Search Results for "state-thread"
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Showing 33 open source projects for "state-thread"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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NautilusTrader
A high-performance algorithmic trading platform
NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, provides quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes. The platform is 'AI-first', designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the... -
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marimo
A reactive notebook for Python
...Notebooks are executed in a deterministic order, with no hidden state, delete a cell and marimo deletes its variables while updating affected cells. -
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Prefect
Prefect is a workflow orchestration framework
Prefect is an open-source modern workflow orchestration tool for scheduling, monitoring, and managing data workflows and tasks. It enables Python-native pipeline definitions with robust retries, caching, observability, and a powerful UI—ideal for data engineering and ETL processes. -
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pydna
Clone with Python! Data structures for double stranded DNA
...Planning genetic constructs with many parts and assembly steps, such as recombinant metabolic pathways, are often difficult to properly document as is evident from the poor state of documentation in the scientific literature. The pydna python package provide a human-readable formal description of cloning and genetic assembly strategies in Python which allow for simulation and verification. Pydna can be used as executable documentation for cloning. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. -
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Universal Commerce Protocol
Specification and documentation for the Universal Commerce Protocol
UCP (Universal Commerce Protocol) is an open standard intended to make commerce integrations interoperable across platforms, agents, businesses, and payment providers without bespoke, one-off connector builds. It defines a shared “common language” and functional primitives so that different systems can express commerce actions and state transitions in a consistent way. The protocol is designed around the realities of existing retail infrastructure, aiming to fit into current operational models while enabling more automated, agent-driven buying experiences. By standardizing how discovery, purchase, and post-purchase steps are represented, it helps reduce integration complexity and makes it easier for multiple parties to participate in the same end-to-end flow. ... -
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Cleanlab
The standard data-centric AI package for data quality and ML
...To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. ... -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
...Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. ... -
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Recommenders
Best practices on recommendation systems
...Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests. -
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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Luigi
Python module that helps you build complex pipelines of batch jobs
Luigi is a Python (3.6, 3.7, 3.8, 3.9 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more. The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to chain many tasks, automate them, and failures will happen. These tasks can be anything, but are typically long running things like Hadoop... -
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relax
Molecular dynamics by NMR data analysis
...It supports exponential curve fitting for the calculation of the R1 and R2 relaxation rates, calculation of the NOE, reduced spectral density mapping, the Lipari and Szabo model-free analysis, study of domain motions via the N-state model and frame order dynamics theories using anisotropic NMR parameters such as RDCs and PCSs, the investigation of stereochemistry in dynamic ensembles, and the analysis of relaxation dispersion data. -
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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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TradingGym
Trading backtesting environment for training reinforcement learning
TradingGym is a toolkit (in Python) for creating trading and backtesting environments, especially for reinforcement learning agents, but also for simpler rule-based algorithms. It follows a design inspired by OpenAI Gym, offering various environments, data formats (tick data and OHLC), and tools to simulate trading with costs, position limits, observation windows etc. Licensed under MIT. This training environment was originally designed for tickdata, but also supports OHLC data format. WIP.... -
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Visdom
A tool for creating, organizing, and sharing data visualizations
...The UI begins as a blank slate, you can populate it with plots, images, and text. These appear in windows that you can drag, drop, resize, and destroy. The windows live in envs and the state of envs is stored across sessions. You can download the content of windows, including your plots in svg. -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. ... -
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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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Pension-Calculator
A simple retirement benefit calculator app written in python with GUI.
A simple retirement benefit calculator app written in python with GUI specifically for West Bengal State Govt. employees (India). This app is not associated with Govt. of West Bengal. This app is for informational purpose only. This project is open source under GNU public license v3.0 -
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Zipline
Zipline, a Pythonic algorithmic trading library
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python... -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
...The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. -
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NYCOpenData-Profiling-Analysis
Open Data Profiling, Quality and Analysis on NYC OpenData dataset
...You will profile a large collection of open data sets and derive metadata that can be used for data discovery, querying, and identification of data quality problems. For each column, identify and summarize the semantic types present in the column. These can be generic types (e.g., city, state) or collection-specific types (NYU school names, NYC agency). For each semantic type T identified, enumerate all the values encountered for T in all columns present in the collection. -
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Wally
Distributed Stream Processing
...Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. The primary API for Wally is written in Pony. Wally applications are written using this Pony API. -
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ThermV
Comprehensive thermal analysis software package
...It offers new algorithm for concurrent peak deconvolution at different heating rates and provides full kinetic analysis of these data, including isoconversional methods for Ea, determination of reaction model and full kinetic triplet, Avrami coefficients, and dimensionality of crystal growth for reactions in the solid state. The project is currently in alpha stage, where individual modules will be provided for data analysis. The modules for peak deconvolution, peak profile analysis and determination of Ea and lnA will be provided first. Full GUI will be provided in beta stage. Due to computational limitations, the code is partly programmed in Python and partly in R. ... -
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CollectD-CSV Plotter
Web interface for visualizing CSV data from CollectD monitoring system
CollectD-CSV Plotter is a web interface for fetching and visualizing data that is saved in CollectD CSV format. An HTML form is used for defining the time interval and choosing the wanted measurements. The plotter is actually a front-end for collectd-csv module (http://pypi.python.org/pypi/collectd-csv/), which it uses for fetching the data. Technologies CollectD-CSV Plotter uses include CGI/Python, HTML5/JavaScript, jQuery, jQuery-UI and Gnuplot. -
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The csvdatamix project aims to randomize CSV input data files in order to conceal the original state of the data. Similar to data masking or data transformation. Also has mapping abilities to translate back to the original state of the data.
