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Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.
This is a final delivery for the parallel programming class at our university, here we will present 2 algorithms which are the Hash-table and Luby's algorithm, they will be presented with normal execution and with parallel execution.
Projeto de TCC focado na paralelização e distribuição do treinamento de redes neurais utilizando clusters com GPUs Nvidia para otimização de tempo de treino.
Official Implementation of the paper MonoIS3DLoc: Simulation to Reality Learning Based Monocular Instance Segmentation to 3D Objects Localization From Aerial View
Exploring data aggregation with Pandas, Scikit-learn, Polar, and Dask. This repo includes scripts, benchmarks, and insights for handling distributed test data.
Parallel search algorithm for finding the closest object in a collection of polygonal models based on Hausdorff Distance with implementation using MPI in Python