Search Results for "using" - Page 6
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Showing 723 open source projects for "using"
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Find Hidden Risks in Windows Task SchedulerWindows Task Scheduler might be hiding critical failures. Download the free JAMS diagnostic tool to uncover problems before they impact production—get a color-coded risk report with clear remediation steps in minutes.
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Vibes don’t ship, Retool doesVibe coding tools create cool demos, but Retool helps you build software your company can actually use. Generate internal apps that connect directly to your data—deployed in your cloud with enterprise security from day one. Build dashboards, admin panels, and workflows with granular permissions already in place. Stop prototyping and ship on a platform that actually passes security review.
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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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vim‑sneak
The missing motion for Vim
vim‑sneak is a minimalist yet powerful navigation plugin for Vim, designed to jump to any location specified by two characters. It maintains compatibility with Vim's operators, macros, visual mode, repeat (.), and supports multibyte text. It provides intuitive highlights that disappear once the jump is completed. Sneak has <Plug> mappings for f and t 1-character-sneak. These mappings do not invoke label-mode, even if you have it enabled. Highlights matches in the direction of your search.... -
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Gradle Docker Compose Plugin
Simplifies usage of Docker Compose for integration testing
The Gradle Docker Compose Plugin by Avast integrates Docker Compose lifecycle management into Gradle builds. It allows developers to define and manage Docker containers required for integration testing or local development directly from their Gradle build scripts. This plugin automates the startup and shutdown of services, supports container health checks, and enables tight integration between application code and containerized services, enhancing reproducibility and automation in... -
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QuadGK.jl
adaptive 1d numerical Gauss–Kronrod integration in Julia
This package provides support for one-dimensional numerical integration in Julia using adaptive Gauss-Kronrod quadrature. The code was originally part of Base Julia. It supports the integration of arbitrary numeric types, including arbitrary-precision (BigFloat), and even the integration of arbitrary normed vector spaces. The package provides three basic functions: quadgk, gauss, and kronrod. quadgk performs the integration, gauss computes Gaussian quadrature points and weights for integrating over the interval [a, b], and kronrod computes Kronrod points, weights, and embedded Gaussian quadrature weights for integrating over [-1, 1]. -
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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Durable Streams
The open protocol for real-time sync to client applications
Durable Streams is an open protocol and reference implementation designed to standardize reliable, resumable, real-time streaming between servers and client applications using simple HTTP semantics, filling a gap left by ephemeral technologies like WebSockets and traditional SSE. It defines an append-only, offset-addressable stream primitive where each stream is mapped to a URL that clients can read from or tail, supporting catch-up reads, historical replay, and live updates with robust resume capabilities across reconnections. ... -
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forecast
Forecasting Functions for Time Series and Linear Models
The forecast package is a comprehensive R package for time series analysis and forecasting. It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. ... -
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Fondant
Production-ready data processing made easy and shareable
...It offers an end-to-end system for ingesting raw data, applying transformations, filtering, and formatting outputs—all while remaining scalable and traceable. Fondant is designed with reproducibility in mind and supports containerized steps using Docker, making it easy to share and reuse data processing components. It’s built for use in research and production, empowering data scientists to streamline dataset curation and preprocessing workflows efficiently. -
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TensorBoardLogger.jl
Easy peasy logging to TensorBoard with Julia
TensorBoardLogger.jl is a native library for logging arbitrary data to Tensorboard, extending Julia's standard Logging framework. It can also be used to deserialize TensoBoard's .proto files. The fundamental type defined in this package is a TBLogger, which behaves like other standard loggers in Julia such as ConsoleLogger or TextLogger. You can create one by passing it the path to the folder where you want to store the data. You can also pass an optional second argument to specify the... -
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HomotopyContinuation.jl
A Julia package for solving systems of polynomials
...Many models in the sciences and engineering are expressed as sets of real solutions to systems of polynomial equations. We can optimize any objective whose gradient is an algebraic function using homotopy methods by computing all critical points of the objective function. An important special case is when the objective function is the euclidean distance to a given point. An example of an non-algebraic objective function whose derivative is algebraic is the Kullback–Leibler divergence. Homotopy continuation methods allow us to study the conformation space of molecules as for example cyclooctane (CH₂)₈. ... -
Easy-to-use Business Software for the Waste Management Software IndustryDOP Software’s mission is to streamline waste and recycling business’ processes by providing them with dynamic, comprehensive software and services that increase productivity and quality of performance.
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Nuclio
High-Performance Serverless event and data processing platform
...The dashboard, when running outside an orchestration platform (e.g. Kubernetes or Swarm), will simply be deployed to the local docker daemon. The Getting Started With Nuclio On Kubernetes guide has a complete step-by-step guide to using Nuclio serverless functions over Kubernetes. -
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GenStage
Producer and consumer actors with back-pressure for Elixir
GenStage is a specification and set of behaviours for building demand-driven data pipelines on the BEAM. It formalizes the roles of producers, consumers, and producer-consumers, using back-pressure so that fast producers don’t overwhelm downstream stages. Developers implement callbacks like handle_demand and handle_events to control how items are emitted, transformed, and consumed across asynchronous boundaries. Because stages are OTP processes, you gain fault tolerance, supervised restarts, and concurrency tuned via configurable demand and partitioning. ... -
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errsole.js
Collect, Store, and Visualize Logs with a Single Module
Errsole is an open-source logger for Node.js. It has a built-in web dashboard to view, filter, and search your app logs. -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
...ImplicitGlobalGrid relies on the Julia MPI wrapper (MPI.jl) to perform halo updates close to hardware limit and leverages CUDA-aware or ROCm-aware MPI for GPU-applications. The communication can straightforwardly be hidden behind computation [1, 3] (how this can be done automatically when using ParallelStencil.jl is shown in; a general approach particularly suited for CUDA C applications is explained in. -
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Roots.jl
Root finding functions for Julia
This package contains simple routines for finding roots, or zeros, of scalar functions of a single real variable using floating-point math. The find_zero function provides the primary interface. The basic call is find_zero(f, x0, [M], [p]; kws...) where, typically, f is a function, x0 a starting point or bracketing interval, M is used to adjust the default algorithms used, and p can be used to pass in parameters. Bisection-like algorithms. For functions where a bracketing interval is known (one where f(a) and f(b) have alternate signs), a bracketing method, like Bisection, can be specified. ... -
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jlrs
Julia bindings for Rust
jlrs is a crate that provides access to most of the Julia C API, it can be used to embed Julia in Rust applications and to use functionality it provides when writing ccallable functions in Rust. Currently, this crate is only tested in combination with Julia 1.6 and 1.9, but also supports Julia 1.7, 1.8, and 1.10. Using the current stable version is highly recommended. The minimum supported Rust version is currently 1.65. Julia must be installed before jlrs can be used, jlrs is compatible with Julia 1.6 up to and including Julia 1.10. The JlrsCore package must also have been installed, if this is not the case it will automatically be added when jlrs is initialized by default. jlrs has not been tested with juliaup yet on Linux and macOS. -
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HDF5.jl
Save and load data in the HDF5 file format from Julia
...For example, HDF5 encodes that a given block of bytes is to be interpreted as an array of Int64, and represents them in a way that is compatible across different computing architectures. However, to preserve Julia objects, one generally needs additional type information to be supplied, which is easy to provide using attributes. -
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Convex.jl
A Julia package for disciplined convex programming
Convex.jl is a Julia package for Disciplined Convex Programming (DCP). Convex.jl makes it easy to describe optimization problems in a natural, mathematical syntax, and to solve those problems using a variety of different (commercial and open-source) solvers. Convex.jl works by transforming the problem—which possibly has nonsmooth, nonlinear constructions like the nuclear norm, the log determinant, and so forth—into a linear optimization problem subject to conic constraints. This reformulation often involves adding auxiliary variables and is called an "extended formulation", since the original problem has been extended with additional variables. ... -
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Luxor
Simple drawings using vector graphics; Cairo "for tourists!"
Luxor is a Julia package for drawing simple static 2D vector graphics. It provides basic drawing functions and utilities for working with shapes, polygons, clipping masks, PNG and SVG images, turtle graphics, and simple animations. The focus of Luxor is on simplicity and ease of use: it should be easier to use than plain Cairo.jl, with shorter names, fewer underscores, default contexts, and simplified functions. For more complex and sophisticated graphics in 2D and 3D, Makie.jl is the best... -
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Optimization.jl
Mathematical Optimization in Julia
Optimization.jl provides the easiest way to create an optimization problem and solve it. It enables rapid prototyping and experimentation with minimal syntax overhead by providing a uniform interface to >25 optimization libraries, hence 100+ optimization solvers encompassing almost all classes of optimization algorithms such as global, mixed-integer, non-convex, second-order local, constrained, etc. It allows you to choose an Automatic Differentiation (AD) backend by simply passing an... -
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ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
ForwardDiff implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD). While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy. Functions like f which map a vector to a scalar are the best case for reverse-mode automatic differentiation, but ForwardDiff may still be a good choice if x is not too large, as it is much simpler. ... -
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protoactor-go
Proto Actor - Ultra fast distributed actors for Go, C# and Java/Kotlin
...Asynchronous and Distributed by design. High-level abstractions like Actors and Virtual Grains. Capable of millions of messages per second cross-process communication. Write systems that self-heal using supervisor hierarchies. The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates the developer from having to deal with explicit locking and thread management, making it easier to write correct concurrent and parallel systems. Grain abstraction, which provides a straightforward approach to building distributed interactive applications, without the need to learn complex programming patterns for handling concurrency, fault tolerance, and resource management. ... -
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see
Visualisation toolbox for beautiful and publication-ready figures
see is an R package that serves as the visualization component of the easystats ecosystem, providing plotting utilities to produce publication-ready visualizations of statistical model parameters, diagnostics, predictions, and performance metrics. It works in conjunction with other easystats packages (such as parameters, performance, modelbased, bayestestR, etc.) to convert model outputs or summary objects into visual forms (dot-and-whisker plots, diagnostic plots, residual plots, etc.). It... -
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ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array programming implementation; in absolute terms, it reached 70% of the theoretical upper performance bound of the used Nvidia P100 GPU, as defined by the effective throughput metric, T_eff. ... -
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CxxWrap
Package to make C++ libraries available in Julia
This package aims to provide a Boost. Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there. The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library. This dynamic library is then loaded into Julia, where the Julia part of this package uses the data provided through a... -
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PySR
High-Performance Symbolic Regression in Python and Julia
...The details of these algorithms are described in the PySR paper. Symbolic regression works best on low-dimensional datasets, but one can also extend these approaches to higher-dimensional spaces by using "Symbolic Distillation" of Neural Networks, as explained in 2006.11287, where we apply it to N-body problems. Here, one essentially uses symbolic regression to convert a neural net to an analytic equation. Thus, these tools simultaneously present an explicit and powerful way to interpret deep neural networks.
