Well commented code for different types of training configurations
-
Updated
Jul 1, 2021 - Jupyter Notebook
Well commented code for different types of training configurations
Faster large mini-batch distributed training w/o. squeezing devices
This project contains scripts/modules for distributed training
Short course: Introduction to Machine Learning
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. 🚀🧠
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.
Tools for ML/MXNet on Kubernetes. Rework of original tf-operator to support MXNet framework.
Transfer Learning applied to Image Classification (VGG16 - Distributed Training on Multi-GPUs)
Training Using Multiple GPUs
Example of Distributed pyTorch
Distributed training of a CNN using MNIST dataset, Tensorflow and Horovod
Distributed training using PyTorch DDP & Suggestive resource allocation
Official DGL Implementation of "Distributed Graph Data Augmentation Technique for Graph Neural Network". KSC 2023
General purpose Kubernetes operator for DL frameworks written in Python
Project showcasing how to get started with Distributed XGBoost using PySpark in CML.
Distributed Machine Learning for Bio-marker Prediction from Big Data Stream collected from Multi-modal Wearable Sensor Data
Metaflow On Kubernetes
Add a description, image, and links to the distributed-training topic page so that developers can more easily learn about it.
To associate your repository with the distributed-training topic, visit your repo's landing page and select "manage topics."