---
comments: true
description: Learn about model training in Ultralytics Platform including project organization, cloud training, and real-time metrics streaming.
keywords: Ultralytics Platform, model training, cloud training, YOLO, GPU training, machine learning, deep learning
---

# Model Training

[Ultralytics Platform](https://platform.ultralytics.com) provides comprehensive tools for training YOLO models, from organizing experiments to running cloud training jobs with real-time metrics streaming.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/bajkq0NrSN8"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Get Started with Ultralytics Platform - Train
</p>

## Overview

The Training section helps you:

- **Organize** models into [projects](projects.md) 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](models.md#supported-formats))

![Ultralytics Platform Train Overview](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/platform-train-overview.avif)

## Workflow

```mermaid
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
```

| Stage         | Description                                                                |
| ------------- | -------------------------------------------------------------------------- |
| **Project**   | Create a workspace to organize related models                              |
| **Configure** | Select [dataset](../data/datasets.md), base model, and training parameters |
| **Train**     | Run on cloud GPUs or your local hardware                                   |
| **Monitor**   | View real-time loss curves and metrics                                     |
| **Export**    | Convert to 17+ deployment formats ([details](models.md#supported-formats)) |

## Training Options

Ultralytics Platform supports multiple training approaches:

| Method                                                  | Description                                   | Best For                   |
| ------------------------------------------------------- | --------------------------------------------- | -------------------------- |
| **[Cloud Training](cloud-training.md)**                 | Train on Ultralytics Cloud GPUs               | No local GPU, scalability  |
| **[Local Training](cloud-training.md#remote-training)** | Train locally, stream metrics to the platform | Existing hardware, privacy |
| **[Colab Training](cloud-training.md#remote-training)** | Use Google Colab with platform integration    | Free GPU access            |

## 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:

```mermaid
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
```

| Subtab      | Metrics                                                |
| ----------- | ------------------------------------------------------ |
| **Charts**  | Box/class/DFL loss, mAP50, mAP50-95, precision, recall |
| **Console** | Live training logs with ANSI color and error detection |
| **System**  | GPU utilization, memory, temperature, CPU, disk        |

!!! 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**](projects.md): Organize your models and experiments
- [**Models**](models.md): Manage trained checkpoints
- [**Cloud Training**](cloud-training.md): 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](cloud-training.md#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?

| Scenario                      | Recommended GPU         |
| ----------------------------- | ----------------------- |
| Most training jobs            | RTX PRO 6000            |
| Large datasets or batch sizes | H100 SXM or H200 (Pro+) |
| Budget-conscious              | RTX 4090                |
