Train Models Your Way

From no-code training to custom PyTorch pipelines. Choose your level of control and let Picsellia handle the infrastructure.

See It In Action Documentation

Works with:

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.

Features:

  • 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.

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)

Features:

  • Type-safe API
  • Jupyter support
  • Pipeline automation

Custom Pipelines

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

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()

Features:

  • 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.

Benefits:

  • 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.

Installation:

$pip install picsellia-cv-engine

Commands:

# 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

Features:

  • CLI + Python decorators
  • Build pipelines from reusable, composable steps with @step decorators
  • Pre-built integrations for Ultralytics, SAM2, CLIP, and more
  • Test locally, deploy to Picsellia cloud with one command
  • Auto Logging: Metrics, artifacts, and parameters logged automatically

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.

Options:

  • 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

Experiment Model Production

Ready to train your models?

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

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