9 Components & Libraries
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Computational analysis tools have revolutionized the way we design engineering systems, but most established codes are proprietary, unavailable, or prohibitively expensive for many users. The SU2 tea…
Covalent is a Python library for AI/ML engineers, developers, and researchers. It provides a straightforward approach to running compute jobs, like LLMs, generative AI, and scientific research, on v…
Lint Review helps automate a tedious part of code review - enforcing coding standards. By using the GitHub API Lint Review runs a repository's configured linters and updates pull requests with line c…
Exploratory data analysis (EDA) is a prerequisite for all data science, as illustrated by the ubiquity of Jupyter notebooks, the preferred interface for EDA among data scientists. The operations invo…
Conduit is an open source project from Lawrence Livermore National Laboratory that provides an intuitive model for describing hierarchical scientific data in C++, C, Fortran, and Python. It is used f…
Heat is a distributed tensor framework for high performance data analytics. With Heat you can: If you need a functionality that is not yet supported: It should print something like this: In order to …
Existing scripts should be migrated to pyomnisci from pymapd, this library will not be updated moving forward. Packages are available on conda-forge and PyPI: We recommend creating a fresh conda 3.7 …
With Entangle you can run simple, hardware parallelized code with conditional logic that looks like this. or train two AI models in parallel using tensorflow container utilizing dedicated CPU and GPU…
In the realm of computer vision 📷, digital image restoration and enhancement techniques have established themselves as indispensable pillars. These techniques, aiming to restore and elevate the qual…
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