This package includes WrapSpawner and ProfilesSpawner, which provide mechanisms for runtime configuration of spawners. The inspiration for their development was to allow users to select from a range of pre-defined batch job profiles, but their operation is completely generic.

An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture.

LDAP Authenticator for JupyterHub

The SudoSpawner enables JupyterHub to spawn single-user servers without being root, by spawning an intermediate process via sudo, which takes actions on behalf of the user.

Jupyter telemetry library

This package provides a spawner for Jupyterhub to spawn notebooks using batch resource managers.

AlphaFold is an AI system developed by DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.

clustimage is a Python library to detect natural groups or clusters of images. Multiple steps are pipelined where images are processed, features extracted, and the clusters evaluated across the feature space. The optimal number of clusters is determined using methods such as silhouette, dbindex, and derivatives in combination with clustering methods, such as agglomerative, kmeans, dbscan and hdbscan. clustimage allows you to determine the most robust clustering by efficiently searching across the parameters and by evaluating the clusters. Besides clustering of images, clustimage can also find the most similar images for a new, unseen sample.

imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.

This package provides a set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques.

Python bindings for KeOps, on CPUs and GPUs, with autodiff and without memory overflows.

This package is an integration module of Optuna, an automatic Hyperparameter optimization software framework. The modules in this package provide users with extended functionalities for Optuna in combination with third-party libraries such as PyTorch, sklearn, and TensorFlow.

The library classeval is developed to evaluate the models performance of any kind of two-class or multi-class model. classeval computes many scoring measures in case of a two-class clasification model. Some measures are utilized from sklearn, among them AUC, MCC, Cohen kappa score, matthews correlation coefficient, whereas others are custom. This library can help to consistenly compare the output of various models. In addition, it can also give insights in tuning the models performance as the the threshold being used can be adjusted and evaluated. The output of classeval can subsequently plotted in terms of ROC curves, confusion matrices, class distributions, and probability plots. Such plots can help in better understanding of the results.

Python front-end in charge of orchestrating the execution a Melissa based study. It automatically handles large-scale scheduler interactions in OpenMPI and with common cluster schedulers (e.g. slurm or OAR).

Flax is a neural network library for JAX that is designed for flexibility.

Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only). Effortlessly build and train models for computer vision, natural language processing, audio processing, timeseries forecasting, recommender systems, etc.

• Accelerated model development: Ship deep learning solutions faster thanks to the high-level UX of Keras and the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.

• State-of-the-art performance: By picking the backend that is the fastest for your model architecture (often JAX!), leverage speedups ranging from 20% to 350% compared to other frameworks.

• Datacenter-scale training: Scale confidently from your laptop to large clusters of GPUs or TPUs.

keopscore is the KeOps meta programming engine. This python module should be used through a binder (e.g. pykeops or rkeops).

Equinox is a comprehensive JAX library that provides a wide range of tools and features not found in core JAX, including neural networks with PyTorch-like syntax, filtered APIs for transformations, PyTree manipulation routines, and advanced features like runtime errors.

EZyRB is a python library for the Model Order Reduction based on baricentric triangulation for the selection of the parameter points and on Proper Orthogonal Decomposition for the selection of the modes.

Skigen is a header-only C++ template library for machine learning, built on Eigen. It brings the scikit-learn API — fit(), transform(), predict() — to native C++.

ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-parametric models. ruptures focuses on ease of use by providing a well-documented and consistent interface. In addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package.

PyThresh is a comprehensive and scalable Python toolkit for thresholding outlier detection likelihood scores in univariate/multivariate data. It has been written to work in tandem with PyOD and has similar syntax and data structures. However, it is not limited to this single library.

PyThresh is meant to threshold likelihood scores generated by an outlier detector. It thresholds these likelihood scores and replaces the need to set a contamination level or have the user guess the amount of outliers that may exist in the dataset beforehand. These non-parametric methods were written to reduce the user's input/guess work and rather rely on statistics instead to threshold outlier likelihood scores. For thresholding to be applied correctly, the outlier detection likelihood scores must follow this rule: the higher the score, the higher the probability that it is an outlier in the dataset. All threshold functions return a binary array where inliers and outliers are represented by a 0 and 1 respectively.

PyThresh includes more than 30 thresholding algorithms. These algorithms range from using simple statistical analysis like the Z-score to more complex mathematical methods that involve graph theory and topology.

aGrUM is a C++ library for graphical models. It is designed for easily building applications using graphical models such as Bayesian networks, influence diagrams, credal networks, Markov random fields, decision trees, GAI networks, (Factored) Markov decision processes, etc.

Features:

• Dedicated data structures

• Lightweight directed/undirected graphs

• Extensible multidimensional matrix

• Bayesian Network algorithms

• Research tools (random generation, introspection)

• Integration tools (listeners, multiple formats)

skops is a Python library helping you share your scikit-learn based models and put them in production.