Overview
Why ModelHub?
ModelHub provides a unified platform to discover, evaluate, and deploy machine learning models. Our documentation helps you integrate AI solutions seamlessly into your workflow.
1. Install SDK
npm install @modelhub/ai
2. Initialize Client
const client = new ModelHubClient({
apiKey: 'YOUR_API_KEY'
});
Getting Started
Step-by-Step Guide
-
1
Create an Account
Sign up at ModelHub Register to get your API key.
-
2
Install SDK
npm install @modelhub/ai -
3
Initialize Client
const client = new ModelHubClient({ apiKey: 'your_api_key_here' }); -
4
Use Pre-trained Models
// Available models const models = await client.listAvailableModels(); // Load model const model = await client.loadModel(models[0].id); // Inference const result = await model.predict(inputTensor);
API Documentation
Get Models
Retrieve a list of available AI models in the ModelHub database.
GET
/api/models
Query Parameters:
category
(string, optional)
sort
(string, default: relevance)
Response:
{ models: [Model[]], total: number }
Train Model
Submit data and configuration to start a model training job in the cloud.
POST
/api/training/jobs
Body:
"datasetId"
(required)
"modelConfig"
(required, JSON)
"computeClass"
(optional, default: standard)
Response:
{ jobId: string, status: string, created: ISO8601 }
Authentication
All API requests must include an Authorization: Bearer header with a valid API token.
Example Request
curl "https://api.modelhub.ai/models" \ -H "Authorization: Bearer YOUR_API_KEY"
Replace YOUR_API_KEY with your actual API token from your account dashboard
Where to Find Your API Key
-
1
Login to your account at ModelHub
-
2
Navigate to the API Keys section
-
3
Copy your token to clipboard and use in API requests
Best Practices
Model Lifecycle Management
- • Use version tags for model deployments
- • Monitor model performance in production
- • Implement rollback strategies
- • Schedule regular evaluation cycles
Performance Optimization
Model Serving
- • Use batch inference when applicable
- • Optimize input preprocessing pipelines
- • Monitor GPU utilization metrics
Training
- • Enable gradient checkpointing
- • Monitor validation metrics closely
- • Use learning rate schedules