HTTP and HTTPS modules that follow redirects.

Jupyter telemetry library

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.

LDAP Authenticator for JupyterHub

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.

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

JupyterHub: A multi-user server for Jupyter notebooks

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

The systemdspawner enables JupyterHub to spawn single-user notebook servers using systemd.

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.

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.

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

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.

pyRiemann is a Python machine learning package based on scikit-learn API. It provides a high-level interface for processing and classification of real (resp. complex)-valued multivariate data through the Riemannian geometry of symmetric (resp. Hermitian) positive definite (SPD) (resp. HPD) matrices.

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

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.

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.

Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16. It abstracts exactly and only the boilerplate code related to multi-GPUs/TPU/fp16 and leaves the rest of your code unchanged.

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.

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.

PyTorch extension for handling deeply nested sequences of variable length.

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.

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.

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.