Search Results for "fb2k-component" - Page 3
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Showing 141 open source projects for "fb2k-component"
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1
LX Linux
A light version of Debian with minimal installed using LXDE.
LX Linux is a distro based on Debian using LXDE as DE with some customization. The installation process uses Calamares. Recommended for very old machines, like 15 years old or more. live password: live Come with some extra repositories to install 3rd part packages (optional). - If brightness keys do not work, open a terminal: sudo nano /etc/default/grub where GRUB_CMDLINE_LINUX_DEFAULT="xxxxx" put acpi_backlight=video or acpi_backlight=vendor try one or another and test... -
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RSM
Radiation Spectrum Method : a modal BPM (Beam Propagation Method)
...This permits, with the interpretation of the guided and radiation modes spectrum, a physical understanding of the propagation mechanisms in the integrated optical device under evaluation. The complex geometry of the component is discretized in a stack of multilayer dielectric waveguides. For Windows, download "RSM_visit_update2.zip" , this file needs the last version of the software be first installed : "RSM VisitSetup2.ex -
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LaSolv
Solves symbolic electrical AC circuit equations
In electrical engineering, AC circuits are often used in the design process. However, deriving the gain, input impedance or what have you is tedious and error prone. LaSolv takes a SPICE like description of your circuit and solves for whatever parameter you specify- voltage gain, trans-impedance, input impedance, etc. -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
...Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without changing the code base. For example, You can add EfficientNet as the backbone, just add efficient_net.py (ALREADY ADDED) and register it, specific it in the config file, It's done! Smooth and enjoyable training procedure: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training CMD. -
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Summarize from Feedback
Code for "Learning to summarize from human feedback"
...Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling component, and the reinforcement learning (or preference-based fine-tuning) phase. The repo also includes utilities for dataset handling, modeling architectures, inference, and evaluation. Because the codebase is experimental, parts of it may not run out-of-box depending on dependencies or environment, but it remains a canonical reference for how to implement summarization via human feedback. -
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SageMaker Experiments Python SDK
Experiment tracking and metric logging for Amazon SageMaker notebooks
...Add Trials to an Experiment that you wish to compare together. Trial: A description of a multi-step machine learning workflow. Each step in the workflow is described by a Trial Component. There is no relationship between Trial Components such as ordering. Trial Component: A description of a single step in a machine learning workflow. For example data cleaning, feature extraction, model training, model evaluation, etc. -
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sense2vec
Contextually-keyed word vectors
sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo. -
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OptiMate
Libraries for optimizing AI models, inference speed, and GPU usage
...One of the core components, Speedster, focuses on accelerating model inference by applying state of the art optimization techniques to increase performance while lowering operational costs. Another component, Nos, targets infrastructure optimization by improving GPU utilization in Kubernetes clusters through dynamic partitioning and elastic resource quotas. -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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Karlo
Text-conditional image generation model based on OpenAI's unCLIP
...Unlike the original implementation of unCLIP, we replace the trainable transformer in the decoder into the text encoder in ViT-L/14 for efficiency. In the case of the SR module, we first train the model using the DDPM objective in 1M steps, followed by additional 234K steps to fine-tune the additional component. -
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HelenOS
A microkernel-based multiserver operating system written from scratch.
...It decomposes key operating system functionality such as file systems, networking, device drivers and graphical user interface into a collection of fine-grained user space components that interact with each other via message passing. A failure or crash of one component does not directly harm others. HelenOS is therefore flexible, modular, extensible, fault tolerant and easy to understand. HelenOS does not aim to be a clone of any existing operating system and trades compatibility with legacy APIs for cleaner design. Most of HelenOS components have been made to order specifically for HelenOS so that its essential parts can stay free of adaptation layers, glue code, franken-components and the maintenance burden incurred by them. -
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ipfs-css
Single-purpose css class names and font-face config to IPFS up your UI
...Once you've installed ipfs-css from npm, the CSS and SCSS files and the web-fonts are available from your node_modules/ipfs-css directory. You can import the theme.json file which can be used with a ThemeProvider component. All the CSS atoms are generated from that, so you can be sure you're using the same values. While ipfs.css contains everything you need, if you prefer variables for fonts, colors, and gradients, these are there for you in theme.scss. -
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Pattern
Web mining module for Python, with tools for scraping
...In addition to data mining features, the library offers natural language processing functionality including part-of-speech tagging, sentiment analysis, and n-gram extraction. The framework also includes machine learning algorithms that support classification, clustering, and vector space modeling for text analysis tasks. Another component of the library provides tools for analyzing and visualizing networks, making it useful for studying relationships between entities in large datasets. -
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PySC2
StarCraft II learning environment
PySC2 is DeepMind's Python component of the StarCraft II Learning Environment (SC2LE). It exposes Blizzard Entertainment's StarCraft II Machine Learning API as a Python RL Environment. This is a collaboration between DeepMind and Blizzard to develop StarCraft II into a rich environment for RL research. PySC2 provides an interface for RL agents to interact with StarCraft 2, getting observations and sending actions. -
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Arrear_Pension_Calculator
Simple app for calculating arrear pension amount of a given period.
A simple Arrear Pension Calculator app written in python with GUI specifically Govt. employees (Central & State). This application calculates arrear (Due - Drawn) / overdrawn / recovery of pension between any given periods. This app is not associated with the Govt. This app is for informational purpose only. This project is open source under GNU public license v3.0 -
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ReinventCommunity
Jupyter Notebook tutorials for REINVENT 3.2
This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.2. -
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DockStream
A Docking Wrapper to Enhance De Novo Molecular Design
DockStream is a docking wrapper providing access to a collection of ligand embedders and docking backends. Docking execution and post hoc analysis can be automated via the benchmarking and analysis workflow. The flexilibity to specifiy a large variety of docking configurations allows tailored protocols for diverse end applications. DockStream can also parallelize docking across CPU cores, increasing throughput. DockStream is integrated with the de novo design platform, REINVENT, allowing one... -
18
BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
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molten
A minimal, extensible, fast and productive framework
...Here we’ve declared a Todo manager whose job it is to store and load Todos in the DB. In order for handlers to be able to request manager instances from the DI system, we also define a component that knows how and when to instantiate a TodoManager. -
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Nebula-Python-SDK
A python SDK for managing Nebula container orchestrator
...It aim is to act as Docker orchestrator for IoT devices as well as for distributed services such as CDN or edge computing that can span thousands (possibly even millions) of devices worldwide and it does it all while being open-source and completely free. Nebula imposes no limits on the scale of the cluster, each component in it is designed to scale out to allow millions of workers to be managed by it. Designed to connect to devices that are spread around the globe Nebula is tolerant of network connection issues and will resync the device when it reconnects. With a single API call you can deploy a new container version to managed devices around the globe in minutes. -
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BlackWidow
Python web scanner for OSINT gathering and OWASP vulnerability fuzzing
...By automatically extracting this data, BlackWidow helps security professionals and researchers build a clearer understanding of a website’s structure and publicly accessible information. In addition to information gathering, the project includes a built-in fuzzing component called Inject-X, which tests dynamic URLs for common vulnerabilities listed in the OWASP Top 10. The scanner analyzes parameters and injects payloads to detect issues such as SQL injection, cross-site scripting (XSS), and open redirect vulnerabilities. -
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HDL Checker
Repurposing existing HDL tools to help writing better code
...It supports Language Server Protocol or a custom HTTP interface; can infer the library VHDL files likely to belong to, besides working out mixed language dependencies, compilation order, interpreting some compiler messages and providing some (limited) static checks. Notice that currently, the unused reports has caveats, namely declarations with the same name inherited from a component, function, procedure, etc. -
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jieba
Stuttering Chinese word segmentation
"Jaba" Chinese word segmentation, do the best Python Chinese word segmentation component. Four word segmentation modes are supported. Precise mode, which tries to cut the sentence most precisely, suitable for text analysis. Full mode, scans all the words that can be formed into words in the sentence, the speed is very fast, but the ambiguity cannot be resolved. The search engine mode, on the basis of the precise mode, divides the long words again to improve the recall rate, which is suitable for word segmentation in search engines. ... -
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AET
Detects visual changes on websites and performs page health checks
AET is a system that detects visual changes on websites and performs basic page health checks (like w3c compliance, accessibility, HTTP status codes, JS Error checks and others). AET is designed as a flexible system that can be adapted and tailored to the regression requirements of a given project. The tool has been developed to aid front-end client-side layout regression testing of websites or portfolios, in essence assessing the impact or change of a website from one snapshot to the next. -
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Nebula worker
The worker node manager container which manages nebula nodes
Nebula is a open source distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking affect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe, some example use cases are IoT devices, appliances\virtual appliances located at clients data centers, and edge computing. Nebula imposes no limits on the scale of the cluster, each component in it is designed to scale out to allow millions of workers to be managed by it. Designed to connect to devices that are spread around the globe Nebula is tolerant of network connection issues and will resync the device when it reconnects. With a single API call you can deploy a new container version to managed devices around the globe in minutes.
