Stars
High-performance parallel save/load for large NumPy arrays using shared memory and multiprocessing
A lightweight utility for training multiple Pytorch models in parallel.
reinforcement learning, deep reinforcement learning, RL
PyTorch Implementation of "Gradient Surgery for Multi-Task Learning" using multiprocessing
A lightweight utility for training multiple Keras models in parallel and comparing their final loss and last-epoch time.
TensorFlow implementation for "Full Parameter Fine-tuning for Large Language Models with Limited Resources"
The fundamental package for scientific computing with Python.
TensorFlow implementation for Ranger, Ranger2020, RangerQH, RangerVA
TensorFlow implementation for "SOAP: Improving and Stabilizing Shampoo using Adam"
TensorFlow reimplementation for "GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection"
TensorFlow reimplementation for "Sharpness-Aware Minimization for Efficiently Improving Generalization"
TensorFlow implementation for "ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning"
D-Adaptation for SGD, Adam, Adan, Lion and AdaGrad
TensorFlow implementation for "PSGD with the Kronecker product pre-conditioner"
TensorFlow implementation for "ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate"
TensorFlow implementation for "Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models"
TensorFlow implementation for "Gradient Surgery for Multi-Task Learning"
optimizer & lr scheduler & loss function collections in PyTorch
This project implements optimizers for TensorFlow and Keras, which can be used in the same way as Keras optimizers. Machine learning, Deep learning
Reinforcement learning library for Keras and PyTorch.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Machine learning library, Distributed training, Deep learning, Reinforcement learning, Models, TensorFlow, PyTorch
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
A toolkit for developing and comparing reinforcement learning algorithms.
Machine learning library, Distributed training, Deep learning, Reinforcement learning, Models, TensorFlow, PyTorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration