Welcome to Cogitare’s documentation!


Cogitare is a Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python. A friendly interface for beginners and a powerful toolset for experts.

It uses the best of PyTorch, Dask, NumPy, and others tools through a simple interface to train, to evaluate, to test models and more with high performance.

Check the tutorials at Cogitare Tutorials.

With Cogitare, you can use classical machine learning algorithms with high performance and develop state-of-the-art models quickly.

The primary objectives of Cogitare are:

  • provide an easy-to-use interface to train and evaluate models;
  • provide tools to debug and analyze the model;
  • provide implementations of state-of-the-art models (models for common tasks, ready to train and ready to use);
  • provide ready-to-use implementations of straightforward and classical models (such as LogisticRegression);
  • be compatible with models for a broad range of problems;
  • be compatible with other tools (scikit-learn, etcs);
  • keep growing with the community: accept as many new features as possible;
  • provide a friendly interface to beginners, and powerful features for experts;
  • take the best of the hardware through multi-processing and multi-threading;
  • and others.

Currently, it’s a work in progress project that aims to provide a complete toolchain for machine learning and deep learning development, taking the best of cuda and multi-core processing.

Contributions are welcome!

Indices and tables