Search Results for "hash-linux" - Page 11
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Showing 390 open source projects for "hash-linux"
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Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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ZMQ.jl
Julia interface to ZMQ
A Julia interface to ZeroMQ. ZMQ.jl is a Julia interface to ZeroMQ, The Intelligent Transport Layer. -
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InvertibleNetworks.jl
A Julia framework for invertible neural networks
Building blocks for invertible neural networks in the Julia programming language. -
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NLPModels.jl
Data Structures for Optimization Models
This package provides general guidelines to represent non-linear programming (NLP) problems in Julia and a standardized API to evaluate the functions and their derivatives. The main objective is to be able to rely on that API when designing optimization solvers in Julia. -
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Oxygen.jl
A breath of fresh air for programming web apps in Julia
A breath of fresh air for programming web apps in Julia. Oxygen is a micro-framework built on top of the HTTP.jl library. Breathe easy knowing you can quickly spin up a web server with abstractions you're already familiar with. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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EllipsisNotation.jl
Julia-based implementation of ellipsis array indexing notation
Julia-based implementation of ellipsis array indexing notation. This implements the notation .. for indexing arrays. It's similar to Python, in that it means "all the columns before (or after)". Note: .. slurps dimensions greedily, meaning that the first occurrence of .. in an index expression creates as many slices as possible. Other instances of .. afterward are treated simply as slices. Usually, you should only use one instance of .. in an indexing expression to avoid possible confusion. -
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InferOpt.jl
Combinatorial optimization layers for machine learning pipelines
InferOpt.jl is a toolbox for using combinatorial optimization algorithms within machine learning pipelines. It allows you to create differentiable layers from optimization oracles that do not have meaningful derivatives. Typical examples include mixed integer linear programs or graph algorithms. -
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Manopt.jl
Optimization on Manifolds in Julia
Optimization Algorithm on Riemannian Manifolds. A framework to implement arbitrary optimization algorithms on Riemannian Manifolds. Library of optimization algorithms on Riemannian manifolds. Easy-to-use interface for (debug) output and recording values during an algorithm run. Several tools to investigate the algorithms, gradients, and optimality criteria. -
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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... -
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ImageInTerminal.jl
Julia package for displaying images in the terminal using ANSI colors
ImageInTerminal is a drop-in package that once imported changes how a single Colorant and whole Colorant arrays (regular images) are displayed in the interactive REPL. The displayed images will be downscaled to fit into the size of your active terminal session. By default, this package will detect if your running terminal supports 24-bit colors (true colors). If it does, the image will be displayed in 24-bit colors, otherwise, it falls back to 8-bit (256 colors). -
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ColorSchemes.jl
colorschemes, colormaps, gradients, and palettes
Color schemes, colormaps, gradients, and palettes. Choose ColorSchemes with care. Refer to Peter Kovesi's PerceptualColourMaps package, or to Fabio Crameri's Scientific Colour Maps for more information. If you want to make more advanced ColorSchemes, use linear-segment dictionaries or indexed lists, and use functions to generate color values, see the make_colorscheme() function in the ColorSchemeTools.jl package. -
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ComponentArrays.jl
Arrays with arbitrarily nested named components
The main export of this package is the ComponentArray type. "Components" of ComponentArrays are really just array blocks that can be accessed through a named index. This will create a new ComponentArray whose data is a view into the original, allowing for standalone models to be composed together by simple function composition. In essence, ComponentArrays allow you to do the things you would usually need a modeling language for, but without actually needing a modeling language. The main... -
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The ARCHModels Package for Julia
A Julia package for estimating ARMA-GARCH models
ARCH (Autoregressive Conditional Heteroskedasticity) models are a class of models designed to capture a feature of financial returns data known as volatility clustering, i.e., the fact that large (in absolute value) returns tend to cluster together, such as during periods of financial turmoil, which then alternate with relatively calmer periods. This package provides efficient routines for simulating, estimating, and testing a variety of GARCH models. ARCH (Autoregressive Conditional... -
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EAGO.jl
A development environment for robust and global optimization
EAGO is an open-source development environment for robust and global optimization in Julia. EAGO is a deterministic global optimizer designed to address a wide variety of optimization problems, emphasizing nonlinear programs (NLPs), by propagating McCormick relaxations along the factorable structure of each expression in the NLP. Most operators supported by modern automatic differentiation (AD) packages (e.g., +, sin, cosh) are supported by EAGO and a number of utilities for sanitizing... -
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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... -
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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,... -
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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... -
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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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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function... -
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GraphNeuralNetworks.jl
Graph Neural Networks in Julia
GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl. -
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StatProfilerHTML.jl
Show Julia profiling data in an explorable HTML page
This module formats the output from Julia's Profile module into an html rendering of the source function lines and functions, allowing for interactive exploration of any bottlenecks that may exist in your code. -
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ReservoirComputing.jl
Reservoir computing utilities for scientific machine learning (SciML)
ReservoirComputing.jl provides an efficient, modular and easy-to-use implementation of Reservoir Computing models such as Echo State Networks (ESNs). For information on using this package please refer to the stable documentation. Use the in-development documentation to take a look at not-yet-released features. -
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101-0250-00
ETH course - Solving PDEs in parallel on GPUs
This course aims to cover state-of-the-art methods in modern parallel Graphical Processing Unit (GPU) computing, supercomputing and code development with applications to natural sciences and engineering. -
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CoordinateTransformations.jl
A fresh approach to coordinate transformations
CoordinateTransformations is a Julia package to manage simple or complex networks of coordinate system transformations. Transformations can be easily applied, inverted, composed, and differentiated (both with respect to the input coordinates and with respect to transformation parameters such as rotation angle). Transformations are designed to be light-weight and efficient enough for, e.g., real-time graphical applications, while support for both explicit and automatic differentiation makes... -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
ImplicitGlobalGrid is an outcome of a collaboration of the Swiss National Supercomputing Centre, ETH Zurich (Dr. Samuel Omlin) with Stanford University (Dr. Ludovic Räss) and the Swiss Geocomputing Centre (Prof. Yuri Podladchikov). It renders the distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid almost trivial and enables close to ideal weak scaling of real-world applications on thousands of GPUs [1, 2, 3]. ImplicitGlobalGrid relies on the... -
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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...
