Search Results for "test"
Sort By:
Showing 120 open source projects for "test"
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.
-
Context for your AI agentsBuild data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
-
1
lakeFS
lakeFS - Git-like capabilities for your object storage
...Easier ETL testing - test your ETLs on top of production data, in isolation, without copying anything. Safely experiment and test on full production data. Easily Collaborate on production data with your team. Automate data quality checks within data pipelines. -
2
PlantUML
Generate diagrams from textual description
Generate UML diagram from textual description. PlantUML is not affected by the log4j vulnerability. The easiest way to test PlantUML is in an online solution that has PlantUML embedded, such as our online server. After testing, you may want to install PlantUML locally. Run (or have your software call) PlantUML, using sequenceDiagram.txt as input. The output is an image, which either appears in the other software, or is written to an image file on disk. -
3
NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers
...The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative linear solvers, along with the ability to use sparse automatic differentiation for Jacobian construction and Jacobian-vector products. NonlinearSolve.jl interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. -
4
Yggdrasil
Collection of builder repositories for BinaryBuilder.jl
This repository contains recipes for building binaries for Julia packages using BinaryBuilder.jl. -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
-
5
CImGui
Julia wrapper for cimgui
This package provides a Julia language wrapper for cimgui: a thin c-api wrapper programmatically generated for the excellent C++ immediate mode gui Dear ImGui. Dear ImGui is mainly for creating content creation tools and visualization / debug tools. You could browse Gallery to get an idea of its use cases. -
6
Encord Active
The toolkit to test, validate, and evaluate your models and surface
Encord Active is an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance. Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically. -
7
dlib
Toolkit for making machine learning and data analysis applications
...It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is about 1 to 4. The library is tested regularly on MS Windows, Linux, and Mac OS X systems. No other packages are required to use the library, only APIs that are provided by an out of the box OS are needed. There is no installation or configure step needed before you can use the library. ... -
8
Datumaro
Dataset Management Framework, a Python library and a CLI tool to build
...It supports importing and exporting annotations and images across a wide variety of standards like COCO, PASCAL VOC, YOLO, ImageNet, Cityscapes, and many more, enabling easy integration with different training pipelines and tools. Datumaro makes it easy to merge datasets, split them into training/validation/test subsets, filter or transform annotations, and validate annotation quality — all while preserving metadata and supporting detailed statistics. It’s especially useful when you’re dealing with heterogeneous data sources or need to prepare complex datasets for machine learning workflows, freeing you from writing custom scripts for every format conversion. -
9
Broot
A new way to see and navigate directory trees
...You may also search with a regular expression. To do this, add a / before the pattern. You may also apply logical operators or combine patterns, for example searching test in all files except json ones could be !/json$/&c/test and searching carg both in file names and file contents would be carg|c/carg. -
Lightspeed golf course management softwareFrom tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
-
10
Automated Tool for Optimized Modelling
Automated Tool for Optimized Modelling
...Testing multiple pipelines requires many lines of code, and writing it all in the same notebook often makes it long and cluttered. On the other hand, using multiple notebooks makes it harder to compare the results and to keep an overview. On top of that, refactoring the code for every test can be quite time-consuming. How many times have you conducted the same action to pre-process a raw dataset? How many times have you copy-and-pasted code from an old repository to re-use it in a new use case? ATOM is here to help solve these common issues. The package acts as a wrapper of the whole machine learning pipeline, helping the data scientist to rapidly find a good model for his problem. -
11
Rocket.jl
Functional reactive programming extensions library for Julia
Rocket.jl is a Julia package for reactive programming using Observables, to make it easier to work with asynchronous data. Rocket.jl has been designed with a focus on performance and modularity. -
12
FinMind
Open Data, more than 50 financial data
...After data is available, statistical analysis, regression analysis, time series analysis, machine learning, and deep learning can be performed. For individual stocks, provide visual analysis of technical, fundamental, and chip levels. According to different strategies, back-test analysis is performed to provide performance, profit and loss, and stock selection targets of different strategy investment portfolios. -
13
PySR
High-Performance Symbolic Regression in Python and Julia
PySR is an open-source tool for Symbolic Regression: a machine learning task where the goal is to find an interpretable symbolic expression that optimizes some objective. Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, (2) as configurable as possible, and (3) easy to use. PySR is developed alongside the Julia library SymbolicRegression.jl, which forms the powerful search engine of PySR. The details of these algorithms are... -
14
IntervalArithmetic.jl
Library for validated numerics using interval arithmetic
...We are working towards having the IntervalArithmetic library be conformant with the IEEE 1788-2015 Standard for Interval Arithmetic. To do so, we have incorporated tests from the ITF1788 test suite. -
15
LinearSolve.jl
High-Performance Unified Interface for Linear Solvers in Julia
LinearSolve.jl is a unified interface for the linear solving packages of Julia. It interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. Performance is key: the current methods are made to be highly performant on scalar and statically sized small problems, with options for large-scale systems. ... -
16
ReTest.jl
Testing framework for Julia
...This is also useful for code that is not (yet) organized as a package, and where one doesn't want to maintain a separate set of files for tests. Filtering run testsets with a Regex, which is matched against the descriptions of testsets. This is useful for running only part of the test suite of a package. For example, if you made a change related to addition, and included "addition" in the description of the corresponding testsets, you can easily run only these tests. -
17
Literate
Simple package for literate programming in Julia
Literate is a package for Literate Programming. The main purpose is to facilitate writing Julia examples/tutorials that can be included in your package documentation. Literate can generate markdown pages (for e.g. Documenter.jl), and Jupyter notebooks, from the same source file. There is also an option to "clean" the source from all metadata, and produce a pure Julia script. Using a single source file for multiple purposes reduces maintenance, and makes sure your different output formats are... -
18
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... -
19
Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
Julia can track how many times, if any, each line of your code is run. This is useful for measuring how much of your code base your tests actually test, and can reveal the parts of your code that are not tested and might be hiding a bug. You can use Coverage.jl to summarize the results of this tracking or to send them to a service like Coveralls.io or Codecov.io. Julia can track how much memory is allocated by each line of your code. This can reveal problems like type instability, or operations that you might have thought were cheap (in terms of memory allocated) but aren't (i.e. accidental copying). -
20
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. -
21
Elementary
Open-source data observability for analytics engineers
...Monitoring of data quality metrics, freshness, volume and schema changes, including anomaly detection. Elementary data monitors are configured and executed like native tests in dbt your project. Uploading and modeling of dbt artifacts, run and test results to tables as part of your runs. Get informative notifications on data issues, schema changes, models and tests failures. Inspect upstream and downstream dependencies to understand impact and root cause of data issues. -
22
SciML Style Guide for Julia
A style guide for stylish Julia developers
...Instead, the principle is to be as correct as possible to begin with, and grow the generic support over time. All recommended functionality should be tested, and any known generality issues should be documented in an issue (and with a @test_broken test when possible). However, a function that is known to not be GPU-compatible is not grounds to block merging, rather it is encouraged for a follow-up PR to improve the general type support. -
23
Population Shift Monitoring
Monitor the stability of a Pandas or Spark dataframe
popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets. popmon creates histograms of features binned in time-slices, and compares the stability of the profiles and distributions of those histograms using statistical tests, both over time and with respect to a reference. It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two... -
24
Covalent workflow
Pythonic tool for running machine-learning/high performance workflows
Covalent is a Pythonic workflow tool for computational scientists, AI/ML software engineers, and anyone who needs to run experiments on limited or expensive computing resources including quantum computers, HPC clusters, GPU arrays, and cloud services. Covalent enables a researcher to run computation tasks on an advanced hardware platform – such as a quantum computer or serverless HPC cluster – using a single line of code. Covalent overcomes computational and operational challenges inherent... -
25
JLD.jl
Saving and loading julia variables while preserving native types
...However, because the serializer is also used for inter-process communication, long-term backward compatibility is currently uncertain. (The JLDArchives repository exists to test the compatibility of older JLD file formats.) If you choose to save data using the serializer, please use the file extension .jls to distinguish the files from .jld files.
