Search Results for "test management"
Sort By:
Showing 101 open source projects for "test management"
View related business solutions-
Earn up to 16% annual interest with Nexo.Generate interest, access liquidity without selling, and execute trades seamlessly. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
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.
-
1
lakeFS
lakeFS - Git-like capabilities for your object storage
Increase data quality and reduce the painful cost of errors. Data engineering best practices using git-like operations on data. lakeFS is an open-source data version control for data lakes. It enables zero-copy Dev / Test isolated environments, continuous quality validation, atomic rollback on bad data, reproducibility, and more. Data is dynamic, it changes over time. Dealing with that without a data version control system is error-prone and labor-intensive. With lakeFS, your data lake is... -
2
Yggdrasil
Collection of builder repositories for BinaryBuilder.jl
This repository contains recipes for building binaries for Julia packages using BinaryBuilder.jl. -
3
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. -
4
Datumaro
Dataset Management Framework, a Python library and a CLI tool to build
Datumaro is a flexible Python-based dataset management framework and command-line tool for building, analyzing, transforming, and converting computer vision datasets in many popular formats. 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. -
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.
-
5
dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. 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... -
6
NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers
Fast implementations of root-finding algorithms in Julia that satisfy the SciML common interface. For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation that contains the unreleased features. NonlinearSolve.jl is a unified interface for the nonlinear solving packages of Julia. The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative... -
7
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. -
8
Elementary
Open-source data observability for analytics engineers
Elementary is an open-source data observability solution for data & analytics engineers. Monitor your dbt project and data in minutes, and be the first to know of data issues. Gain immediate visibility, detect data issues, send actionable alerts, and understand the impact and root cause. Generate a data observability report, host it or share with your team. Monitoring of data quality metrics, freshness, volume and schema changes, including anomaly detection. Elementary data monitors are... -
9
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... -
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.
-
10
IntervalArithmetic.jl
Library for validated numerics using interval arithmetic
IntervalArithmetic.jl is a Julia package for validated numerics in Julia. All calculations are carried out using interval arithmetic where quantities are treated as intervals. The final result is a rigorous enclosure of the true value. 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. -
11
ReTest.jl
Testing framework for Julia
ReTest is a testing framework for Julia allowing defining tests in source files, whose execution is deferred and triggered on demand. This is useful when one likes to have definitions of methods and corresponding tests close to each other. 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... -
12
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... -
13
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... -
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
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... -
16
Mapbox GL JS
Interactive, thoroughly customizable maps in the browser
Mapbox GL JS is a JavaScript library that uses WebGL to render interactive maps from vector tiles and Mapbox styles. It is part of the Mapbox GL ecosystem, which includes Mapbox Mobile, a compatible renderer written in C++ with bindings for desktop and mobile platforms. Mapbox GL JS is part of the cross-platform Mapbox GL ecosystem, which also includes compatible native SDKs for applications on Android, iOS, macOS, Qt, and React Native. Mapbox provides building blocks to add location... -
17
FinMind
Open Data, more than 50 financial data
In the era of big data, data is the foundation of everything. We collect more than 50 kinds of Taiwan stock related information and provide download, online analysis, and backtesting. Regardless of the program, you can download data through the api provided by FinMind, or you can download data directly from the website. After data is available, statistical analysis, regression analysis, time series analysis, machine learning, and deep learning can be performed. For individual stocks, provide... -
18
PySyft
Data science on data without acquiring a copy
Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines without first obtaining control over those machines. This is very limiting to human collaboration and systematically drives the centralization of data, because you cannot work with a bunch of data... -
19
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... -
20
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... -
21
SciML Style Guide for Julia
A style guide for stylish Julia developers
The SciML Style Guide is a style guide for the Julia programming language. It is used by the SciML Open Source Scientific Machine Learning Organization. As such, it is open to discussion with the community. If the standard for code contributions is that every PR needs to support every possible input type that anyone can think of, the barrier would be too high for newcomers. Instead, the principle is to be as correct as possible to begin with, and grow the generic support over time. All... -
22
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,... -
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
Automated Tool for Optimized Modelling
Automated Tool for Optimized Modelling
During the exploration phase of a machine learning project, a data scientist tries to find the optimal pipeline for his specific use case. This usually involves applying standard data cleaning steps, creating or selecting useful features, trying out different models, etc. 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... -
25
Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions...
