neulab / prompt2model

prompt2model - Generate Deployable Models from Natural Language Instructions

https://github.com/neulab/prompt2model

EuroPython 2023 Videos

Here are all the videos for the conference, brought to you by the EuroPython 2023 Team and the EuroPython Society.

https://www.youtube.com/playlist?list=PL8uoeex94UhFcwvAfWHybD7SfNgIUBRo-

PyGraft

Configurable Generation of Schemas and Knowledge Graphs at Your Fingertips.

https://github.com/nicolas-hbt/pygraft

19X faster response time

Lincoln Loop optimized a large publishing platform's database performance. Overall, the database performance increased 19 times.

https://lincolnloop.com/insights/optimizing-response-time-19x-faster/

facebookresearch / codellama

Inference code for CodeLlama models

https://github.com/facebookresearch/codellama

vpselector

Visual Pandas Selector: Visualize and interactively select time-series data.

https://github.com/manumerous/vpselector

Speeding up Floyd-Steinberg dithering: an optimization exercise

A worked out example: optimizing low-level code to get significant performance and memory improvements.

https://pythonspeed.com/articles/optimizing-dithering/

Why Are There So Many Python Dataframes?

This post explores the proliferation of Python dataframes, dissecting the reasons behind their prevalence in data science and analysis, shedding light on the various libraries and frameworks that contribute to their abundance.

https://ponder.io/why-are-there-so-many-python-dataframes/

Building RAG-based LLM Applications for Production (Part 1)

In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale, evaluation and routing.

https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1

Logparser

Logparser provides a machine learning toolkit and benchmarks for automated log parsing, which is a crucial step for structured log analytics

https://github.com/logpai/logparser

QuasiQueue

QuasiQueue is a MultiProcessing library for Python that makes it super easy to have long running MultiProcess jobs. QuasiQueue handles process creation and cleanup, signal management, cross process communication, and all the other garbage that makes people hate dealing with multiprocessing.

https://github.com/tedivm/quasiqueue

HTTP-Shell

MultiPlatform HTTP Reverse Shell.

https://github.com/JoelGMSec/HTTP-Shell

EvoDiff

Generation of protein sequences and evolutionary alignments via discrete diffusion models.

https://github.com/microsoft/evodiff

Compiling ML models to C for fun

ML models can be compiled to graphs, which can be traversed to perform forward and backward passes. This approach can improve performance and make it easier to debug ML models.

https://bernsteinbear.com/blog/compiling-ml-models/

Temporian

Temporian is a Python library for feature engineering and data augmentation of temporal data (e.g. time-series, transactions) in machine learning applications.

https://github.com/google/temporian

Simulate the Monty Hall problem in Python

Use Python to solve this classic probability puzzle that has stumped mathematicians and Nobel Prize winners!

https://www.dataschool.io/python-probability-simulation/

PyLLMCore

A pythonic library providing light-weighted interface with LLMs

https://github.com/paschembri/py-llm-core