Picsellia CV Engine
Picsellia CV Engine is a modular engine for building, testing, and deploying computer vision pipelines — fully integrated with the Picsellia platform.
Whether you're transforming datasets, training models, or tracking experiments, this engine helps you organize everything into clean, reusable components.
🧠 What’s a pipeline?
A pipeline is a structured sequence of actions — like:
- 🧼 Preprocessing images
- 🧪 Training a model
- 📊 Evaluating predictions
- ☁️ Uploading results to Picsellia
Each action is implemented as a step — a small, focused function decorated with @step.
You can chain together these steps inside a @pipeline, and run it locally or on Picsellia.
🚀 Getting Started
Install from PyPI:
- With uv:
uv add picsellia-cv-engine
uv add picsellia-pipelines-cli
- With pip:
pip install picsellia-cv-engine
pip install picsellia-pipelines-cli
🛠 Create and run your first pipeline
Use the Picsellia Pipelines CLI to scaffold and manage your pipelines.
1. Initialize a pipeline
pxl-pipeline init my_pipeline --type training --template ultralytics
This generates everything you need: config, Dockerfile, code templates, and a virtual environment.
➡️ See pipeline lifecycle and commands
2. Run it locally
pxl-pipeline test my_pipeline
3. Deploy to Picsellia
pxl-pipeline deploy my_pipeline
🔎 Want real examples? Explore the pipeline usage templates for training and processing workflows.
📘 Documentation
The full documentation is available at: 👉 https://picselliahq.github.io/picsellia-cv-engine/
It includes:
- Getting Started
- CLI Usage Guide
- API Reference
- Pipeline templates & examples
🧑💻 Local Development
To contribute or explore the code:
1. Clone the repo
git clone https://github.com/picselliahq/picsellia-cv-engine.git
cd picsellia-cv-engine
2. Install dependencies
uv sync
3. Run the documentation
uv run mkdocs serve -a 127.0.0.1:8080
Then open http://127.0.0.1:8080 in your browser.
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