feed

LLM Chat — Quick Call

curl http://<device-ip>:8001/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "rkllm-model", "messages": [{"role": "user", "content": "Hello!"}], "max_tokens": 256}'

Vision Chat — Image + Question

curl -X POST http://<device-ip>:8002/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "rkllm-vision",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "What is in this image?"},
        {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}}
      ]
    }],
    "max_tokens": 256
  }'

Python (OpenAI client) — works for LLM Chat and Vision Chat

import openai
client = openai.OpenAI(base_url="http://<device-ip>:8001/v1", api_key="dummy")
response = client.chat.completions.create(
    model="rkllm-model",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=256
)
print(response.choices[0].message.content)

プリセット

Vision Chat

Deploy the Qwen2.5-VL vision-language model to a reComputer RK3576, with an OpenAI-compatible vision API on the device for image captioning and visual Q&A.

Devices: reComputer RK3576 with 8GB+ RAM.
Software: Docker installed on the device, reachable over SSH.

Deploy Qwen2.5-VL

Deploy the vision-language model container to the RK3576.

デプロイ方法

Deploy to the RK3576 over SSH.

配線

Connect the RK3576 to the same network as your computer
Fill in device IP, SSH username, and password
Click Deploy

トラブルシューティング

Symptom	Fix
SSH connection failed	Verify IP address, username, password
NPU not detected	Ensure device is RK3576 with RKNPU kernel module loaded
Out of memory	VLM requires 8GB+ RAM. Close other services to free memory
Image pull slow	Check the network. Image is about 3GB

デプロイ後

Vision chat API: http://<device-ip>:8002/v1/chat/completions
API docs: http://<device-ip>:8002/docs

Try Vision Chat

Test the VLM by sending an image or text.

モードモード

Upload an image and ask a question about it.

トラブルシューティング

Symptom	Fix
Connection refused	Wait 60-120 seconds for model to load
Timeout	VLM model is large, initial load takes time