Search Results for "accessible-coconut"

Developed by Kitware, trame is a Python-based framework that allows developers to create web applications with desktop-like functionality. It enables the integration of various components and technologies, such as VTK and ParaView, into web applications written entirely in Python. With best-in-class platforms at its core, trame provides complete control of 3D visualizations and data processing. Developers benefit from a write-once environment from trame. trame is an open source project...

...Enter the weights and reps you've done for each exercise to generate diagrams that let you see at a glance how well you're doing. Of course, the raw numbers are still accessible.

...The vision of the Apache TVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. Compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet and more. Start using TVM with Python today, build out production stacks using C++, Rust, or Java the next day.

Grassroots DiCoM is a C++ library for DICOM medical files. It is accessible from Python, C#, Java and PHP. It supports RAW, JPEG, JPEG 2000, JPEG-LS, RLE and deflated transfer syntax. It comes with a super fast scanner implementation to quickly scan hundreds of DICOM files. It supports SCU network operations (C-ECHO, C-FIND, C-STORE, C-MOVE). PS 3.3 & 3.6 are distributed as XML files. It also provides PS 3.15 certificates and password based mecanism to anonymize and de-identify DICOM datasets.

...Unlike traditional computer vision libraries that focus solely on modular components, Classy Vision provides a complete and unified framework, featuring distributed training, reproducible experiments, and flexible configuration tools. It offers high performance and scalability—capable of training models like ResNet-50 on ImageNet in just minutes—while remaining accessible to both researchers and production engineers. The library integrates seamlessly with PyTorch Hub for easy access to pretrained models and supports elastic training using PyTorch Elastic, making distributed training robust to changes in cluster resources or hardware failures.

We use nginx in our company lab environment. It often happens that my colleagues have developed an application that is now deployed in our Stage or Prod environment. To make this application accessible nginx has to be adapted. Most of the time my colleagues don't have permission to access the server and change the configuration files and since I don't feel like doing this for everyone anymore I thought a UI could help us all. If you feel the same way I wish you a lot of fun with the application and I am looking forward to your feedback, change requests or even a star. ...

The Golem Network fosters a global group of creators building ambitious software solutions that will shape the technological landscape of future generations by accessing computing resources across the platform. Golem Network is an accessible, reliable, open access and censorship-resistant protocol, democratizing access to digital resources and connecting users through a flexible, open-source platform. With Golem Network, users can connect with ease and pay each other for sharing their unused resources. Golem’s democratized access combined with a unique peer-to-peer exchange creates an unstoppable ecosystem for a myriad of use-cases to be born, allowing software developers to leverage their creativity more than ever before. ...

...It provides a secure and intuitive environment for performing computations on encrypted data using Secure Multiparty Computation (SMPC). Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. Its design mirrors PyTorch’s modular and library-based structure, enabling flexible experimentation, debugging, and model development. ...