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Confit is a complete and easy-to-use configuration framework aimed at improving the reproducibility of experiments by relying on the Python typing system, minimal configuration files and command line interfaces.
This module contains classes for the object model defined by the Static Analysis Results Interchange Format (SARIF) file format.
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.
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.
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
pmapsinvmapswithin a context manager).Test JAX code across many
variants(e.g. jitted vs non-jitted).
This library implements support for mixed precision training in JAX. It provides two key abstractions. These abstractions are mixed precision policies and loss scaling.
ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.
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.
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.
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.
Optax is a gradient processing and optimization library for JAX.
This is a collection of independent Python modules providing utilities for various projects.
JAXopt provides hardware accelerated, batchable and differentiable optimizers in JAX.
Hardware accelerated: the implementations run on GPU and TPU, in addition to CPU.
Batchable: multiple instances of the same optimization problem can be automatically vectorized using JAX’s
vmap.Differentiable: optimization problem solutions can be differentiated with respect to their inputs either implicitly or via autodiff of unrolled algorithm iterations.
TypeScript definitions for morgan
XML language support for the CodeMirror code editor
Use react-devtools outside of the browser
Compile ES2015 destructuring to ES5
TypeScript definitions for use-sync-external-store