Reverse Dependencies of joblib

The following projects have a declared dependency on joblib:

• analitica-escalable-pec-2 — Example model package.
• analitica-test — Example model package.
• analysis-tools — Analysis tools for Machine learning projects
• analytics-mesh — Facades and common functions necessary for data science and data engineering workflows
• ananse — A python package to partially automate search term selection and writing search strategies for systematic reviews
• anatomy — Shapley-based decomposition to anatomize the of out-of-sample accuracy of time-series forecasting models
• AniMAIRE — Python library for running the anisotropic version of MAIRE+
• animal-classification — classification of animals using machine learning models
• AnimatedWordCloudTimelapse — Animate a timelapse of word cloud
• anime-pgen — cli-утилита для генерации превью-изображений по данным Шикимори
• anl — ANAC Automated Negotiations League Platform
• ANN-Implementation-AmanGupta0112 — Simple ANN implementation using tensflow Dataset
• anndata — Annotated data.
• annhub-python — Main backend module, which is used for developing web-app logic and deploying AI model.
• annif — Automated subject indexing and classification tool
• annin_dofu — annin_dofu description
• AnnotationPipeline — WUR nematology Annotation Pipeline
• anonymeter — Measure singling out, linkability, and inference risk for synthetic data.
• anrg.saga — Collection of schedulers for distributed computing
• anscenter — Backend module for ANSCENTER project
• ansible-risk-insight — My package description
• antco — Ant Colony Optimization framework
• antibacterial-model — A model for predicting antibacterial activity from SMILES strings
• apache-beam — Apache Beam SDK for Python
• apache-beam-ai2 — A FORK! for testing with different dill version
• aplusml — Conduct usefulness simulations of ML models embedded in workflows
• apollinaire — Module for helio- and asteroseismic data analysis
• app-rappi-dfmejial — Rappi Titanic challenge
• application-gces-2-2022-douglasmonteles — no summary
• approxbayescomp — Approximate Bayesian Computation for actuaries
• approximate-cluster-identities — A package to calculate and visualise approximate cluster identities for a large number of short nucleotide sequences using minimizers.
• apscale — Advanced Pipeline for Simple yet Comprehensive AnaLysEs of DNA metabarcoding data
• APSCALE-blast — Advanced Pipeline for Simple yet Comprehensive AnaLysEs of DNA metabarcoding data - BLAST application
• apsimNGpy — apsimx next generation package interface
• aquariumlearning — Aquarium Learning Python Client
• archetype-core-nlp — no summary
• archive-query-log — Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives.
• arcos4py — A python package to detect collective spatio-temporal phenomena.
• arctic-ai — Python package for Arctic Workflow. Mirrors jupyter developments.
• arcus-azureml — A Python library to improve MLOps methodology on Azure Machine Learning
• ares-ai — ARES is an advanced evaluation framework for Retrieval-Augmented Generation (RAG) systems,
• argueview — ArgueView is a tool for generating text-based presentations for machine-learning predictions and feature-importance based explanation tools. The tool makes use of Toulmin's model of argumentation for structuring the text-based explanations.
• arnold-house-price-regression-model — Example regression model package by Arnold Ighiwiyisi
• artap — Platform for robust design optimization
• artificial-neural-network-model-automation — This repository is for automating artificial neural network model creation with tabular data using Keras framework.
• artis-tomo — Scientific Methods for tomography
• artus — A geoAI package to train and predict spatial occurences on rasters or georeferenced images.
• arus — Activity Recognition with Ubiquitous Sensing
• ASAPPpy — Semantic Textual Similarity and Dialogue System package for Python
• asleep — A sleep classification tool for wearables
• asmd — Audio-Score Meta-Dataset
• asoid — ASOiD: An active learning approach to behavioral classification
• aspect-stable — Automatic SPEctra Components Tagging
• aspire — Algorithms for Single Particle Reconstruction
• asr-library — Automatic Speech Recognition inference for wav2vec2 models
• ast-toolbox — A toolbox for worst-case validation of autonomous policies
• astrodust — A library for predicting the distribution of dust particles in protoplanetary disks
• astromer — Creates light curves embeddings using ASTROMER
• AstronomyCalc — no summary
• astroslam — A forward model using SVR to estimate stellar parameters from spectra.
• AsympDirsCalculator — Python library containing tools for calculating asymptotic directions and vertical cut-off rigidities.
• atacnet — Package for building co-accessibility networks from ATAC-seq data.
• atelierflow — An ML pipeline using apache beam for run experiments
• atlantis — Python library for simplifying data science
• atom-ml — A Python package for fast exploration of machine learning pipelines
• atoMEC — KS-DFT average-atom code
• attendance-model — An end to end machine learning app to predict canteens attendance 2-3 weeks ahead in Nantes Metropole
• audiblez — Generate audiobooks from e-books (epub to wav/m4b)
• audiocaps-download — This package aims at simplifying the download of the AudioCaps dataset.
• audiolm-pytorch — AudioLM - Language Modeling Approach to Audio Generation from Google Research - Pytorch
• audiolm-superfeel — AudioLM - Language Modeling Approach to Audio Generation from Google Research - Pytorch
• audioset-download — This package aims at simplifying the download of the AudioSet dataset.
• audioset-strong-download — This package aims at simplifying the download of the strong version of AudioSet dataset. This is a revised version of audioset-download (https://github.com/MorenoLaQuatra/audioset-download).
• audiossl — no summary
• audiozen — Audio ZEN is a library for audio/speech signal processing.
• auger.ai.predict — Auger ML predict python and command line interface
• augly — A data augmentations library for audio, image, text, & video.
• aurora-tr — Translate via aurora and variants, openai, azure openai, etc.
• auto-learn-gpt — autoML for training and inference Deep Learning model
• auto-modelling — A light package for automatic model tuning and stacking
• auto-surprise — A python package that automates algorithm selection and hyperparameter tuning for the recommender system library Surprise
• auto-tensorflow — Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code. To make Deep Learning on Tensorflow absolutely easy for the masses with its low code framework and also increase trust on ML models through What-IF model explainability.
• auto-uncertainties — Linear Uncertainty Propagation with Auto-Differentiation
• autoballs — Python package for segmentation of axons and morphological analysis.
• autobiasdetector — tools for detecting bias patterns of LLMs
• autobmt — a modeling tool that automatically builds scorecards and tree models.
• autocat — Tools for automated generation of catalyst structures and sequential learning
• AutoClassifierRegressor — Tools for getting analysis of all classifiers and regressors
• autocluster — Automated machine learning toolkit for performing clustering tasks.
• autodmri — Implementation of "Automated characterization of noise distributions in diffusion MRI data".
• autoemulate — A python package for semi-automated emulation
• autofeat — Automatic Feature Engineering and Selection Linear Prediction Model
• Autofhm — Python Automated Machine Learning
• autogluon-assistant — ML Assistant for Competitive Machine Learning
• autogluon.timeseries — Fast and Accurate ML in 3 Lines of Code
• autolab-core — Core utilities for the Berkeley AutoLab
• autolgbm — autolgbm: tuning lightgbm with optuna
• automate-machinelearning — A library package used to automate the machine learning on regression and classification problems.
• automated-accessibility-testing — package with the intention of automating accessibility tests for web development
• autoMLF — autoML for training and inference Deep Learning model