This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.

A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)

Deep reinforcement learning framework for fast prototyping based on PyTorch

Hyperparameter tuning for FCN using Ray Tune

1st place solution to Automated Machine Learning https://www.automl.ai/competitions/2

A sample workflow for classifying wetlands in Google Earth Engine. Uses data from multiple sources.

Learning ReLU networks to high uniform accuracy is intractable (ICLR 2023)

Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Code to reproduce 'Learning Distance Estimators from Pivoted Embeddings of Metric Objects'.

Hyperparameter Optimization of Tree Parity Machines to Minimize the Effectiveness of Unconventional Attacks on Neural Cryptography​.

Instance segmentation with U-Net/Mask R-CNN workflow using Keras & Ray Tune

YOLOV8 - Object detection

DeepAR implementation for seasonal influenza cases in German districts

Official Repository for the paper: Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Low-code machine learning and deep learning

Training ReLU networks to high uniform accuracy is intractable

Micro tutorial on how to run and scale HPO with LightGBM and Tune

Additional stoppers for ray tune