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.

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.

Melissa is a file-avoiding, adaptive, fault-tolerant and elastic framework, to run large-scale sensitivity analysis or deep-surrogate training on supercomputers. This package builds the API used when instrumenting the clients.

Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.

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.

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.

PyDMD is a Python package designed for Dynamic Mode Decomposition (DMD), a data-driven method used for analyzing and extracting spatiotemporal coherent structures from time-varying datasets. It provides a comprehensive and user-friendly interface for performing DMD analysis, making it a valuable tool for researchers, engineers, and data scientists working in various fields.

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

Library implementing Block-GMres with Inexact Breakdown and Deflated Restarting, Breakdown Free Block Conjudate Gradiant, Block General Conjugate Residual and Block General Conjugate Residual with Inner Orthogonalization and with inexact breakdown and deflated restarting.

The Basic Linear Algebra Subprograms (BLAS) have been around for many decades and serve as the de facto standard for performance-portable and numerically robust implementation of essential linear algebra functionality. The objective of BLAS++ is to provide a convenient, performance oriented API for development in the C++ language, that, for the most part, preserves established conventions, while, at the same time, takes advantages of modern C++ features, such as: namespaces, templates, exceptions, etc.

The Linear Algebra PACKage (LAPACK) is a standard software library for numerical linear algebra. The objective of LAPACK++ is to provide a convenient, performance oriented API for development in the C++ language, that, for the most part, preserves established conventions, while, at the same time, takes advantages of modern C++ features, such as: namespaces, templates, exceptions, etc.

Grace is a 2D plotting tool for the X Window System. It has a Motif-based GUI and a scripting language that includes curve fitting, analysis, and export capabilities.

STIR is an object-oriented framework for tomographic image reconstruction, with an emphasis on iterative reconstruction in PET and SPECT. This package includes the C++ core and Python bindings.

MVAPICH2 (pronounced as “em-vah-pich 2”) is an open-source MPI software to exploit the novel features and mechanisms of high-performance networking technologies (InfiniBand, iWARP, RDMA over Converged Enhanced Ethernet (RoCE v1 and v2), Slingshot 10, and Rockport Networks) and deliver best performance and scalability to MPI applications.

MVAPICH (pronounced as “em-vah-pich”) is an open-source MPI software to exploit the novel features and mechanisms of high-performance networking technologies (InfiniBand, iWARP, RDMA over Converged Enhanced Ethernet (RoCE v1 and v2), Slingshot 10, and Rockport Networks) and deliver best performance and scalability to MPI applications. MVAPICH 4.1 has support for the Cray Slingshot 11, Cornelis OPX, and Intel PSM3 interconnects through the OFI libfabric library, and for the UCX communication library.

Gifticlib is a a library for reading and writing files in GIfTI format. GIfTI is a standard for Geometry Data Format for Exchange of Surface-Based Brain Mapping Data.

CiftiLib is a C++ library for CIFTI-2 file reading/writing. It additionally supports CIFTI-1 files, and supports both on-disk and in-memory access. It also provides C++ code for reading and writing generic NIfTI-1 and NIfTI-2 files.

CIFTI (Connectivity Informatics Technology Initiative) standardizes file formats for the storage of connectivity data. These formats are developed by the Human Connectome Project and other interested parties.

See http://www.nitrc.org/projects/cifti/ for more information.

Convert3d is a command-line tool for converting 3D images between common file formats. The tool also includes a growing list of commands for image manipulation, such as thresholding and resampling. The tool can also be used to obtain information about image files.

The nipype1-workflows repository contains legacy workflows from Nipype 1.x, showcasing nearly a decade of development in neuroimaging data processing and analysis.

This package provides a Pydantic schema for BIDS Stats Models.

This package provides utilities for feature analysis, preprocessing and visualization of image quality metrics generated by MRIQC.

This package provides the Python Client code for accessing neuroimaging templates hosted using TemplateFlow.

The BIDS Validator is a web application, command-line utility, and Javascript/Typescript library for assessing compliance with the BIDS standard.

NARPS Open Pipelines is a project aimed at reproducing the 70 pipelines from the NARPS study (Botvinik-Nezer et al., 2020) and sharing them as an open resource for the community. It uses Nipype for workflow management and provides templates to facilitate the reproduction of neuroimaging analyses.