Search Results for "xray-core" - Page 3
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Showing 98 open source projects for "xray-core"
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DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. ... -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. ... -
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CommandlineConfig
A library for users to write configurations in Python
...It lets you define configuration in familiar Python dictionaries or JSON files and then access nested parameters via dot notation in code, improving readability and reducing boilerplate. One of its core strengths is the ability to override configuration values directly from the command line, making it convenient to run many experimental variants without editing files repeatedly. The library supports arbitrarily deep nested structures, type handling, enumerated value constraints, and even tuple types, which are common in ML experiment setups. ... -
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Jraph
A Graph Neural Network Library in Jax
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time compilation. The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. ... -
Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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tom_core
tom_core - a tool for automating events on a computer
tom_core is a software tool used for the automation of everything that happens on your computer. By using this application, you can easily record your activity on your computer, starting the recording at any moment that you choose. The application repeats all your clicks or drags, keystrokes, hotkeys, etc. All in exactly the timing and number of repetitions you need. The toolbox such as the optical recognition and voice control enables to branch out the recordings into complex forms, with... -
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...This formalism is an extension of the Shlaer–Mellor method and executable UML, facilitating deployment of domains across multiple hosts, using networked bridges to process and pass events between them. Core technologies: XML, Python, Qt/PySide. Current release solely targets Linux, but has previously run on macOS. Currently released components are: - eda-model: XML schema and schematron that define the storage of a valid design. - eda-model-interface: python3 library for loading, editing, diffing and validating an instance of an EDA design...
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TorchGAN
Research Framework for easy and efficient training of GANs
The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to provide building blocks for popular GANs and also to allow customization for cutting-edge research. ... -
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Thesa
It is a Platform to connect to tryton (json-rpc) and is based on qt
Thesa It is a Platform to connect to tryton (json-rpc) and is based on qt/qml libraries. Requires designing the interface of each Tab without having to touch the core. Tabs are created with qml files and can be loaded locally from a folder or from trytond using thesamodule (https://github.com/numaelis/thesamodule). Thesa's goal is to be able to combine tryton with Qt / Qml, for special cases such as using the opengl performance of qml2 Requirements: pyside2 5.12 or higher: https://download.qt.io/official_releases/QtForPython/pyside2/ Run: python3 main.py you can find source code in: https://github.com/numaelis/thesa -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
...We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. Each base class supports a different data format. -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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Behaviour Suite Reinforcement Learning
bsuite is a collection of carefully-designed experiments
bsuite is a research framework developed by Google DeepMind that provides a comprehensive collection of experiments for evaluating the core capabilities of reinforcement learning (RL) agents. Its main goal is to identify, measure, and analyze fundamental aspects of learning efficiency and generalization in RL algorithms. The library enables researchers to benchmark their agents on standardized tasks, facilitating reproducible and transparent comparisons across different approaches. ... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a... -
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AWS IoT Greengrass Core SDK
SDK to use with functions running on Greengrass Core using Python
The AWS IoT Greengrass Core SDK is meant to be used by AWS Lambda functions running on an AWS IoT Greengrass Core. It will enable Lambda functions to invoke other Lambda functions deployed to the Greengrass Core, publish messages to the Greengrass Core and work with the local Shadow service. To use the AWS IoT Greengrass Core SDK, you must first import the AWS IoT Greengrass Core SDK in your Lambda function as you would with any other external libraries. ... -
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SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
...With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well. -
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EasyArch
Arch Linux Installer ISO
A simple yet full featured Archlinux installer ISO. Minimum system requirements- Processor - 2 core 64 bit Ram - 1 GB HDD space - 10 GB If you are looking for another linux distro, then you are at wrong place, this is not a new or separate distribution. It is just a live ISO to provide simple and easy way to get Archlinux up and running in very little time and with or without internet connection. Yes, you read it right, you can install Archlinux without internet with this ISO. ... -
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interactive-coding-challenges
120+ interactive Python coding interview challenges
Interactive Coding Challenges is a collection of practice problems designed to strengthen data structures, algorithms, and problem-solving skills. The repository emphasizes a learn-by-doing approach: you read a prompt, attempt a solution, and verify behavior with tests, often within notebooks or scripts. Problems span arrays, strings, stacks, queues, linked lists, trees, graphs, dynamic programming, and more, mirroring common interview themes. Many challenges include hints and reference... -
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TACO is a toolkit for building distributed control systems or any other distributed system. It is based on a C/C++ core. It is based on the client-server model. It supports writing clients and server on Unix+Windows. Clients and servers can be written in
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Flask-GraphQL
Adds GraphQL support to your Flask application
...If you are using the Schema type of Graphene library, be sure to use the graphql_schema attribute to pass as schema on the GraphQLView view. Otherwise, the GraphQLSchema from graphql-core is the way to go. The GraphQLSchema object that you want the view to execute when it gets a valid request. A value to pass as the context_value to graphql execute function. By default is set to dict with request object at key request. The root_value you want to provide to graphql execute. -
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setuplib
Extensions for setuptools - detailed information on entry points
The *setuplib* package provides core functions for the query of meta information and installation repositories of *Python* packages. It provides query and filter options on the installed packages and the available information, while displaying the result data in various formats, e.g. as table, list, or JSON, XML, YAML, CSV, etc. The provided commands and extension points integrate seamless into the standard *setuptools* and/or *distutils*. -
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Rasa Core
Rasa Core is now part of the Rasa repo
Rasa is an open source machine learning framework to automate text and voice-based conversations. With Rasa, you can build contextual assistants. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forth. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed – Rasa enables you to build assistants that can do this in a... -
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AeroPython
Classical Aerodynamics of potential flow using Python
The AeroPython series of lessons is the core of a university course (Aerodynamics-Hydrodynamics, MAE-6226) by Prof. Lorena A. Barba at the George Washington University. The first version ran in Spring 2014 and these Jupyter Notebooks were prepared for that class, with assistance from Barba-group PhD student Olivier Mesnard. In Spring 2015, we revised and extended the collection, adding student assignments to strengthen the learning experience. -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more robust to noise and adversarial examples. This repository implements mixup for the CIFAR-10 dataset, showcasing its effectiveness in improving generalization, stability, and calibration of neural networks. ... -
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TensorFlow-ZH
Chinese version of the official document of TensorFlow
The tensorflow-zh repository is a Chinese translation of the official TensorFlow documentation, organized to make the core guides, tutorials, and reference material accessible to Chinese speakers. It was initiated shortly after TensorFlow’s open-sourcing, with translation and proofreading contributions from a community of volunteers who aimed to bridge the language barrier for learners in China and other Mandarin communities. The repo mirrors the structure of the original English docs: chapters, sections, code examples, API references, and supplementary content like configuration and build guides. ... -
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jsondata
Modular JSON by trees and branches, pointers and patches
The 'jsondata' package provides for the modular in-memory processing of JSON data by trees, branches, pointers, and patches. The main interface classes are: - JSONData - Core for RFC7159 based data structures. Provides modular data components. - JSONDataSerializer - Core for RFC7159 based data persistence. Provides modular data serialization. - JSONPointer - RFC6901 for addressing by pointer paths. Provides pointer arithmetics. - JSON Relative Pointer - draft-handrews-relative-json-pointer/2018, contained in JSONPointer... -
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Rekall
Rekall Memory Forensic Framework
...The design emphasizes repeatability: investigators run well-defined analyses that produce timelines, indicators, and reports suitable for case work or automation. Rekall supports profile-free operation for many targets, reducing setup friction and making it easier to handle varied images in the field. Extensibility is a core theme, with a plugin API and notebook-friendly workflows for custom hunts and triage. Used well, it compresses what would be hours of manual sleuthing into scripted passes over a consistent object model. -
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Flasky
Companion code to my O'Reilly book "Flask Web Development"
...The project shows how to organize a Flask application into reusable blueprints, configure environment-specific settings, integrate SQL databases via SQLAlchemy, and manage migrations. Beyond the core web functionality, Flasky illustrates testing strategies using Python’s unittest framework, including tests for models, views, and authentication flows to promote test-driven development.
