Experiments Python Packages

hogql-parser

🦔 PostHog is an all-in-one developer platform for building successful products. We offer product analytics, web analytics, session replay, error tracking, feature flags, experimentation, surveys, data warehouse, a CDP, and an AI product assistant to help debug your code, ship features faster, and keep all your usage and customer data in one stack.

gofeatureflag-python-provider

GO Feature Flag is a simple, complete and lightweight self-hosted cloud native feature flag solution 100% Open Source. 🎛️

qcodes

Modular data acquisition framework

silx

silx toolkit

experimaestro

Experimaestro (python version)

record-keeper

Record experiment data easily

ebtorch

Collection of PyTorch additions, extensions, utilities, uses and abuses

springs

A set of utilities to turn Dataclasses into useful configuration managers.

expfactory

software to generate a reproducible container with a battery of experiments

pytorchlabflow

PyTorchLabFlow is a lightweight framework that simplifies PyTorch experiment management...

mltraq

Track and Collaborate on ML & AI Experiments.

autolab

Python package for scientific experiments interfacing and automation

boilr

Basic framework for model training and evaluation in PyTorch.

muteria

Software Analysis and Testing Framework

poscidyn

Fast Simulation of Nonlinear Oscillator Dynamics in Python

lazyscribe

Lightweight, lazy model experiment logging

pytorch-kito

Effortless PyTorch training - define your model, Kito handles the rest

reddit-experiments

reddit's python experiments framework

plynx

PLynx is a domain agnostic platform for managing reproducible experiments and data-oriented workflows.

grilly

GPU-accelerated neural network operations using Vulkan compute shaders

dead-salmon-brain

Apache Spark based framework for analysis A/B experiments

booltest

BoolTest: Polynomial randomness tester

built

BuilT - Build a Trainer of deep neural networks

easyembed

downloading pre-trained embedding easily and keeping only the necessary amount of embedding while training