Likwid is a simple to install and use toolsuite of command line applications and a library for performance oriented programmers. It works for Intel, AMD, ARMv8 and POWER9 processors on the Linux operating system. There is additional support for Nvidia GPUs. There is support for ARMv7 and POWER8 but there is currently no test machine in our hands to test them properly. Note that in this recipe we compile the package with ACCESSMODE=perf_event because we lack the root privileges on HPC platforms. One of the implications is that thermic measures are disabled. See the Wiki on the home page of the package for more details.

Dash table.

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.

Bootstrap themed components for use in Plotly 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 a minimal version without the dashboard functionality. This is the fork of Marek Felšöci.

Python client library for visual regression testing with Percy (https://percy.io).

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.

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.

Core component suite for Dash.

This package provides a dash component for specifying raw HTML.

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

Example of a Dash library that uses Flow Types.

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.

Deferring loading of JS files until after React loads.

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 a minimal version without the dashboard functionality.

image and video datasets and models for torch deep learning

Generates LaTeX source from Python functions.

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.

Export data as binary VTK files

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).

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.

This package provides a complete GCC tool chain for Fortran development to be installed in user profiles. This includes gfortran, as well as libc (headers and binaries, plus debugging symbols in the debug output), and binutils.