Meta-Iterative Map-Reduce to perform Regression massively parallely on a cluster with MPI and CUDA for GPU and CPU-nodes support.

Well commented code for different types of training configurations

Project showcasing how to get started with Distributed XGBoost using PySpark in CML.

This project contains scripts/modules for distributed training

📜 A python library for distributed training of a Transformer neural network across the Internet to solve the Running Key Cipher, widely known in the field of Cryptography.

Short course: Introduction to Machine Learning

Everything is born from a simple experiment.

Adaptive Tensor Parallelism for Foundation Models

Distributed Machine Learning for Bio-marker Prediction from Big Data Stream collected from Multi-modal Wearable Sensor Data

Compression-accelerated distributed DNN training system at large scales.

Access programming assignments and labs from the TensorFlow Advanced Techniques and TensorFlow Developer Specializations by deeplearning.ai on Coursera. 🚀🧠

Development of Project HPGO | Hybrid Parallelism Global Orchestration

基于kubernetes/client-go API， 进行分布式训练GPU资源生命周期控制并支持多用户多任务训练日志实时通过websocket的连续重定向

A GitHub repository showcasing the implementation of AI scaling techniques and integration with MLflow for streamlined experiment tracking and management in machine learning workflows.

Messing with Distributed TensorFlow and Kubernetes

In this project, I implement and compare the different distributed training techniques from data parallelization and model parallelization from scratch using PyTorch

This repository shows how to distribute training of large machine learning models to make it faster.

Experiments with low level communication patterns that are useful for distributed training.

Tools for ML/MXNet on Kubernetes. Rework of original tf-operator to support MXNet framework.