Search Results for "state-thread" - Page 3
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Showing 136 open source projects for "state-thread"
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Arianne RPG
Arianne is an engine to develop multiplayer online games like Stendhal
...Marauroa manages the client server communication and provides an object orientated view of the world for game developers. It handles database access in a transparent way to store player accounts, character progress and the state of the world -
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Postgres-WASM
A PostgresQL server running in your browser
Postgres-WASM is an experimental project that brings the PostgreSQL database engine into the browser by running it inside a WebAssembly-powered virtualized environment. Instead of reimplementing PostgreSQL, the project embeds a full Linux environment and runs a native PostgreSQL instance within it, all compiled to WebAssembly and executed in the browser. This allows developers to interact with a real PostgreSQL server locally in a sandboxed environment without installing any database... -
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PyAppExec
Launcher that prepares Python/deps and runs your app like OS-native
...The optional run-time GUI captures logs, while CLI mode stays lean for automation. Config is driven by a simple INI per OS, so app IDs, entry points, requirements, and paths are declarative. PyAppExec also manages user-level caches/state, log rotation, and GUI suppression preferences letting you ship Python apps without asking end users to touch Python, virtualenvs, or package managers. -
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Cortex AI Infrastructure
Open-source multi-chain data routing & low-latency scanning framework.
...Core Technical Modules: 1. Cross-Exchange Telemetry Scanner: Monitors order book depth and spreads across 18 venues concurrently (Binance, ByBit, OKX, HTX) with zero thread-locking. 2. Polymarket Analytics Engine: Tracks open prediction markets and volume anomalies using high-throughput CLOB data streams. 3. Solana & TON Node Routing: An asynchronous broker client optimized for private RPC nodes, tailored for high-frequency solana trading bot execution loops and automated TON/USDT spread environments (sub-28ms processing). 4. ... -
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PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
...Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! ... -
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lixa
LIXA, LIbre XA, is a free and open source XA transaction manager
...LIXA technology enables every application container, like a web server or a shell, to become a two phase commit application server. The client/server architecture of LIXA allows many application containers to share a single LIXA (state) server: this is ideal when horizontal scalability is a must and many identical application containers must refer to a single transactional environment. LIXA can be used with the C, C++, Java, Python and COBOL programming languages. -
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Django Notebook
Django + shell_plus + Jupyter notebooks made easy
...A Jupyter notebook with access to objects from the Django ORM is a powerful tool to introspect data and run ad-hoc queries. Built-in integration with the imported objects from django-extensions shell_plus. Saves the state between sessions so you don't need to remember what you did. Inheritance diagrams on any object, including ORM models. -
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Transformers4Rec
Transformers4Rec is a flexible and efficient library
...The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. Generally, the next user interaction is related to the sequence of the user's previous choices. ... -
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hloc
Visual localization made easy with hloc
This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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SMC - The State Machine Compiler
Translates state machine into a target programming language.
SMC takes a state machine stored in a .sm file and generates a State pattern in 14 programming languages. Includes: default transitions, transition args, transition guards, push/pop transitions and Entry/Exit actions. See User Manual for more info. -
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learn2learn
A PyTorch Library for Meta-learning Research
Learn2Learn is a PyTorch-based library focused on meta-learning and few-shot learning research. It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization. -
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ClassyVision
An end-to-end PyTorch framework for image and video classification
Classy Vision is a PyTorch-based framework designed for large-scale training and deployment of state-of-the-art image and video classification models. Developed by Facebook Research, it serves as an end-to-end system that simplifies the process of training at scale, reducing redundancy and friction in moving from research to production. 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. ... -
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Classic HWUT
Software Unit Tests (Language Independent Approach)
...In particular for C, HWUT supports make file generation using 'sos' and 'sols' modes. Remote control-able function stubs may be generated using the 'stub' mode. Test cases can be generated using the 'gen' mode, and state machine walkers by the 'sm_walker' mode. Over the last decade HWUT has matured towards a full fledged unit test tool for a wide variety of applications. -
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ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals, students, research groups and businesses. ... -
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FairScale
PyTorch extensions for high performance and large scale training
...It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce boilerplate in complex distributed setups. Its components are modular, so teams can adopt just the sharding optimizer or the pipeline engine without rewriting their training loop. FairScale puts emphasis on correctness and debuggability, offering hook points, logging, and reference examples for common trainer patterns. ... -
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dirsearch
Web path scanner
...To use multiple wordlists, you can separate your wordlists with commas. Example: wordlist1.txt,wordlist2.txt. Default values for dirsearch flags can be edited in the configuration file: default.conf. The thread number (-t | --threads) reflects the number of separated brute force processes. And so the bigger the thread number is, the faster dirsearch runs. By default, the number of threads is 30, but you can increase it if you want to speed up the progress. -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
...To aggregate spatial information, we design spatial cross-attention that each BEV query extracts the spatial features from the regions of interest across camera views. For temporal information, we propose temporal self-attention to recurrently fuse the history BEV information. Our approach achieves the new state-of-the-art 56.9\% in terms of NDS metric on the nuScenes \texttt{test} set, which is 9.0 points higher than previous best arts and on par with the performance of LiDAR-based baseline. -
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Facexlib
FaceXlib aims at providing ready-to-use face-related functions
facexlib is a PyTorch-based library providing ready-to-use face-related functions, including detection, alignment, recognition, and more. It integrates state-of-the-art open-source methods for various face processing tasks. -
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Official YOLOv7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art
YOLOv7 is the official implementation of the paper “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.” It is a PyTorch-based object detection project focused on high speed and strong accuracy for real-time computer vision. The repository provides model definitions, training scripts, testing tools, inference examples, pretrained weights, and deployment-oriented materials. YOLOv7 introduced training-time improvements that raise accuracy without increasing inference cost, which is why the project became important in real-time detection research. ... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. ... -
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µPipenv
A lightweight wrapper for pip to support requirements.txt
A lightweight wrapper for pip to support requirements.txt, Pipenv and Poetry lock files or converting them to pip-tools compatible output. Designed for containerized Python applications but not limited to them. if requirements.txt state all the packages in a pinned version with hashes (e.g. pip-tools), micropipenv installs packages with a possible fallback if the installation order is relevant. micropipenv does not substitute Poetry it rather complements it for containerized deployments where the size of the container image and software shipped with it matters. ... -
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Manticore
Symbolic execution tool
Manticore helps us quickly take advantage of symbolic execution, taint analysis, and instrumentation to analyze binaries. Parts of Manticore underpinned our symbolic execution capabilities in the Cyber Grand Challenge. As an open-source tool, we hope that others can take advantage of these capabilities in their own projects. We prioritized simplicity and usability while building Manticore. We used minimal external dependencies and our API should look familiar to anyone with exploitation or... -
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Interpret-Text
State-of-the-art explainers for text-based machine learning models
...Users can run their experiments across multiple state-of-the-art explainers and easily perform comparative analysis on them. Using these tools, users will be able to explain their machine-learning models globally on each label or locally for each document. -
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Detectron2
Next-generation platform for object detection and segmentation
...With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
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Spleeter
Deezer source separation library including pretrained models
Spleeter is the Deezer source separation library with pretrained models written in Python and using Tensorflow. It makes it easy to train music source separation models (assuming you have a dataset of isolated sources), and provides already trained state of the art models for performing various flavours of separation. 2 stems and 4 stems models have state of the art performances on the musdb dataset. Spleeter is also very fast as it can perform separation of audio files to 4 stems 100x faster than real-time when run on a GPU. We designed Spleeter so you can use it straight from command line as well as directly in your own development pipeline as a Python library. ...
