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Showing 48 open source projects for "speed"
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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PlotJuggler
The Time Series Visualization Tool that you deserve
Fast, intuitive, and extensible time series visualization tool. Its Drag & Drop interface is designed to maximize both simplicity and speed. PlotJuggler is perfect for visualizing logs, offline and real-time data, and it can be used in multiple fields. PlotJuggler can be connected to an external application using any inter-process communication and display data in real time. Thanks to its plugin-based architecture, it is easy to add new data sources and functionalities. -
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F1 Race Replay
An interactive Formula 1 race visualisation and data analysis tool
...It ingests official timing and positional data, then renders vehicle movements through track maps and 3D visualizations so fans, analysts, and engineers can review strategy, overtakes, tire degradation effects, and pit stop impacts in detail. Users can scrub through time, jump between cars, and overlay performance graphs such as speed, sector times, and gap differentials to evaluate performance trends across laps. This deep dive capability turns passive viewing into active exploration, empowering enthusiasts and professionals to discover insights usually hidden in raw data. The viewer also supports annotations and bookmark capabilities so users can mark moments of interest for future review or comparison. -
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GPUArrays
Reusable array functionality for Julia's various GPU backends
Reusable GPU array functionality for Julia's various GPU backends. This package is the counterpart of Julia's AbstractArray interface, but for GPU array types: It provides functionality and tooling to speed-up development of new GPU array types. This package is not intended for end users! Instead, you should use one of the packages that builds on GPUArrays.jl, such as CUDA.jl, oneAPI.jl, AMDGPU.jl, or Metal.jl. -
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Backstage
Backstage is an open platform for building developer portals
Powered by a centralized software catalog, Backstage restores order to your infrastructure and enables your product teams to ship high-quality code quickly, without compromising autonomy. At Spotify, we've always believed in the speed and ingenuity that comes from having autonomous development teams. But as we learned firsthand, the faster you grow, the more fragmented and complex your software ecosystem becomes. And then everything slows down again. By centralizing services and standardizing your tooling, Backstage streamlines your development environment from end to end. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. -
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IoTDB
Apache IoTDB
...Due to its light-weight architecture, high performance and rich feature set together with its deep integration with Apache Hadoop, Spark and Flink, Apache IoTDB can meet the requirements of massive data storage, high-speed data ingestion and complex data analysis in the IoT industrial fields. In the scene of factories, there are tens of devices under LAN network. IoTDB can be installed on a local controller server in the factory to receive data from those devices. The local controller server (normal PC or workstation) with IoTDB can provide the ability to persist data and query data with SQL-like interface. ... -
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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. -
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Plots
Powerful convenience for Julia visualizations and data analysis
Data visualization has a complicated history. Plotting software makes trade-offs between features and simplicity, speed and beauty, and a static and dynamic interface. Some packages make a display and never change it, while others make updates in real-time. Plots is a visualization interface and toolset. It sits above other backends, like GR, PythonPlot, PGFPlotsX, or Plotly, connecting commands with implementation. If one backend does not support your desired features or make the right trade-offs, you can just switch to another backend with one command. ... -
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DataFrames.jl
In-memory tabular data in Julia
...It provides a familiar, flexible, and efficient interface for handling datasets, making it easy to load, manipulate, join, and analyze structured data. With syntax inspired by data frames in R and pandas in Python, it offers intuitive tools while taking advantage of Julia’s speed and type system. The package is actively maintained by the JuliaData community, with contributions from over 200 developers worldwide. It is widely used for data science, research, and production applications, supported by extensive documentation, tutorials, and a free Julia Academy course. Cited in academic literature and trusted by practitioners, DataFrames.jl is one of the core libraries for Julia’s growing data ecosystem. -
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Alluxio
Open Source Data Orchestration for the Cloud
Alluxio is the world’s first open source data orchestration technology for analytics and AI for the cloud. It bridges the gap between computation frameworks and storage systems, bringing data from the storage tier closer to the data driven applications. This enables applications to connect to numerous storage systems through a common interface. It makes data local, more accessible and as elastic as compute. -
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ReverseDiff
Reverse Mode Automatic Differentiation for Julia
ReverseDiff is a fast and compile-able tape-based reverse mode automatic differentiation (AD) that implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really). While performance can vary depending on the functions you evaluate, the algorithms implemented by ReverseDiff generally outperform non-AD algorithms in both speed and accuracy. -
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ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
...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. The best case for forward-mode differentiation is a function that maps a scalar to a vector. -
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collapse
Advanced and Fast Data Transformation in R
...Built to offer a more performant alternative to dplyr and data.table, it is particularly well-suited for large datasets and econometric applications. It operates on base R data structures like data frames and vectors and uses highly optimized C++ code under the hood to deliver significant speed improvements. collapse also includes tools for grouped operations, weighted statistics, and time series manipulation, making it a compact yet powerful utility for data scientists and researchers working in R. -
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Tullio.jl
Tullio is a very flexible einsum macro
Tullio is a very flexible einsum macro. It understands many array operations written in index notation -- not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting. Used by itself the macro writes ordinary nested loops much like Einsum.@einsum. One difference is that it can parse more expressions, and infer ranges for their indices. Another is that it will use multi-threading (via Threads.@spawn) and recursive tiling, on large enough arrays. -
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TIGRE
TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox
TIGRE is an open-source toolbox for fast and accurate 3D tomographic reconstruction for any geometry. Its focus is on iterative algorithms for improved image quality that have all been optimized to run on GPUs (including multi-GPUs) for improved speed. It combines the higher-level abstraction of MATLAB or Python with the performance of CUDA at a lower level in order to make it both fast and easy to use. TIGRE is free to download and distribute: use it, modify it, add to it, and share it. Our aim is to provide a wide range of easy-to-use algorithms for the tomographic community "off the shelf". ... -
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Checkmate
Checkmate is an open-source, self-hosted tool
Checkmate is an open-source, self-hosted infrastructure monitoring platform that provides real-time visibility into server health, uptime, response times, and incident activity through a modern web interface. The application continuously checks whether websites and services are accessible and performing optimally, generating alerts and reports when availability or performance degrades. It supports detailed infrastructure monitoring through an optional agent called Capture, which collects... -
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targets
Function-oriented Make-like declarative workflows for R
The targets package is a pipeline / workflow management tool in R, designed to coordinate multi‐step computational workflows in data science / statistics. It tracks dependencies between “targets” (computational steps), skips steps whose upstream data or code hasn’t changed, supports parallel computation, branching (dynamic generation of sub‐targets), file format abstractions, and encourages reproducible and efficient analyses. It’s something like GNU Make for R, but more integrated. Skipping... -
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Modin
Scale your Pandas workflows by changing a single line of code
Scale your pandas workflow by changing a single line of code. Modin uses Ray, Dask or Unidist to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code. Even using the DataFrame constructor is identical. It is not necessary to know in advance the available hardware resources in order to use Modin. Additionally, it is not necessary to specify how to distribute or place data. ... -
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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... -
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Semantic Type Detection
Metadata/data identification Java library
...Large set of built-in Semantic Types (extensible via JSON defined plugins). Extensive Profiling metrics (e.g. Min, Max, Distinct, signatures, …) Sufficiently fast to be used inline. See Speed notes below. Minimal false positives for Semantic type detection. See Performance notes below. Usable in either Streaming, Bulk or Record mode. Broad country/language support - including US, Canada, Mexico, Brazil, UK, Australia, much of Europe, Japan and China. Support for sharded analysis (i.e. Analysis results can be merged) Once stream is profiled then subsequent samples can be validated and/or new samples can be generated. -
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TurboVNC
High-speed, 3D-friendly, TightVNC-compatible remote desktop software
...It contains a variant of Tight encoding that is tuned for maximum performance and compression with 3D applications (VirtualGL), video, and other image-intensive workloads. TurboVNC, in combination with VirtualGL, provides a complete solution for remotely displaying 3D applications with interactive performance. TurboVNC's high-speed encoding methods have been adopted by TigerVNC and libvncserver, and TurboVNC is also compatible with any other TightVNC derivative. TurboVNC forked from TightVNC in 2004 and still covers all of the TightVNC 1.3.x features, but TurboVNC contains numerous feature enhancements and bug fixes relative to TightVNC, and it compresses 3D and video workloads much better than TightVNC while using generally only 5-20% of the CPU time of the latter. ... -
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VirtualGL
3D Without Boundaries
VirtualGL redirects 3D commands from a Unix/Linux OpenGL application onto a server-side GPU and converts the rendered 3D images into a video stream with which remote clients can interact to view and control the 3D application in real time. -
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GETL
ETL engine based on Groovy
...The data structures tend to change over time, or not be known in advance, working with them must be maintained; 3. All routine work ETL should be automated wherever possible; 4. Compiling the code on the fly bail speed and reserve for the optimization; 5. Sophisticated class hierarchy guarantee easy connection of other open source solutions. -
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SciMLBenchmarks.jl
Benchmarks for scientific machine learning (SciML) software
SciMLBenchmarks.jl holds webpages, pdfs, and notebooks showing the benchmarks for the SciML Scientific Machine Learning Software ecosystem. -
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WhereTraits.jl
Traits for julia: dispatch on whatever you want using where syntax
...In addition, WhereTraits comes with a standardized way how to resolve ambiguities among traits, by defining an order among the traits with @traits_order. Under the hood @traits uses normal function dispatch to achieve the speed and flexibility, however, julia function dispatch can lead to ambiguities. With traits these can easily happen if someone defines @traits for the same standard dispatch but using different traits.
