Tools and utilities shared by Deisa backends.

Hydra is an open-source Python framework that simplifies the development of research and other complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line.

Key features:

• Hierarchical configuration composable from multiple sources

• Configuration can be specified or overridden from the command line

• Dynamic command line tab completion

• Run your application locally or launch it to run remotely

• Run multiple jobs with different arguments with a single command

Bracex is a brace expanding library (à la Bash) for Python. Brace expanding is used to generate arbitrary strings.

Chaco is a Python package for building interactive and custom 2-D plots and visualizations. Chaco facilitates writing plotting applications at all levels of complexity, from simple scripts with hard-coded data to large plotting programs with complex data interrelationships and a multitude of interactive tools. While Chaco generates attractive static plots for publication and presentation, Chaco differs from tools like Matplotlib in that it also works well for dynamic interactive data visualization and exploration.

This package provides a small utility for simplifying and cleaning up argument parsing scripts.

This package provides functions for 3D coordinate transformations.

Compute a single hash of the file contents of a directory.

Python meta-package for deisa-dask and deisa-ray.

df2onehot is a Python package to convert unstructured DataFrames into structured dataframes, such as one-hot dense arrays.

ismember is a Python library that checks whether a particular element belongs to an array.

pypickle is a user-friendly Python library for saving and loading data using the pickle format. Unlike the standard pickle module, pypickle puts safety first—offering built-in validation, extension checks, and protection against common exploits. Whether you're persisting models, storing session data, or sharing files, pypickle makes serialization easy and more secure.

JAXopt provides hardware accelerated, batchable and differentiable optimizers in JAX.

1. Hardware accelerated: the implementations run on GPU and TPU, in addition to CPU.

2. Batchable: multiple instances of the same optimization problem can be automatically vectorized using JAX’s vmap.

3. Differentiable: optimization problem solutions can be differentiated with respect to their inputs either implicitly or via autodiff of unrolled algorithm iterations.

TensorFlow is a flexible platform for building and training machine learning models. It provides a library for high performance numerical computation and includes high level Python APIs, including both a sequential API for beginners that allows users to build models quickly by plugging together building blocks and a subclassing API with an imperative style for advanced research.

JAX is Autograd and XLA, brought together for high-performance numerical computing, including large-scale machine learning research. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.

ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.

JAX is Autograd and XLA, brought together for high-performance numerical computing, including large-scale machine learning research. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.

The h5py package provides both a high- and low-level interface to the HDF5 library from Python. The low-level interface is intended to be a complete wrapping of the HDF5 API, while the high-level component supports access to HDF5 files, datasets and groups using established Python and NumPy concepts.

TensorFlow is a flexible platform for building and training machine learning models. It provides a library for high performance numerical computation and includes high level Python APIs, including both a sequential API for beginners that allows users to build models quickly by plugging together building blocks and a subclassing API with an imperative style for advanced research.

ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.

Optax is a gradient processing and optimization library for JAX.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.

NumPyro is a lightweight probabilistic programming library that provides a NumPy backend for Pyro. It relies on JAX for automatic differentiation and JIT compilation to GPU / CPU.

Chex is a library of utilities for helping to write reliable JAX code. This includes utils to help:

• Instrument your code (e.g. assertions)

• Debug (e.g. transforming pmaps in vmaps within a context manager).

• Test JAX code across many variants (e.g. jitted vs non-jitted).

This is a collection of independent Python modules providing utilities for various projects.