# Entraînez vos modèles à votre façon

Du no-code aux pipelines PyTorch personnalisés. Choisissez votre niveau de contrôle et laissez Picsellia gérer l'infrastructure.

[See It In Action](/content/demo/index.html) [Documentation](https://documentation.picsellia.com/docs/experiment)

Works with:

+more

1-click

GPU allocation

20+

Pre-built pipelines

∞

Custom flexibility

0

Infrastructure to manage

## Choose your level of control

Start with no-code for quick iterations, use the SDK for automation, or build fully custom pipelines when you need complete control.

### No-Code Training

Launch training jobs directly from the UI. Select a pre-built pipeline, configure parameters, and start training.

- Configure in UI
- Select GPU
- Launch training
- Visual parameter configuration
- One-click GPU allocation
- Real-time progress monitoring

### Python SDK

Full programmatic control with our Python SDK. Integrate into your existing workflows and CI/CD pipelines.

```python
from picsellia import Client

client = Client()
project = client.get_project("defects")

# Create experiment
experiment = project.create_experiment("yolo-training")

# Attach dataset
dataset = client.get_dataset("defects").get_version("v3")
experiment.attach_dataset("train", dataset)
```

Type-safe API

Jupyter support

Pipeline automation

### Custom Pipelines

Build custom training pipelines with CV Engine. Modular steps, any framework, full flexibility.

```python
from picsellia_cv_engine import step, Pipeline

@step
def train(context):
    model = load_model(context.parameters)
    for epoch in range(context.parameters.epochs):
        # Your training logic
        context.experiment.log("loss", loss)
    context.experiment.store("model.pt")

pipeline = Pipeline([train])
pipeline.run()
```

Composable steps

Framework agnostic

Local + remote execution

## Production-grade models, ready to train

Start training in minutes with our pre-built pipelines. Ultralytics for YOLO, SAM2 for segmentation, Grounding DINO for zero-shot detection, and more.

- One-click deployment to GPU
- Pre-configured hyperparameters
- Automatic metric logging
- Model export to registry

### Ultralytics

production

Train YOLOv8/v11 models for detection, segmentation, and classification

- Detection
- Segmentation
- Classification

### SAM2

production

Segment Anything Model for automatic mask generation and refinement

- Segmentation
- Pre-annotation

### Grounding DINO

production

Open-set object detection with text prompts for zero-shot labeling

- Detection
- Zero-shot

### CLIP

production

Fine-tune embeddings for domain-specific similarity search

- Embeddings
- Classification

## Build custom pipelines with ease

Picsellia CV Engine is a modular toolkit for building computer vision workflows. Composable steps, framework extensions, and CLI automation.

terminal

```bash
$pip install picsellia-cv-engine

# Initialize a new training pipeline
$pxl-pipeline init --type training

# Run locally for testing
$pxl-pipeline test

# Deploy to Picsellia cloud
$pxl-pipeline deploy --gpus 1
```

CLI + Python decorators [View Docs →](https://picselliahq.github.io/picsellia-cv-engine/)

### Modular Steps

Build pipelines from reusable, composable steps with @step decorators

### Framework Extensions

Pre-built integrations for Ultralytics, SAM2, CLIP, and more

### Local & Remote

Test locally, deploy to Picsellia cloud with one command

### Auto Logging

Metrics, artifacts, and parameters logged automatically

### Managed GPUs

Available now

NVIDIA A100

80GB VRAM

$3.50/hr

Pay only for what you use

Auto-shutdown on completion

### Bring Your Own Compute

SageMaker

Connect your AWS SageMaker account to train on your own infrastructure while keeping full orchestration through Picsellia.

- Your AWS billing
- Your GPU instances
- Our orchestration

## Zero infrastructure to manage

Focus on your models, not your servers. Train on our managed A100 GPUs at $3.50/hr, or connect your own SageMaker account for full flexibility. Picsellia handles environment setup and job orchestration.

- NVIDIA A100 GPUs at $3.50/hr
- Bring your own SageMaker account
- Pre-configured CUDA environments
- Automatic job queuing
- Real-time training logs

## Connected to your entire workflow

AI Lab connects directly to datasets, experiment tracking, and model deployment. Full lineage from data to production.

- [Dataset](/content/dataset-management/index.html)
-  AI Lab
- [Experiment](/content/experiment-tracking/index.html)
- [Model](/content/model-deployment/index.html)
- [Production](/content/model-monitoring/index.html)

## Ready to train your models?

Start with no-code training or build custom pipelines. Zero infrastructure to manage.

[Start Free Trial](/content/trial/index.html) [Request Demo](/content/demo/index.html)