One of the most advanced WSGI utility modules. It includes a powerful debugger, full-featured request and response objects, HTTP utilities to handle entity tags, cache control headers, HTTP dates, cookie handling, file uploads, a powerful URL routing system and a bunch of community-contributed addon modules.

gcvb (generate compute validate benchmark) is a Python 3 module aiming at facilitating non-regression, validation and benchmarking of simulation codes. gcvb is not a complete tool of continuous integration (CI). It is rather a component of the testing part of a CI workflow. It can compare the different metrics of your computation with references that can be a file, depends of the 'configuration' or are absolute. This is a minimal version without the dashboard functionality. This is the fork of Marek Felšöci.

Deferring loading of JS files until after React loads.

This package provides a Python framework for building reactive web-apps. Developed by Plotly.

Vanilla HTML components for Dash.

gcvb (generate compute validate benchmark) is a Python 3 module aiming at facilitating non-regression, validation and benchmarking of simulation codes. gcvb is not a complete tool of continuous integration (CI). It is rather a component of the testing part of a CI workflow. It can compare the different metrics of your computation with references that can be a file, depends of the 'configuration' or are absolute. This is the fork of Marek Felšöci.

This package provides a dash component for specifying raw HTML.

Flask is a micro web framework based on the Werkzeug toolkit and Jinja2 template engine. It is called a micro framework because it does not presume or force a developer to use a particular tool or library.

Bootstrap themed components for use in Plotly Dash.

Example of a Dash library that uses Flow Types.

Dash table.

Core component suite for Dash.

gcvb (generate compute validate benchmark) is a Python 3 module aiming at facilitating non-regression, validation and benchmarking of simulation codes. gcvb is not a complete tool of continuous integration (CI). It is rather a component of the testing part of a CI workflow. It can compare the different metrics of your computation with references that can be a file, depends of the 'configuration' or are absolute.

Python implementation of the Tensor Train (TT) toolbox. It contains several important packages for working with the TT-format in Python. It is able to do TT-interpolation, solve linear systems, eigenproblems, solve dynamical problems. Several computational routines are done in Fortran (which can be used separately), and are wrapped with the f2py tool.

Access dict values as attributes (works recursively).

Export data as binary VTK files

image and video datasets and models for torch deep learning

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost. For usage of ODE solvers in deep learning applications.

As the solvers are implemented in PyTorch, algorithms in this repository are fully supported to run on the GPU.

Generates LaTeX source from Python functions.

Mini-chameleon is an educational purpose dense linear algebra solver. As provided, it essentially provides drivers while the actual computational routines remain to be completed. The goal is to implement a dense matrix-matrix product and an LU factorization, first targeting a sequential implementation, followed by an simd version, a shared-memory openmp one, a distributed memory MPI one, an MPI+openmp one and a runtime-based starpu one.

pmtool aims at performing post-mortem analyses of the behavior of StarPU applications. Provide lower bounds on makespan. Study the performance of different schedulers in a simple context. Limitations: ignore communications for the moment; branch comms attempts to remove this limitation.

PaRSEC is a generic framework for architecture aware scheduling and management of micro-tasks on distributed many-core heterogeneous architectures.

Chameleon is a dense linear algebra solver relying on sequential task-based algorithms where sub-tasks of the overall algorithms are submitted to a run-time system. Such a system is a layer between the application and the hardware which handles the scheduling and the effective execution of tasks on the processing units. A run-time system such as StarPU is able to manage automatically data transfers between not shared memory area (CPUs-GPUs, distributed nodes).