Meta-Iterative Map-Reduce to perform Regression massively parallely on a cluster with MPI and CUDA for GPU and CPU-nodes support.
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Updated
Jul 3, 2018 - Cuda
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.
📜 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.
This project contains scripts/modules for distributed training
Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits
Short course: Introduction to Machine Learning
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.
Everything is born from a simple experiment.
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.
Adaptive Tensor Parallelism for Foundation Models
Tensorflow implementation of U-Net model with TPU Estimator support.
Transfer Learning applied to Image Classification (VGG16 - Distributed Training on Multi-GPUs)
Example ML projects that use the Determined library.
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