AI Coding2018
Differentiable programming framework for quantum computers: build variational circuits, compute their gradients alongside PyTorch, TensorFlow, or JAX, and run identical code on simulators or real hardware via IBM, AWS Braket, and Google plugins.
AI Infra2018
Runs ONNX models faster on CPU, GPU, and NPU by routing graph subgraphs to backend execution providers (CUDA, TensorRT, OpenVINO, DirectML, CoreML). One engine serves the same model across cloud, browser, mobile, and edge, for both inference and training.
AI Image2019
Bundles hundreds of pretrained image backbones — ResNet, EfficientNet, ViT, ConvNeXt, Swin and more — behind one consistent API for classification and feature extraction, with training and inference scripts that reproduce published ImageNet results.
AI Train2019
Turns raw PyTorch training loops into structured modules that scale from a laptop to multi-node GPUs without rewriting model logic. It handles precision, checkpointing, logging, and distributed execution while preserving PyTorch control.
AI Train2019
Implements deep RL algorithms (PPO, DQN, SAC, TD3, DDPG, C51, PPG) as standalone single-file scripts — the PPO Atari variant is ~340 readable lines. Built for research debugging and reproducibility, with W&B and TensorBoard tracking.
AI Audio2019
Build, fine-tune, and deploy speech AI on NVIDIA GPUs: ASR, text-to-speech, and speech LLMs in one PyTorch stack. Ships pretrained Parakeet/Canary recognition and Magpie TTS checkpoints; broader LLM/multimodal training now lives in v2.7.0.
AI Audio2019
Provides a toolkit and codebase for building, training, and deploying speech and multimodal models — Automatic Speech Recognition, Text-to-Speech, and speech-aware LLMs — with modular neural components and pre-trained checkpoints for PyTorch. Supports streaming/low-latency inference, multi-language models, and optional compiled kernels for acceleration.
AI Image2019
Modular implementations of object detection, instance/semantic/panoptic segmentation and related vision models for research and deployment. Offers a large model zoo, export to TorchScript/Caffe2, and PyTorch-native optimizations for faster training and extensibility.
AI Train2020
thu-ml (Tsinghua University Machine Learning Group)Tsinghua University, appliedAI Institute for Europe
Deep reinforcement learning library on pure PyTorch and Gymnasium, with 30+ algorithms across on-policy, off-policy, and offline RL. Exposes both a one-call high-level interface and a procedural API, plus vectorized envs and reproducible MuJoCo benchmarks.
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.