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Documentation Index

Fetch the complete documentation index at: https://docs.cyberwave.com/llms.txt

Use this file to discover all available pages before exploring further.

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What are ML Models?

ML Models in Cyberwave are AI models registered in your workspace that can process various inputs—video, images, audio, text, or robot actions. They integrate with workflows and can run in the cloud or on edge devices.
ML Models define what the model can do (input types) and where it runs (provider). The actual inference happens through the model provider’s API or on your edge device.

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Model Capabilities

Each ML Model specifies what inputs it can process:
CapabilityDescriptionExample Use Cases
can_take_video_as_inputProcess video streamsSurveillance, teleoperation
can_take_image_as_inputProcess single imagesQuality inspection, object detection
can_take_audio_as_inputProcess audio dataVoice commands, anomaly detection
can_take_text_as_inputProcess text promptsNatural language commands
can_take_action_as_inputProcess robot actionsBehavior cloning, RL policies

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Registering a Model

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Via the SDK

from cyberwave import Cyberwave

client = Cyberwave()

# Register a vision model
model = client.api.create_mlmodel({
    "name": "Object Detector",
    "description": "YOLOv8 model for detecting warehouse items",
    "model_external_id": "yolov8n",
    "model_provider_name": "local",
    "can_take_image_as_input": True,
    "can_take_video_as_input": True,
    "visibility": "private",
    "tags": ["vision", "detection", "warehouse"]
})

print(f"Model UUID: {model.uuid}")

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Via the Dashboard

  1. Navigate to ML Models in your workspace
  2. Click Add Model
  3. Fill in the model details:
    • Name and description
    • External ID (model identifier for the provider)
    • Provider name (e.g., “openai”, “local”, “huggingface”)
    • Input capabilities
  4. Click Create

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Model Providers

Models can run through different providers:

Local / Edge

Run on your edge devices using ONNX, TensorRT, or custom inference

Cloud APIs

Use OpenAI, Anthropic, or other cloud AI services

Hugging Face

Deploy models from Hugging Face Hub

Custom

Your own inference servers and endpoints

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Using Models in Workflows

ML Models integrate with workflow nodes for automated processing:

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Example: Vision-Language Model

Register a VLM for natural language robot control: 
from cyberwave import Cyberwave

client = Cyberwave()

# Register GPT-4 Vision for robot commands
vlm = client.api.create_mlmodel({
    "name": "Robot Commander",
    "description": "Interprets visual scenes and generates robot commands",
    "model_external_id": "gpt-4-vision-preview",
    "model_provider_name": "openai",
    "can_take_image_as_input": True,
    "can_take_text_as_input": True,
    "visibility": "private",
    "metadata": {
        "max_tokens": 1000,
        "temperature": 0.2
    }
})

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Listing Models

from cyberwave import Cyberwave

client = Cyberwave()

# List all models in your workspace
models = client.api.list_mlmodels()

for model in models:
    print(f"{model.name} ({model.model_provider_name})")
    print(f"  Video: {model.can_take_video_as_input}")
    print(f"  Image: {model.can_take_image_as_input}")
    print(f"  Text: {model.can_take_text_as_input}")

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Model Visibility

VisibilityWho Can Access
privateOnly your workspace members
workspaceAll workspace members
publicAnyone (admin-only to create)

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Running Inference

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Cloud Models

For cloud-based models, Cyberwave routes requests to the provider: 
# Via workflow nodes or API
response = client.api.vlm_generation({
    "model_uuid": vlm.uuid,
    "prompt": "What objects do you see? How should the robot pick them up?",
    "image_url": "https://..."
})

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Edge Models

For local models, run inference on your edge device: 
import asyncio
from cyberwave import Cyberwave

async def run_local_inference():
    client = Cyberwave()
    
    # Your local model
    import torch
    model = torch.hub.load('ultralytics/yolov8', 'yolov8n')
    
    twin_uuid = "your-twin-uuid"
    
    def on_frame(frame_data):
        """Process each incoming frame."""
        # Run detection
        results = model(frame_data)
        
        # Publish results back to Cyberwave
        detections = results.pandas().xyxy[0].to_dict()
        client.mqtt.publish(
            f"twins/{twin_uuid}/detections",
            detections
        )
    
    # Subscribe to video frames
    client.mqtt.subscribe(
        f"twins/{twin_uuid}/video/frames",
        on_frame
    )
    
    while True:
        await asyncio.sleep(1)

asyncio.run(run_local_inference())

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Next Steps

Workflows

Use ML Models in automated workflows

Edge Devices

Run models on edge hardware