Cloud Training

Ultralytics Platform Cloud Training offers single-click training on cloud GPUs, making model training accessible without complex setup. Train YOLO models with real-time metrics streaming and automatic checkpoint saving.

graph LR
    A[Configure] --> B[Start Training]
    B --> C[Provision GPU]
    C --> D[Download Dataset]
    D --> E[Train]
    E --> F[Stream Metrics]
    F --> G[Save Checkpoints]
    G --> H[Complete]

    style A fill:#2196F3,color:#fff
    style B fill:#FF9800,color:#fff
    style E fill:#9C27B0,color:#fff
    style H fill:#4CAF50,color:#fff

Training Dialog

Start training from the platform UI by clicking New Model on any project page (or Train from a dataset page). The training dialog has two tabs: Cloud Training and Local Training.

Step 1: Select Base Model

Choose from official YOLO26 models or your own trained models:

Official models are organized by task type (Detect, Segment, Pose, OBB, Classify) with sizes from nano to xlarge.

Step 2: Select Dataset

Choose a dataset to train on (see Datasets):

Step 3: Configure Parameters

Set core training parameters:

Step 4: Advanced Settings (Optional)

Expand Advanced Settings to access the full YAML-based parameter editor with 40+ training parameters organized by group (see configuration reference):

Parameters are task-aware (e.g., copy_paste only shows for segment tasks, pose/kobj only for pose tasks). A Modified badge appears when values differ from defaults, and you can reset all to defaults with the reset button.

mosaic: 1.0       # Keep mosaic on
mixup: 0.3        # Add mixup blending
copy_paste: 0.3   # Add copy-paste (segment only)
fliplr: 0.5       # Horizontal flip
degrees: 10.0     # Slight rotation
scale: 0.9        # Aggressive scaling

Step 5: Select GPU (Cloud Tab)

Choose your GPU from Ultralytics Cloud:

The dialog shows your current balance and a Top Up button. An estimated cost and duration are calculated based on your configuration (model size, dataset images, epochs, GPU speed).

Step 6: Start Training

Click Start Training to launch your job. The Platform:

1. Provisions a GPU instance
2. Downloads your dataset
3. Begins training
4. Streams metrics in real-time

Training Job Lifecycle

Training jobs progress through the following statuses:

Monitor Training

View real-time training progress on the model page's Train tab:

Charts Subtab

Console Subtab

Live console output with ANSI color support, progress bars, and error detection.

System Subtab

Real-time GPU utilization, memory, temperature, CPU, and disk usage.

Checkpoints

Checkpoints are saved automatically:

• Every epoch: Latest weights saved
• Best model: Highest mAP checkpoint preserved
• Final model: Weights at training completion

Cancel Training

Click Cancel Training on the model page to stop a running job:

• The compute instance is terminated
• Credits stop being charged
• Checkpoints saved up to that point are preserved

Remote Training

graph LR
    A[Local GPU] --> B[Train]
    B --> C[ultralytics Package]
    C --> D[Stream Metrics]
    D --> E[Platform Dashboard]

    style A fill:#FF9800,color:#fff
    style C fill:#2196F3,color:#fff
    style E fill:#4CAF50,color:#fff

Train on your own hardware while streaming metrics to the platform.

pip install -U ultralytics

Setup API Key

1. Go to Settings > Profile (API Keys section)
2. Create a new key (or the platform auto-creates one when you open the Local Training tab)
3. Set the environment variable:

export ULTRALYTICS_API_KEY="your_api_key"

Train with Streaming

Use the project and name parameters to stream metrics:

yolo train model=yolo26n.pt data=coco.yaml epochs=100 \
  project=username/my-project name=experiment-1

from ultralytics import YOLO

model = YOLO("yolo26n.pt")
model.train(
    data="coco.yaml",
    epochs=100,
    project="username/my-project",
    name="experiment-1",
)

The Local Training tab in the training dialog shows a pre-configured command with your API key, selected parameters, and advanced arguments included.

Using Platform Datasets

Train with datasets stored on the platform using the ul:// URI format:

yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset epochs=100 \
  project=username/my-project name=exp1

from ultralytics import YOLO

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

The ul:// URI format automatically downloads and configures your dataset. The model is automatically linked to the dataset on the platform (see Using Platform Datasets).

Billing

Training costs are based on GPU usage:

Cost Estimation

Before training starts, the platform estimates total cost by:

1. Estimating seconds per epoch from dataset size, model complexity, image size, batch size, and GPU speed
2. Calculating total training time by multiplying seconds per epoch by the number of epochs, then adding startup overhead
3. Computing the estimated cost from total training hours multiplied by the GPU's hourly rate

Factors affecting cost:

Cost Examples

Billing Flow

graph LR
    A[Estimate Cost] --> B[Balance Check]
    B --> C[Train]
    C --> D[Charge Actual Runtime]

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

Cloud training billing flow:

1. Estimate: Cost calculated before training starts
2. Balance Check: Available credits are checked before launch
3. Train: Job runs on selected compute
4. Charge: Final cost is based on actual runtime

Payment Methods

View Training Costs

After training, view detailed costs in the Billing tab:

• Per-epoch cost breakdown
• Total GPU time
• Download cost report

Training Tips

Choose the Right Model Size

Optimize Training Time

Troubleshooting

FAQ

How long does training take?

Training time depends on:

• Dataset size
• Model size
• Number of epochs
• GPU selected

Typical times (1000 images, 100 epochs):

Can I train overnight?

Yes, training continues until completion. You'll receive a notification when training finishes. Make sure your account has sufficient balance for epoch-based training.

What happens if I run out of credits?

Training pauses at the end of the current epoch. Your checkpoint is saved, and you can resume after adding credits.

Can I use custom training arguments?

Yes, expand the Advanced Settings section in the training dialog to access a YAML editor with 40+ configurable parameters. Non-default values are included in both cloud and local training commands.

Can I train from a dataset page?

Yes, the Train button on dataset pages opens the training dialog with the dataset pre-selected and locked. You then select a project and model to begin training.

Training Parameters Reference

📅 Created 1 month ago ✏️ Updated 5 days ago

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