189 lines
7.4 KiB
Markdown
Executable File
189 lines
7.4 KiB
Markdown
Executable File
---
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comments: true
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description: Learn about model training in Ultralytics Platform including project organization, cloud training, and real-time metrics streaming.
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keywords: Ultralytics Platform, model training, cloud training, YOLO, GPU training, machine learning, deep learning
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---
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# Model Training
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[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.
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## Overview
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The Training section helps you:
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- **Organize** models into [projects](projects.md) for easier management
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- **Train** on cloud GPUs with a single click
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- **Monitor** real-time metrics during training
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- **Compare** model performance across experiments
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- **Export** to 17+ deployment formats (see [supported formats](models.md#supported-formats))
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## Workflow
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```mermaid
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graph LR
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A[📁 Project] --> B[⚙️ Configure]
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B --> C[🚀 Train]
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C --> D[📈 Monitor]
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D --> E[📦 Export]
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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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style E fill:#00BCD4,color:#fff
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```
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| Stage | Description |
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| ------------- | -------------------------------------------------------------------------- |
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| **Project** | Create a workspace to organize related models |
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| **Configure** | Select [dataset](../data/datasets.md), base model, and training parameters |
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| **Train** | Run on cloud GPUs or your local hardware |
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| **Monitor** | View real-time loss curves and metrics |
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| **Export** | Convert to 17+ deployment formats ([details](models.md#supported-formats)) |
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## Training Options
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Ultralytics Platform supports multiple training approaches:
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| Method | Description | Best For |
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| ------------------------------------------------------- | --------------------------------------------- | -------------------------- |
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| **[Cloud Training](cloud-training.md)** | Train on Ultralytics Cloud GPUs | No local GPU, scalability |
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| **[Local Training](cloud-training.md#remote-training)** | Train locally, stream metrics to the platform | Existing hardware, privacy |
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| **[Colab Training](cloud-training.md#remote-training)** | Use Google Colab with platform integration | Free GPU access |
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## GPU Options
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Available GPUs for cloud training on Ultralytics Cloud:
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| GPU | VRAM | Cost/Hour | Best For |
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| ------------ | ------ | --------- | ------------------------- |
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| RTX 2000 Ada | 16 GB | $0.24 | Small datasets, testing |
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| RTX A4500 | 20 GB | $0.24 | Small-medium datasets |
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| RTX A5000 | 24 GB | $0.26 | Medium datasets |
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| RTX 4000 Ada | 20 GB | $0.38 | Medium datasets |
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| L4 | 24 GB | $0.39 | Inference optimized |
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| A40 | 48 GB | $0.40 | Larger batch sizes |
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| RTX 3090 | 24 GB | $0.46 | Great price/performance |
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| RTX A6000 | 48 GB | $0.49 | Large models |
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| RTX 4090 | 24 GB | $0.59 | Best price/performance |
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| RTX 6000 Ada | 48 GB | $0.77 | Large batch training |
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| L40S | 48 GB | $0.86 | Large batch training |
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| RTX 5090 | 32 GB | $0.89 | Latest generation |
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| L40 | 48 GB | $0.99 | Large models |
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| A100 PCIe | 80 GB | $1.39 | Production training |
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| A100 SXM | 80 GB | $1.49 | Production training |
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| RTX PRO 6000 | 96 GB | $1.89 | Recommended default |
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| H100 PCIe | 80 GB | $2.39 | High-performance training |
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| H100 SXM | 80 GB | $2.69 | Fastest training |
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| H100 NVL | 94 GB | $3.07 | Maximum performance |
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| H200 NVL | 143 GB | $3.39 | Maximum memory |
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| H200 SXM | 141 GB | $3.59 | Maximum performance |
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| B200 | 180 GB | $4.99 | Largest models |
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!!! tip "Signup Credits"
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New accounts receive signup credits for training. Check [Billing](../account/billing.md) for details.
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## Real-Time Metrics
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During training, view live metrics across three subtabs:
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```mermaid
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graph LR
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A[Charts] --> B[Loss Curves]
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A --> C[Performance Metrics]
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D[Console] --> E[Live Logs]
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D --> F[Error Detection]
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G[System] --> H[GPU Utilization]
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G --> I[Memory & Temp]
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style A fill:#2196F3,color:#fff
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style D fill:#FF9800,color:#fff
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style G fill:#9C27B0,color:#fff
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```
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| Subtab | Metrics |
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| ----------- | ------------------------------------------------------ |
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| **Charts** | Box/class/DFL loss, mAP50, mAP50-95, precision, recall |
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| **Console** | Live training logs with ANSI color and error detection |
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| **System** | GPU utilization, memory, temperature, CPU, disk |
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!!! info "Automatic Checkpoints"
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The Platform automatically saves checkpoints at every epoch. The **best model** (highest mAP) and **final model** are always preserved.
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## Quick Start
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Get started with cloud training in under a minute:
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=== "Cloud (UI)"
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1. Create a project in the sidebar
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2. Click **New Model**
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3. Select a model, dataset, and GPU
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4. Click **Start Training**
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=== "Remote (CLI)"
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```bash
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export ULTRALYTICS_API_KEY="your_api_key"
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yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset \
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epochs=100 project=username/my-project name=exp1
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```
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=== "Remote (Python)"
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```python
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from ultralytics import YOLO
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model = YOLO("yolo26n.pt")
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model.train(
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data="ul://username/datasets/my-dataset",
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epochs=100,
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project="username/my-project",
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name="exp1",
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)
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```
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## Quick Links
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- [**Projects**](projects.md): Organize your models and experiments
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- [**Models**](models.md): Manage trained checkpoints
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- [**Cloud Training**](cloud-training.md): Train on cloud GPUs
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## FAQ
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### How long does training take?
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Training time depends on:
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- Dataset size (number of images)
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- Model size (n, s, m, l, x)
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- Number of epochs
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- GPU type selected
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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.
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### Can I train multiple models simultaneously?
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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.
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### What happens if training fails?
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If training fails:
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1. Checkpoints are saved at each epoch
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2. You can resume from the last checkpoint
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3. Credits are only charged for completed compute time
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### How do I choose the right GPU?
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| Scenario | Recommended GPU |
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| ----------------------------- | ---------------- |
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| Most training jobs | RTX PRO 6000 |
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| Large datasets or batch sizes | H100 SXM or H200 |
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| Budget-conscious | RTX 4090 |
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