AI Infra2020
Optimizes distributed PyTorch training and inference for very large models with ZeRO memory partitioning, parallelism, MoE, offload, and compression. Best when GPU memory, training cost, or cluster throughput is the bottleneck.
AI Image2020
PyTorch object detector built for shipping: train on your own data, then export to ONNX, CoreML, TFLite, or TensorRT with one command. Comes in five sizes (n/s/m/l/x) and adds instance-segmentation and classification heads beyond bounding-box detection.
AI Audio2020
Extracts vocals and instrumentals from audio using an ensemble of models — MDX-Net/MDX23C, Demucs v3/v4, and the VR architecture. Runs locally via a Tkinter GUI with GPU acceleration across Nvidia, AMD, Intel, and Apple chips.
Collects 60+ PyTorch implementations of neural network papers — transformers, diffusion, GANs, RL, optimizers — each annotated line-by-line and rendered beside the code at nn.labml.ai, so you study the math and a runnable implementation together.
AI Train2020
PyTorch library for operator learning: neural networks that map between whole function spaces, not fixed grids, so a model trained at one resolution runs at any other. Bundles FNO, Tensorized FNO and related architectures, mainly for solving PDEs.
AI Infra2020
Sits between PyTorch and micrograd: eager tensors with autograd plus a small, fully hackable compiler that fuses operations into kernels. Adding a new accelerator backend takes about 25 low-level ops, so it runs on CUDA, Metal, AMD, and WebGPU.
A 12-week, 24-lesson beginner-friendly AI curriculum with executable Jupyter notebooks, quizzes and labs that teach neural networks, computer vision, NLP, generative models and ethics using PyTorch and TensorFlow examples.
AI Infra2021
Drop-in transformer building blocks with custom CUDA kernels: memory-efficient exact attention (up to ~10x faster), block-sparse attention, fused softmax/layernorm/SwiGLU ops. Cuts VRAM and speeds up diffusion and LLM training on Nvidia GPUs.
AI Train2021
HPC-AI Technology Inc. (Colossal-AI team), Shenggui Li +1HPC-AI Technology Inc., National University of Singapore
Scales a single-GPU training script to thousands of GPUs through a unified interface, combining data, pipeline, tensor, and sequence parallelism. Its Gemini memory manager offloads tensors across GPU, CPU, and NVMe so models far larger than VRAM still fit.
AI Image2021
Library for benchmarking, developing, and deploying deep-learning visual anomaly-detection models — includes ready-to-use model implementations (PatchCore, DINO-based), experiment/HPO tooling, OpenVINO export for edge inference, and a low-code Studio for deployment.
AI Deploy2022
Deploys PyTorch models directly on phones, microcontrollers, and embedded hardware via ahead-of-time compilation to a ~50KB C++ runtime. Delegates subgraphs to 12+ backends (XNNPACK, CoreML, Qualcomm, ARM Ethos-U) with torchao quantization.
AI Infra2022
Compiles plain Python functions into GPU or CPU kernels at runtime via a JIT decorator, with differentiable output that plugs into PyTorch, JAX, and Paddle. Ships physics, robotics, geometry, and FEM primitives — particles, meshes, ray-casting, FFT.