BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.

M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.

Python-centric Cookiecutter for Molecular Computational Chemistry Packages

Tool for encapsulating, running, and reproducing data science projects

Change conda environments in the Vim editor (with Jedi-vim support)

Visualizations of machine learning models and algorithms

ROIPooling for pytorch

FPGA tool performance profiling

Flexible omics pipeline

A whole Genome/Metagenome Sequencing Alignment Pipeline

Project Repo for GSoC 2019 at CERN

MATLAB utility to switch between Anaconda environments

CLI Tool to init new reproducible project with the help of conda and github

A tiny parser for more flexible conda environment files

An Anaconda3 environment with relevant python libraries to support various linked data OpenRefine reconciliation scripts

Build and maintain multiple custom conda environments all in once place.

The DKFZ alignment workflow plugin originally developed at the eilslabs.

A minimal setup to let you crunch numbers like a pro. Read the article:

Script to build OpenCV 4.2.0 on conda environment (Python3).

Per-project environments for Conda, Julia, Perl, Python, Rust, and more

Cmder powerline for python venv and Conda env

Pipelines dealing with high throughput sequencing data for microbiology diagnostic procedures. Documentation available at http://metagenlabdiag-pipelines.readthedocs.io/en/latest/ and Docker images at https://hub.docker.com/r/metagenlab/diag_pipelines/.

Tools for Conda

Conda recipes for the abiconda channel.

Bioinformatics Pipeline Building meeting - Demo 2018-12-13 on reproducibility using Snakemake and Conda

Daily notification service using google sheets as data store, deployed to Heroku

Run conda environment in Docker

ILAMB virtual environment

Environmental configuration files used to aid in committing Data Science and Machine Learning.

Simple CSV parser for huge volumes of data with the use of the library Pandas for Python for getting specific columns of a CSV file and putting the extracted data into one or more files (each column in a separated file or all of them in the same output) in a short amount of time.