Search Results for "which"
Sort By:
Showing 349 open source projects for "which"
View related business solutions-
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI Studio. Switch between models without switching platforms.
-
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
-
1
Stellarium
GPL software which renders realistic skies in real time
Stellarium is a free GPL software which renders realistic skies in real time with OpenGL. It is available for Linux/Unix, Windows and macOS. With Stellarium, you really see what you can see with your eyes, binoculars or a small telescope. Stellarium is a free open source planetarium for your computer. It shows a realistic sky in 3D, just like what you see with the naked eye, binoculars or a telescope. -
2
PlantUML
Generate diagrams from textual description
...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. Diagrams are defined using a simple and intuitive language. (see PlantUML Language Reference Guide). Images can be generated in PNG, in SVG or in LaTeX format. It is also possible to generate ASCII art diagrams (only for sequence diagrams). -
3
ggplot2
An implementation of the Grammar of Graphics in R
ggplot2 is a system written in R for declaratively creating graphics. It is based on The Grammar of Graphics, which focuses on following a layered approach to describe and construct visualizations or graphics in a structured manner. With ggplot2 you simply provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it will take care of the rest. ggplot2 is over 10 years old and is used by hundreds of thousands of people all over the world for plotting. ... -
4
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. -
Cut Cloud Costs with Google Compute EngineSave on compute costs with Compute Engine. Reduce your batch jobs and workload bill 60-91% with Spot VMs. Compute Engine's committed use offers customers up to 70% savings through sustained use discounts. Plus, you get one free e2-micro VM monthly and $300 credit to start.
-
5
StaticTools.jl
Enabling StaticCompiler.jl-based compilation of (some) Julia code
Tools to enable StaticCompiler.jl-based static compilation of Julia code (or more accurately, a subset of Julia which we might call "unsafe Julia") to standalone native binaries by avoiding GC allocations and llvmcall-ing all the things. This package currently requires Julia 1.8 or greater for best results (if in doubt, check which versions are passing CI). Integration tests against StaticCompiler.jl and LoopVectorization.jl are currently run with Julia 1.8 and 1.9 on x86-64 Linux and mac; other platforms and versions may or may not work but will depend on StaticCompiler.jl support. ... -
6
MPI.jl
MPI wrappers for Julia
...Inspiration is taken from mpi4py, although we generally follow the C and not the C++ MPI API. (The C++ MPI API is deprecated.) MPI is based on a single program, multiple data (SPMD) model, where multiple processes are launched running independent programs, which then communicate as necessary via messages. As the main entry point for users, MPI.jl provides a high-level interface which loosely follows the MPI C API and is described in details in the following sections. The syntax should look familiar if you know MPI already, but some arguments may not be needed (e.g. the type or the number of elements of arrays, which are inferred automatically), others may be placed slightly differently, and others may be optional keyword arguments (e.g. for the index of the root process, or the source and destination of point-to-point communication functions). -
7
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... -
8
SPX
A simple & straight-to-the-point PHP profiling extension
SPX, which stands for Simple Profiling eXtension, is just another profiling extension for PHP. It differentiates itself from other similar extensions as being totally free and confined to your infrastructure (i.e. no data leaks to a SaaS). Very simple to use: just set an environment variable (command line) or switch on a radio button (web request) to profile your script. -
9
LIBSVM.jl
LIBSVM bindings for Julia
...Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM. Model objects are represented by Julia-type SVM which gives you easy access to model features and can be saved e.g. as JLD file. -
$300 in Free Credit Across 150+ Cloud ServicesStart your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale with Google Cloud.
-
10
ImplicitDifferentiation.jl
Automatic differentiation of implicit functions
ImplicitDifferentiation.jl is a package for automatic differentiation of functions defined implicitly, i.e., forward mappings. Those for which automatic differentiation fails. Reasons can vary depending on your backend, but the most common include calls to external solvers, mutating operations or type restrictions. Those for which automatic differentiation is very slow. A common example is iterative procedures like fixed point equations or optimization algorithms. -
11
wavesurfer.js
Navigable waveform built on Web Audio and Canvas
...With wavesurfer.js you can create anything from an HTML5 audio player to a sophisticated DJ application. wavesurfer.js works only in modern browsers supporting Web Audio. It will fallback to Audio Element without graphics in other browsers (IE 11 and lower). You can also try wavesurfer.swf which is a Flash-based fallback. The audio will start playing as you press play. A thin line will be displayed until the whole audio file is downloaded and decoded to draw the waveform. Web Audio needs the whole file to decode it in the browser. You can however load pre-decoded waveform data to draw the waveform immediately. wavesurfer.js runs on modern browsers supporting Web Audio, including Firefox, Chrome, Safari (desktop and mobile) and Opera. -
12
Wflow.jl
Hydrological modeling
...Successfully applied worldwide for analyzing flood hazards, drought, climate change impacts and land use changes, wflow is growing to be a leader in hydrology solutions. Wflow is conceived as a framework, within which multiple distributed model concepts are available, which maximizes the use of open earth observation data, making it the hydrological model of choice for data-scarce environments. Based on gridded topography, soil, land use and climate data, wflow calculates all hydrological fluxes at any given grid cell in the model at a given time step. -
13
Surrogates.jl
Surrogate modeling and optimization for scientific machine learning
...In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from evaluations of f. -
14
vue-chartjs
Vue.js wrapper for Chart.js
...You can easily create reusable chart components. You can use vue-chartjs directly in the browser without any build setup. Like in this codepen. For this case, please use the vue-chartjs.min.js which is the minified version. You also need to add the Chart.js CDN script. Chart.js does not update or re-render the chart if new data is passed. However, you can simply implement this on your own or use one of the two mixins which are included. The mixins automatically create chartData as a prop or data. And add a watcher. If data has changed, the chart will update. ... -
15
ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
...ParallelStencil relies on the native kernel programming capabilities of CUDA.jl and AMDGPU.jl and on Base.Threads for high-performance computations on GPUs and CPUs, respectively. It is seamlessly interoperable with ImplicitGlobalGrid.jl, which renders the distributed parallelization of stencil-based GPU and CPU apps. -
16
Go Recipes
Collection of handy tools for Go projects
...Tell Go compiler which versions of upstreams to include in your build. Tell all users of your module how to deal with versions of your module. -
17
CausalityTools.jl
Algorithms for detecting associations, dynamical influences
...Association measures from conventional statistics, information theory, and dynamical systems theory, for example, distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more. A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can think of. For example, we offer the generic SurrogateTest, which is fully compatible with TimeseriesSurrogates.jl, and the LocalPermutationTest for conditional independence testing. -
18
NCDatasets.jl
Load and create NetCDF files in Julia
NCDatasets allows one to read and create netCDF files. NetCDF data set and attribute list behave like Julia dictionaries and variables like Julia arrays. This package implements the CommonDataModel.jl interface, which means that the datasets can be accessed in the same way as GRIB files opened with GRIBDatasets.jl. -
19
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 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. -
20
Books.jl
Create books with Julia
...For many standard output types, such as DataFrames and plots, the package can run your code and will automatically handle proper embedding in the output documents, and also try to guess suitable captions and labels. Also, it is possible to work via the live server, which shows changes within seconds. -
21
CondaPkg.jl
Add Conda dependencies to your Julia project
Add Conda dependencies to your Julia project. This package is a lot like Pkg from the Julia standard library, except that it is for managing Conda packages. Conda dependencies are defined in CondaPkg.toml, which is analogous to Project.toml. CondaPkg will install these dependencies into a Conda environment specific to the current Julia project. Hence dependencies are isolated from other projects or environments. Functions like add, rm, status exist to edit the dependencies programmatically. Or you can do pkg> conda add some_package to edit the dependencies from the Pkg REPL. -
22
Rackula
Drag and drop rack visualizer
...The tool emphasizes clarity and ease of use so that both hobbyists and professionals can plan wiring, sizing, and space allocation before physically committing to changes. It also supports exporting and sharing of layouts, which is useful for documentation or collaboration across teams. Community contributions focus on persistent storage, self-hosting guides, and UI improvements that make Rackula more adaptable for different environments. -
23
NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers
...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 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. ... -
24
Convex.jl
A Julia package for disciplined convex programming
...These formulations rely on the problem being modeled by combining Convex.jl's "atoms" or primitives according to certain rules which ensure convexity, called the disciplined convex programming (DCP) ruleset. If these atoms are combined in a way that does not ensure convexity, the extended formulations are often invalid. -
25
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.
