Search Results for "runtime"
Sort By:
Showing 40 open source projects for "runtime"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
-
1
RuntimeGeneratedFunctions.jl
Functions generated at runtime without world-age issues or overhead
RuntimeGeneratedFunctions are functions generated at runtime without world-age issues and with the full performance of a standard Julia anonymous function. This builds functions in a way that avoids eval. For technical reasons, RuntimeGeneratedFunctions needs to cache the function expression in a global variable within some module. This is normally transparent to the user, but if the RuntimeGeneratedFunction is evaluated during module precompilation, the cache module must be explicitly set to the module currently being precompiled. ... -
2
Infiltrator.jl
No-overhead breakpoints in Julia
This package provides the @infiltrate macro, which acts as a breakpoint with negligible runtime performance overhead. Note that you cannot access other function scopes or step into further calls. Use an actual debugger if you need that level of flexibility. Running code that ends up triggering the @infiltrate REPL mode via inline evaluation in VS Code or Juno can cause issues, so it's recommended to always use the REPL directly. -
3
Bukdu.jl
Bukdu is a web development framework for Julia
Bukdu.jl is a web development framework for Julia. -
4
GoldenCheetah
Performance Software for Cyclists, Runners, Triathletes and Coaches
Analyze using summary metrics like BikeStress, TRIMP, or RPE. Extract insight via models like Critical Power and W'bal. Track and predict performance using models like Banister and PMC. Optimize aerodynamics using Virtual Elevation. Train indoors with ANT and BTLE trainers. Upload and Download with many cloud services including Strava, Withings, and Today's Plan. Import and export data to and from a wide range of bike computers and file formats. Track body measures, and equipment use and set... -
Incredable is the first DLT-secured platform that allows you to save time, eliminate errors, and ensure your organization is compliant all in one place.Incredable streamlines and simplifies the complex process of medical credentialing for hospitals and medical facilities, helping you save valuable time, reduce costs, and minimize risks. With Incredable, you can effortlessly manage all your healthcare providers and their credentials within a single, unified platform. Our state-of-the-art technology ensures top-notch data security, giving you peace of mind.
-
5
Zenith
Sort of like top or htop but with zoom-able charts, CPU, GPU
...If one needs to build with NVIDIA support in a virtual environment, then it requires some more setup since typically the VM software is unable to directly expose NVIDIA GPU. Unlike the runtime zenith script, the Makefile has been setup to detect only the presence of required NVIDIA libraries, so it is possible to build with NVIDIA support even when without NVIDIA GPU. -
6
DynamicQuantities.jl
Lightweight + fast physical quantities in Julia
DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods. -
7
Malli
High-performance data-driven data specification library
Malli is a powerful, data-driven schema library for Clojure and ClojureScript, offering rich support for specification, validation, parsing, error reporting, and generative testing. Designed for performance, Malli leverages efficient runtime representations and code generation, seamlessly integrating with Clojure’s data-oriented architecture. It supports function schemas, JSON transformation, and OpenAPI generation for strong API contracts. -
8
CBinding.jl
Automatic C interfacing for Julia
Use CBinding.jl to automatically create C library bindings with Julia at runtime. In order to support the fully automatic conversion and avoid name collisions, the names of C types or functions are mangled a bit to work in Julia. Therefore everything generated by CBinding.jl can be accessed with the c"..." string macro to indicate that it lives in C-land. As an example, the function func above is available in Julia as c"func". -
9
DearPyGui
Graphical User Interface Toolkit for Python with minimal dependencies
Dear PyGui is an easy-to-use, dynamic, GPU-Accelerated, cross-platform graphical user interface toolkit(GUI) for Python. It is “built with” Dear ImGui. Features include traditional GUI elements such as buttons, radio buttons, menus, and various methods to create a functional layout. Additionally, DPG has an incredible assortment of dynamic plots, tables, drawings, debuggers, and multiple resource viewers. DPG is well suited for creating simple user interfaces as well as developing complex... -
Inventors: Validate Your Idea, Protect It and Gain Market AdvantagessenseIP is an AI innovation platform for inventors, automating any aspect of IP from the moment you have an idea. You can have it researched for uniqueness and protected; quickly and effortlessly, without expensive attorneys. Built for business success while securing your competitive edge.
-
10
htop
An interactive process viewer
htop is a cross-platform interactive process viewer. htop allows scrolling the list of processes vertically and horizontally to see their full command lines and related information like memory and CPU consumption. Also, system-wide information, like load average or swap usage, is shown. The information displayed is configurable through a graphical setup and can be sorted and filtered interactively. Tasks related to processes (e.g. killing and renicing) can be done without entering their... -
11
oneAPI.jl
Julia support for the oneAPI programming toolkit.
Julia support for the oneAPI programming toolkit. oneAPI.jl provides support for working with the oneAPI unified programming model. The package is verified to work with the (currently) only implementation of this interface that is part of the Intel Compute Runtime, only available on Linux. This package is still under significant development, so expect bugs and missing features. -
12
Symfony PropertyInfo
Extracts information about PHP class' properties using metadata
Symfony PropertyInfo is a component that extracts information about the properties of PHP classes, such as their names, types, visibility, and documentation. It is particularly useful in scenarios like serialization, form generation, and validation, where understanding the structure of an object is essential. PropertyInfo can fetch data from PHPDoc annotations, reflection, and type hints, offering flexible integration with Symfony and other systems. -
13
Sysdig
Linux system exploration and troubleshooting tool
...Gain deep insight with container and Kubernetes monitoring that is fully Prometheus compatible. Validate compliance against standards like PCI, NIST and SOC2 for containers, hosts, Kubernetes and cloud. Sysdig created Falco, the open standard for runtime threat detection for containers, hosts, Kubernetes and cloud. -
14
The Tengo Language
A fast script language for Go
Tengo is a small, dynamic, fast, secure script language for Go. Tengo is fast and secure because it's compiled/executed as bytecode on stack-based VM that's written in native Go. Securely Embeddable and Extensible. Compiler/runtime written in native Go (no external deps or cgo). Executable as a standalone language / REPL. Use cases, rules engine, state machine, data pipeline, transpiler. If you need to evaluate a simple expression, you can use Eval function instead. -
15
SageMaker Training Toolkit
Train machine learning models within Docker containers
...To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. If you use a prebuilt SageMaker Docker image for training, this library may already be included. Write a training script (eg. train.py). Define a container with a Dockerfile that includes the training script and any dependencies. -
16
Siddhi Core Libraries
Stream Processing and Complex Event Processing Engine
...Event processing logic can be written using Streaming SQL queries via graphical and source editors, to capture events from diverse data sources, process and analyze them, integrate with multiple services and data stores, and publish output to various endpoints in real time. Agile development experience with SQL-like query language and graphical drag-and-drop editor supporting event simulation. Lightweight runtime that can natively run on Kubernetes, Docker, VM, or bare metal, and embedded in any Java or Python application. Scalable, and highly available distributed event processing on Kubernetes, with NATS Streaming and Siddhi Kubernetes Operator. -
17
PyCall.jl
Package to call Python functions from the Julia language
Package to call Python functions from the Julia language. This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them. -
18
SageMaker Inference Toolkit
Serve machine learning models within a Docker container
...Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed. Containerizing your model and code enables fast and reliable deployment of your model. The SageMaker Inference Toolkit implements a model serving stack and can be easily added to any Docker container, making it deployable to SageMaker. This library's serving stack is built on Multi Model Server, and it can serve your own models or those you trained on SageMaker using machine learning frameworks with native SageMaker support. -
19
Gaius.jl
Divide and Conquer Linear Algebra
Gaius.jl is a multi-threaded BLAS-like library using a divide-and-conquer strategy to parallelism, and built on top of the fantastic LoopVectorization.jl. Gaius spawns threads using Julia's depth-first parallel task runtime and so Gaius's routines may be fearlessly nested inside multi-threaded Julia programs. Gaius is not stable or well-tested. Only use it if you're adventurous. -
20
Diana.jl
GraphQL for Julia
GraphQL is a data query language for APIs and a runtime for fulfilling those queries with your existing data developed by Facebook in 2012 and launched on the internet in 2015. It is an alternative to REST and ad-hoc web service architectures. -
21
DotNET.jl
This package provides interoperability between Julia and .NET apps
This package provides interoperability between Julia and Common Language Runtime, the execution engine of .NET applications. Many languages run on CLR, including C#, Visual Basic .NET and PowerShell. -
22
ReplMaker.jl
Simple API for building repl modes in Julia
...Suppose you've invented some language called MyLang and you've implemented a parser that turns MyLang code into Julia code which is then supposed to be executed by the Julia runtime. With ReplMaker.jl, you can simply hook your parser into the package and ReplMaker will then create a REPL mode where end users just type MyLang code and have it executed automatically. My hope is for this to be useful to someone who implements a full language or DSL in Julia that uses syntax not supported by Julia's parser and doesn't want to deal with the headache of making their own REPL mode. -
23
SageMaker Containers
Create SageMaker-compatible Docker containers
...To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. If you use a prebuilt SageMaker Docker image for training, this library may already be included. Very often, an entry point needs additional information from the container that is not available in hyperparameters. ... -
24
immutability-helper
Mutate a copy of data without changing the original source
React lets you use whatever style of data management you want, including mutation. However, if you can use immutable data in performance-critical parts of your application it's easy to implement a fast shouldComponentUpdate() method to significantly speed up your app. Dealing with immutable data in JavaScript is more difficult than in languages designed for it, like Clojure. However, we've provided a simple immutability helper, update(), that makes dealing with this type of data much easier,... -
25
Inviwo
Interactive Visualization Workshop
Inviwo is a modern, open-source visualization framework designed for interactive visual data analysis and scientific computing. Built in C++ with a modular, extensible architecture, Inviwo combines a visual editor (for creating data pipelines) with a powerful runtime engine that supports real-time rendering, interaction, and GPU-accelerated processing. It’s widely used in scientific domains for building and sharing visualizations of complex data such as medical imaging, simulations, and machine learning models. The platform supports both novice users through its graphical interface and advanced users through scripting and plugin development.
