Search Results for "you"
Sort By:
Showing 116 open source projects for "you"
View related business solutions-
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.
-
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
-
1
TensorBoardLogger.jl
Easy peasy logging to TensorBoard with Julia
...It can also be used to deserialize TensoBoard's .proto files. The fundamental type defined in this package is a TBLogger, which behaves like other standard loggers in Julia such as ConsoleLogger or TextLogger. You can create one by passing it the path to the folder where you want to store the data. You can also pass an optional second argument to specify the behaviour in case there already exists a document at the given path. -
2
MATLAB.jl
Calling MATLAB in Julia through MATLAB Engine
The MATLAB.jl package provides an interface for using MATLAB® from Julia using the MATLAB C api. In other words, this package allows users to call MATLAB functions within Julia, thus making it easy to interoperate with MATLAB from the Julia language. You cannot use MATLAB.jl without having purchased and installed a copy of MATLAB® from MathWorks. This package is available free of charge and in no way replaces or alters any functionality of MathWorks's MATLAB product. -
3
JuliaWorkshop
Intensive Julia workshop that takes you from zero to hero
...It targets people already familiar with programming, so that the established basics such as for-loops are skipped through quickly and efficiently. Nevertheless, it assumes only rudimentary programming familiarity and does explain concepts that go beyond the basics. The goal of the workshop is to take you from zero to hero (regarding Julia): even if you know nothing about Julia, by the end you should be able to use it like a pro. The material has been updated during July-December 2023 to Julia v1.9+ and the corresponding latest stable versions of used packages -
4
Infiltrator.jl
No-overhead breakpoints in Julia
This package provides the @infiltrate macro, which acts as a breakpoint with negligible runtime performance overhead. Note that you cannot access other function scopes or step into further calls. Use an actual debugger if you need that level of flexibility. Running code that ends up triggering the @infiltrate REPL mode via inline evaluation in VS Code or Juno can cause issues, so it's recommended to always use the REPL directly. When the infiltration point is hit, it will drop you into an interactive REPL session that lets you inspect local variables and the call stack as well as execute arbitrary statements in the context of the current local and global scope. -
Add Two Lines of Code. Get Full APM.Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
-
5
CompatHelper.jl
Automatically update the [compat] entries for your Julia dependencies
CompatHelper.jl is a Julia package which keeps your Project.toml [compat] entries up to date. -
6
WordCloud.jl
Word cloud generator in julia
...The importance of each word is shown with its font size, position, or color. WordCloud.jl is the perfect tool for generating word clouds, offering several advantages. You have control over every aspect of generating a word cloud. You can customize the shape, color, angle, position, distribution, density, and spacing to align with your preferences and artistic style. This visualization solution guarantees precise results. Each word appears only once, and its font size is determined solely by the provided weight. ... -
7
DynamicQuantities.jl
Lightweight + fast physical quantities in Julia
DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods. -
8
Makie
Interactive data visualizations and plotting in Julia
...Each backend re-exports all of Makie.jl so you don't have to install or load it explicitly. -
9
ComponentArrays.jl
Arrays with arbitrarily nested named components
...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 targets are for use in DifferentialEquations.jl and Optim.jl, but anything that requires flat vectors is fair game. -
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
-
10
Calculus.jl
Calculus functions in Julia
The Calculus package provides tools for working with the basic calculus operations of differentiation and integration. You can use the Calculus package to produce approximate derivatives by several forms of finite differencing or to produce exact derivatives using symbolic differentiation. You can also compute definite integrals by different numerical methods. -
11
CUDA.jl
CUDA programming in Julia
...With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will happen automatically when you install the package using Julia's package manager. -
12
LLVM.jl
Julia wrapper for the LLVM C API
A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language. -
13
HCubature.jl
Pure-Julia multidimensional h-adaptive integration
...Because hcubature is written purely in Julia, the integrand f(x) can return any vector-like object (technically, any type supporting +, -, * real, and norm: a Banach space). You can integrate real, complex, and matrix-valued integrands, for example. Note that HCubature assumes that your function f(x) can be computed at arbitrary points in the integration domain. (This is the ideal way to do numerical integration.) If you instead have f(x) precomputed at a fixed set of points, such as a Cartesian grid, you will need to use some other method (e.g. ... -
14
Coluna.jl
Branch-and-Price-and-Cut in Julia
Coluna is a branch-and-price-and-cut framework written in Julia. You write an original MIP that models your problem using the JuMP modeling language and our specific extension BlockDecomposition offers a syntax to specify the problem decomposition. Then, Coluna reformulates the original MIP and optimizes the reformulation using the algorithms you choose. Coluna aims to be very modular and tweakable so that you can define the behavior of your customized branch-and-price-and-cut algorithm. -
15
ChaosTools.jl
Tools for the exploration of chaos and nonlinear dynamics
A Julia module that offers various tools for analyzing nonlinear dynamics and chaotic behavior. It can be used as a standalone package, or as part of DynamicalSystems.jl. All further information is provided in the documentation, which you can either find online or build locally by running the docs/make.jl file. ChaosTools.jl is the jack-of-all-trades package of the DynamicalSystems.jl library: methods that are not extensive enough to be a standalone package are added here. You should see the full DynamicalSystems.jl library for other packages that may contain functionality you are looking for but did not find in ChaosTools.jl. -
16
Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
...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). -
17
TensorCast.jl
It slices, it dices, it splices
This package lets you work with multi-dimensional arrays in index notation, by defining a few macros which translate this to broadcasting, permuting, and reducing operations. -
18
NBInclude.jl
import code from IJulia Jupyter notebooks into Julia programs
NBInclude is a package for the Julia language that allows you to include and execute IJulia (Julia-language Jupyter) notebook files just as you would include an ordinary Julia file. The goal of this package is to make notebook files just as easy to incorporate into Julia programs as ordinary Julia (.jl) files, giving you the advantages of a notebook (integrated code, formatted text, equations, graphics, and other results) while retaining the modularity and re-usability of .jl files. -
19
BinaryBuilder
Binary Dependency Builder for Julia
Binary Dependency Builder for Julia. Building binary packages is a pain. BinaryBuilder follows a philosophy that is similar to that of building Julia itself; when you want something done right, you do it yourself. To that end, BinaryBuilder is designed from the ground up to facilitate the building of packages within an easily reproducible and reliable Linux environment, ensuring that the built libraries and executables are deployable to every platform that Julia itself will run on. Packages are cross-compiled using a sequence of shell commands, packaged up inside tarballs, and hosted online for all to enjoy. ... -
20
Pkg Julia
Package manager for the Julia programming language
...The exact set of packages and versions in an environment is captured in a manifest file which can be checked into a project repository and tracked in version control, significantly improving reproducibility of projects. If you’ve ever tried to run code you haven’t used in a while only to find that you can’t get anything to work because you’ve updated or uninstalled some of the packages your project was using, you’ll understand the motivation for this approach. In Pkg, since each project maintains its own independent set of package versions, you’ll never have this problem again. Moreover, if you check out a project on a new system, you can simply materialize the environment described by its manifest file and immediately be up and running. -
21
GPUCompiler.jl
Reusable compiler infrastructure for Julia GPU backends
Reusable compiler infrastructure for Julia GPU backends. This package offers reusable compiler infrastructure and tooling for implementing GPU compilers in Julia. It is not intended for end users! Instead, you should use one of the packages that builds on GPUCompiler.jl, such as CUDA.jl or AMDGPU.jl. -
22
MacroTools.jl
MacroTools provides a library of tools for working with Julia code
MacroTools provides a library of tools for working with Julia code and expressions. This includes a powerful template-matching system and code-walking tools that let you do deep transformations of code in a few lines. See the docs for more info. -
23
KernelAbstractions.jl
Heterogeneous programming in Julia
KernelAbstractions (KA) is a package that enables you to write GPU-like kernels targetting different execution backends. KA is intended to be a minimal and performant library that explores ways to write heterogeneous code. -
24
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. -
25
GMT.jl
Generic Mapping Tools Library Wrapper for Julia
...However, we also acknowledge that not every one is comfortable with the GMT syntax. This syntax is needed to accommodate the immense pool of options that let you control all details of a figure but that also makes it harder to read/master.
