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

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.

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation and providing a delightful developer experience.

Haiku is a simple neural network library for JAX. It is developed by some of the authors of Sonnet, a neural network library for TensorFlow.

PyTorch extension for handling deeply nested sequences of variable length.

This is a minimum version for checking the input argument dict. It would examine argument's type, as well as keys and types of its sub-arguments. A special case called variant is also handled, where you can determine the items of a dict based the value of on one of its flag_name key.

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.

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute. These are the provided Ray AI libraries:

• Data: Scalable datasets for ML;

• Train: Distributed training;

• Tune: Scalable hyperparameter tuning;

• RLlib: Scalable reinforcement learning;

• Serve: Scalable and programmable serving.

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).

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.

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters.

This package provides a Python library for outlier and anomaly detection, integrating classical and deep learning techniques .

TensorStore is a C++ and Python software library designed for storage and manipulation of large multi-dimensional arrays that:

• Provides advanced, fully composable indexing operations and virtual views.

• Provides a uniform API for reading and writing multiple array formats, including zarr and N5.

• Natively supports multiple storage systems, such as local and network filesystems, Google Cloud Storage, Amazon S3-compatible object stores, HTTP servers, and in-memory storage.

• Offers an asynchronous API to enable high-throughput access even to high-latency remote storage.

• Supports read caching and transactions, with strong atomicity, isolation, consistency, and durability (ACID) guarantees.

• Supports safe, efficient access from multiple processes and machines via optimistic concurrency.

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.

This package provides a set of custom transformers, metrics and models complementing scikit-learn, which results from a collaboration between multiple companies in the Netherlands.

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute. These are the provided Ray AI libraries:

• Data: Scalable datasets for ML;

• Train: Distributed training;

• Tune: Scalable hyperparameter tuning;

• RLlib: Scalable reinforcement learning;

• Serve: Scalable and programmable serving.

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.

This package provides a neural network library for PyTorch compatible with the scikit-learn API.

Datasets is a lightweight library providing access to major public datasets (image, audio, text, etc.), as well as enabling efficient data preparation for inspection and ML model evaluation and training.

Orbax is a namespace providing common utility libraries for JAX users. Orbax also includes a serialization library for JAX users, enabling the exporting of JAX models to the TensorFlow SavedModel format.

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

Basix is a finite element definition and tabulation runtime library.

Basix allows users to:

• evaluate finite element basis functions and their derivatives at a set of points;

• access geometric and topological information about reference cells;

• apply push forward and pull back operations to map data between a reference cell and a physical cell;

• permute and transform DOFs to allow higher-order elements to be use on arbitrary meshes;

• interpolate into and between finite element spaces.

Basix includes a range of built-in elements, and also allows the user to define their own custom elements.

This package provides the Python wrapper for Basix.

DOLFINx is the computational environment of FEniCSx and implements the FEniCS Problem Solving Environment in C++ and Python.

This package provides the C++ interface.