Open-source deep-learning optimisation library from Microsoft that scales PyTorch training and inference to trillions of parameters with maximum efficiency.
The 500 AI Agents Projects is a curated collection of AI agent use cases across various industries. It showcases practical applications and provides links to open-source projects for implementation, illustrating how AI agents are transforming sectors such as healthcare, finance, education, retail, and more.
LightX2V is an advanced lightweight video generation inference framework engineered to deliver efficient, high-performance video synthesis solutions. This unified platform integrates multiple state-of-the-art video generation techniques, supporting diverse generation tasks including text-to-video (T2V) and image-to-video (I2V). X2V represents the transformation of different input modalities (X, such as text or images) into video output (V).
LightGBM is an open-source gradient-boosting framework that delivers fast, memory-efficient tree-based learning for classification, regression and ranking tasks.
Ray is an open-source distributed compute engine that lets you scale Python and AI workloads—from data processing to model training and serving—without deep distributed-systems expertise.
Open-source, node-based workflow-automation platform for designing and running complex integrations and AI-powered flows.
NVIDIA’s model-parallel training library for GPT-like transformers at multi-billion-parameter scale.
A PyTorch-based system for large-scale model parallel training, memory optimization, and heterogeneous acceleration.
Open-source framework that provides composable building blocks to create, orchestrate and monitor LLM-powered applications and agents.
Data framework that connects large-language models to private or enterprise data via indexing, retrieval and agent orchestration.
Memory layer that lets AI agents remember users and context across sessions.
Open-source community and framework researching the scaling laws of multi-agent systems.
