Pyface contains toolkit-independent GUI abstraction layers, used to support the TraitsUI package. Thus, you can write code in the abstraction of the Pyface API and the selected toolkit and backend take care of the details of displaying them.

Envisage is a Python-based framework for building extensible applications, that is, applications whose functionality can be extended by adding 'plug-ins. Envisage provides a standard mechanism for features to be added to an application, whether by the original developer or by someone else. In fact, when you build an application using Envisage, the entire application consists primarily of plug-ins. In this respect, it is similar to the Eclipse and Netbeans frameworks for Java applications.

xsData is a complete data binding library for python allowing developers to access and use XML and JSON documents as simple objects rather than using DOM. The included XML and JSON parser/serializer are highly optimized and adaptable, with multiple handlers and configuration properties. xsData is constantly tested against the W3C XML Schema 1.1 test suite.

The TraitsUI project provides a toolkit-independent GUI abstraction layer, which is used to support the “visualization” features of the Traits package. You can write a model using the Traits API and specify a GUI using the TraitsUI API (views, items, editors, etc.), and let TraitsUI and your selected toolkit back-end (Qt or Wx) take care of the details of displaying them.

Dask backend for Deisa.

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

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

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.

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

This library implements support for mixed precision training in JAX. It provides two key abstractions. These abstractions are mixed precision policies and loss scaling.

Optax is a gradient processing and optimization library for JAX.

This module contains classes for the object model defined by the Static Analysis Results Interchange Format (SARIF) file format.

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.

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.

This package provides tools for Makefile execution powered by pure Python.

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).

Protocol buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data.

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.

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.

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

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.