This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
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Updated
Feb 13, 2024 - Jupyter Notebook
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)
Hyperparameter tuning for FCN using Ray Tune
Deep reinforcement learning framework for fast prototyping based on PyTorch
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.
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
Learning ReLU networks to high uniform accuracy is intractable (ICLR 2023)
YOLOV8 - Object detection
The project implements the Snake game as an OpenAI Gym environment. Deep learning is implemented using the RLlib library. A convolutional neural network is used to work with the game frames.
DeepAR implementation for seasonal influenza cases in German districts
Official Repository for the paper: Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation
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
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