Python 3.8+ toolbox for submitting jobs to Slurm

Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.

GVProf: A Value Profiler for GPU-based Clusters

Python CLI to apply word2vec to all sorts of text documents.

The Clusters-Features package allows data science users to compute high-level linear algebra operations on any type of data set. It computes approximatively 40 internal evaluation scores such as Davies-Bouldin Index, C Index, Dunn and its Generalized Indexes and many more ! Other features are also available to evaluate the clustering quality.

Interactive tree-maps with SBERT & Hierarchical Clustering (HAC)

A lightweight REST interface for under-the-covers cluster system management.

A pytest powered framework for multi-host integration and system testing

SFoF is a friends-of-friends galaxy cluster detection algorithm that operates in either spectroscopic or photometric redshift space. The linking parameters, both transverse and along the line-of-sight, change as a function of redshift to account for selection effects. The code is written in C++ and implements OMP to loop through the photometric …

🗄️💧 Find exposed and unsecured databases from a list of hosts. 🗄️💧

Open-Source Apache Kafka Governance Platform

A package for easier interface and setup for pipelining in distributed cluster.

VmShepherd is a lightweight, scalable, standalone asynchronous app to manage clusters

Configuration files to configure/compile Abinit

Search for Binary Supernova Ejected OB-type Runaway Stars from Young Open Clusters | Master Thesis Project

The Otago Research Genetic Algorithm for Nanoclusters, Including Structural Methods and Similarity (Organisms) program is a genetic algorithm designed to globally optimise nanoclusters (also known as clusters or nanoparticles)

Simple project for extraction of key-phrases from single document based on Sentence Trasfomers

Clustering text (recipes) using TF-IDF and K-Means.

Percolation Problem Analysis

The thesis presents the parallelisation of a state-of-the art clustering algorithm, FISHDBC. This objective has been achived by improving the main data structures and components of the algorithm: HNSW, MST and HDBSCAN. My contribution is based on a lock-free strategy, completely wrote in Python.