206 lines
7.1 KiB
Markdown
Executable File
206 lines
7.1 KiB
Markdown
Executable File
---
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comments: true
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description: Learn about model deployment options in Ultralytics Platform including inference testing, dedicated endpoints, and monitoring dashboards.
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keywords: Ultralytics Platform, deployment, inference, endpoints, monitoring, YOLO, production, cloud deployment
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---
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# Deployment
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[Ultralytics Platform](https://platform.ultralytics.com) provides comprehensive deployment options for putting your YOLO models into production. Test models with browser-based inference, deploy to dedicated endpoints across 43 global regions, and monitor performance in real-time.
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## Overview
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The Deployment section helps you:
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- **Test** models directly in the browser with the `Predict` tab
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- **Deploy** to dedicated endpoints in 43 global regions
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- **Monitor** request metrics, logs, and health checks
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- **Scale** automatically with traffic (including scale-to-zero)
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## Deployment Options
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Ultralytics Platform offers multiple deployment paths:
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| Option | Description | Best For |
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| --------------------------------------- | -------------------------------------------------------- | ----------------------- |
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| **[Predict Tab](inference.md)** | Browser-based inference with image, webcam, and examples | Development, validation |
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| **Shared Inference** | Multi-tenant service across 3 regions | Light usage, testing |
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| **[Dedicated Endpoints](endpoints.md)** | Single-tenant services across 43 regions | Production, low latency |
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## Workflow
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```mermaid
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graph LR
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A[✅ Test] --> B[⚙️ Configure]
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B --> C[🌐 Deploy]
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C --> D[📊 Monitor]
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style A fill:#4CAF50,color:#fff
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style B fill:#2196F3,color:#fff
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style C fill:#FF9800,color:#fff
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style D fill:#9C27B0,color:#fff
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```
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| Stage | Description |
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| ------------- | ------------------------------------------------------------------------ |
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| **Test** | Validate model with the [`Predict` tab](inference.md) |
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| **Configure** | Select region, resources, and deployment name |
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| **Deploy** | Create a dedicated endpoint from the [`Deploy` tab](endpoints.md) |
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| **Monitor** | Track requests, latency, errors, and logs in [Monitoring](monitoring.md) |
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## Architecture
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### Shared Inference
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The shared inference service runs in 3 key regions, automatically routing requests based on your data region:
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```mermaid
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graph TB
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User[User Request] --> API[Platform API]
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API --> Router{Region Router}
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Router -->|US users| US["US Predict Service<br/>Iowa"]
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Router -->|EU users| EU["EU Predict Service<br/>Belgium"]
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Router -->|AP users| AP["AP Predict Service<br/>Hong Kong"]
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style User fill:#f5f5f5,color:#333
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style API fill:#2196F3,color:#fff
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style Router fill:#FF9800,color:#fff
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style US fill:#4CAF50,color:#fff
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style EU fill:#4CAF50,color:#fff
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style AP fill:#4CAF50,color:#fff
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```
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| Region | Location |
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| ------ | ----------------------- |
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| US | Iowa, USA |
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| EU | Belgium, Europe |
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| AP | Hong Kong, Asia-Pacific |
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### Dedicated Endpoints
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Deploy to 43 regions worldwide on Ultralytics Cloud:
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- **Americas**: 14 regions
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- **Europe**: 13 regions
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- **Asia-Pacific**: 12 regions
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- **Middle East & Africa**: 4 regions
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Each endpoint is a single-tenant service with:
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- Dedicated compute resources (configurable CPU and memory)
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- Auto-scaling (scale-to-zero when idle)
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- Unique endpoint URL
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- Independent monitoring, logs, and health checks
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## Deployments Page
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Access the global deployments page from the sidebar under `Deploy`. This page shows:
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- **World map** with deployed region pins (interactive map)
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- **Overview cards**: Total Requests (24h), Active Deployments, Error Rate (24h), P95 Latency (24h)
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- **Deployments list** with three view modes: cards, compact, and table
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- **New Deployment** button to create endpoints from any completed model
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!!! info "Automatic Polling"
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The page polls every 30 seconds for metric updates. When deployments are in a transitional state (creating, deploying, stopping), polling increases to every 2-3 seconds for near-instant feedback.
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## Key Features
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### Global Coverage
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Deploy close to your users with 43 regions covering:
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- North America, South America
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- Europe, Middle East, Africa
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- Asia Pacific, Oceania
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### Auto-Scaling
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Endpoints scale automatically:
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- **Scale to zero**: No cost when idle (default)
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- **Scale up**: Handle traffic spikes automatically
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!!! tip "Cost Savings"
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Scale-to-zero is enabled by default (min instances = 0). You only pay for active inference time.
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### Low Latency
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Dedicated endpoints provide:
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- Cold start: ~5-15 seconds (cached container), up to ~45 seconds (first deploy)
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- Warm inference: 50-200ms (model dependent)
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- Regional routing for optimal performance
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### Health Checks
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Each running deployment includes an automatic health check with:
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- Live status indicator (healthy/unhealthy)
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- Response latency display
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- Auto-retry when unhealthy (polls every 20 seconds)
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- Manual refresh button
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## Quick Start
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Deploy a model in under 2 minutes:
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1. Train or upload a model to a project
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2. Go to the model's **Deploy** tab
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3. Select a region from the latency table
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4. Click **Deploy** — your endpoint is live
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!!! example "Quick Deploy"
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```
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Model → Deploy tab → Select region → Click Deploy → Endpoint URL ready
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```
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Once deployed, use the endpoint URL with your API key to send inference requests from any application.
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## Quick Links
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- [**Inference**](inference.md): Test models in browser
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- [**Endpoints**](endpoints.md): Deploy dedicated endpoints
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- [**Monitoring**](monitoring.md): Track deployment performance
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## FAQ
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### What's the difference between shared and dedicated inference?
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| Feature | Shared | Dedicated |
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| ----------- | --------------- | -------------- |
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| **Latency** | Variable | Consistent |
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| **Cost** | Pay per request | Pay for uptime |
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| **Scale** | Limited | Configurable |
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| **Regions** | 3 | 43 |
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| **URL** | Generic | Custom |
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### How long does deployment take?
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Dedicated endpoint deployment typically takes 1-2 minutes:
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1. Image pull (~30s)
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2. Container start (~30s)
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3. Health check (~30s)
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### Can I deploy multiple models?
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Yes, each model can have multiple endpoints in different regions. There's no limit on total endpoints (subject to your plan).
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### What happens when an endpoint is idle?
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With scale-to-zero enabled:
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- Endpoint scales down after inactivity
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- First request triggers cold start
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- Subsequent requests are fast
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First requests after an idle period trigger a cold start.
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