Open source. Open possibilities.

fastai is a deep learning library built on PyTorch that provides both high-level components for rapid productivity and low-level components for research and customization.

PyTorch is an open-source machine learning library providing tensor computation with GPU acceleration and a flexible deep neural network framework based on a tape-based autograd system.

A tiny GPT language model (~540M parameters) designed to be trained from scratch on consumer laptop hardware, with pretraining on Fineweb-edu and chat finetuning capabilities.

CatBoost is Yandex's open-source gradient boosting library for classification, regression and ranking, with native categorical feature support, CPU/GPU training, distributed training via Apache Spark, and APIs for Python, R, Java and C++.

FragmentColor is a cross-platform GPU abstraction for Rust, JS, Python, Swift, and Kotlin. It simplifies shader workflows using WGSL/GLSL across Vulkan, Metal, DirectX, and WebGPU.

Scalene is a high-performance CPU, GPU, and memory profiler for Python that provides line-level analysis and AI-powered optimization suggestions.

DeepSpeed is a deep learning optimization library designed to make the training and inference of large-scale models easy, efficient, and effective.

Apache TVM is an open machine learning compilation framework designed for universal deployment and customizable compiler pipelines.

Eclipse Deeplearning4J (DL4J) is a JVM-based deep learning ecosystem supporting Java, Scala, Kotlin, and more. It includes model import from Keras, TensorFlow, ONNX; ND4J for linear algebra; SameDiff for automatic differentiation; DataVec for ETL; and runs on CPU/GPU across Windows, Linux, macOS.