Popular GitHub Repositories for Performance Optimization

OneDiff: An out-of-the-box acceleration library for diffusion models.

Dynamic batching library for Deep Learning inference. Tutorials for LLM, GPT scenarios.

Deep Learning at Scale Training Event at NERSC

MONTE Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space and building a search tree accordingly. It has already had a profound impact on Artificial Intelligence (AI) approaches for domains that can be represented as trees of sequential decisions, particularly games and planning problems. In this project I used a board game called "HEX" as a platform to test different simulation strategies in MCTS field.

The course equips developers with techniques to enhance the reliability of LLMs, focusing on evaluation, prompt engineering, and fine-tuning. Learn to systematically improve model accuracy through hands-on projects, including building a text-to-SQL agent and applying advanced fine-tuning methods.

A lightweight and efficient memory optimization tool for Windows. Free up system resources, clear cache, and enhance performance with manual and automated memory cleanup. Open-source and customizable.

EdgeTrain: An adaptive Python package for optimizing deep learning training parameters on edge devices by monitoring CPU and GPU performance.

This repository implements a 3-layer neural network with L2 and Dropout regularization using Python and NumPy. It focuses on reducing overfitting and improving generalization. The project includes forward/backward propagation, cost functions, and decision boundary visualization. Inspired by the Deep Learning Specialization from deeplearning.ai.