Model Training

Ultralytics Platform provides comprehensive tools for training YOLO models, from organizing experiments to running cloud training jobs with real-time metrics streaming.

<strong>Watch:</strong> Get Started with Ultralytics Platform - Train

Overview

The Training section helps you:

• Organize models into projects for easier management
• Train on cloud GPUs with a single click
• Monitor real-time metrics during training
• Compare model performance across experiments
• Export to 17+ deployment formats (see supported formats)

Workflow

graph LR
    A[📁 Project] --> B[⚙️ Configure]
    B --> C[🚀 Train]
    C --> D[📈 Monitor]
    D --> E[📦 Export]

    style A fill:#4CAF50,color:#fff
    style B fill:#2196F3,color:#fff
    style C fill:#FF9800,color:#fff
    style D fill:#9C27B0,color:#fff
    style E fill:#00BCD4,color:#fff

Training Options

Ultralytics Platform supports multiple training approaches:

GPU Options

Available GPUs for cloud training on Ultralytics Cloud:

{% include "macros/platform-gpu-table.md" %}

!!! info "GPU Tier Access"

H200 and B200 GPUs require a [Pro or Enterprise plan](../account/billing.md#plans). All other GPUs are available on all plans including Free.

!!! tip "Signup Credits"

New accounts receive signup credits for training. Check [Billing](../account/billing.md) for details.

Real-Time Metrics

During training, view live metrics across three subtabs:

graph LR
    A[Charts] --> B[Loss Curves]
    A --> C[Performance Metrics]
    D[Console] --> E[Live Logs]
    D --> F[Error Detection]
    G[System] --> H[GPU Utilization]
    G --> I[Memory & Temp]

    style A fill:#2196F3,color:#fff
    style D fill:#FF9800,color:#fff
    style G fill:#9C27B0,color:#fff

!!! info "Automatic Checkpoints"

For cloud training, the **best model** (`best.pt`, the highest-mAP checkpoint) is saved automatically and made available for download, export, and deployment after training completes.

Quick Start

Get started with cloud training in under a minute:

=== "Cloud (UI)"

1. Create a project in the sidebar
2. Click **New Model**
3. Select a model, dataset, and GPU
4. Click **Start Training**

=== "Remote (CLI)"

```bash
export ULTRALYTICS_API_KEY="YOUR_API_KEY"
yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset \
  epochs=100 project=username/my-project name=exp1
```

=== "Remote (Python)"

```python
from ultralytics import YOLO

model = YOLO("yolo26n.pt")
model.train(
    data="ul://username/datasets/my-dataset",
    epochs=100,
    project="username/my-project",
    name="exp1",
)
```

Quick Links

• Projects: Organize your models and experiments
• Models: Manage trained checkpoints
• Cloud Training: Train on cloud GPUs

FAQ

How long does training take?

Training time depends on:

• Dataset size (number of images)
• Model size (n, s, m, l, x)
• Number of epochs
• GPU type selected

A typical training run with 1000 images, YOLO26n, 100 epochs on RTX PRO 6000 takes about 2-3 hours. Smaller runs (500 images, 50 epochs on RTX 4090) complete in under an hour. See cost examples for detailed estimates.

Can I train multiple models simultaneously?

Yes. Concurrent cloud training limits depend on your plan: Free allows 3, Pro allows 10, and Enterprise is unlimited. For additional parallel training, use remote training from multiple machines.

What happens if training fails?

If training fails:

1. Checkpoints are saved at each epoch
2. You can resume from the last checkpoint
3. Credits are only charged for completed compute time

How do I choose the right GPU?