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Pkg.jl Alternatives
Similar projects and alternatives to Pkg.jl
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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parca
Continuous profiling for analysis of CPU and memory usage, down to the line number and throughout time. Saving infrastructure cost, improving performance, and increasing reliability.
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SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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TriangularSolve.jl
rdiv!(::AbstractMatrix, ::UpperTriangular) and ldiv!(::LowerTriangular, ::AbstractMatrix)
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AutoMLPipeline.jl
A package that makes it trivial to create and evaluate machine learning pipeline architectures.
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ScientificTypes.jl
An API for dispatching on the "scientific" type of data instead of the machine type
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better Pkg.jl alternative or higher similarity.
Pkg.jl reviews and mentions
Posts with mentions or reviews of Pkg.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-10.
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Julia 1.9 Highlights
There was a "bug" (or just unhandled caching case) that effected the Pluto notebook system that required precompilation each time. This is because Pluto notebooks kept a manifest (so they always instantiated with the same packages every time for full reproducibility) and the instantiation of that manifest triggered not just package running but also precompilation. That was fixed in https://github.com/JuliaLang/Pkg.jl/pull/3378, with a larger discussion in https://discourse.julialang.org/t/first-pluto-notebook-launc.... That should largely remove this issue as in included in the v1.9 release (it was first in v1.9-RC2 IIRC).
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Unable to load PDMats package.
The closest thing I got to is this and I don't even understand what they are saying.
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Why Fortran is easy to learn
Julia's compiler is made to be extendable. GPUCompiler.jl which adds the .ptx compilation output for example is a package (https://github.com/JuliaGPU/GPUCompiler.jl). The package manager of Julia itself... is an external package (https://github.com/JuliaLang/Pkg.jl). The built in SuiteSparse usage? That's a package too (https://github.com/JuliaLang/SuiteSparse.jl). It's fairly arbitrary what is "external" and "internal" in a language that allows that kind of extendability. Literally the only thing that makes these packages a standard library is that they are built into and shipped with the standard system image. Do you want to make your own distribution of Julia that changes what the "internal" packages are? Here's a tutorial that shows how to add plotting to the system image (https://julialang.github.io/PackageCompiler.jl/dev/examples/...). You could setup a binary server for that and now the first time to plot is 0.4 seconds.
Julia's arrays system is built so that most arrays that are used are not the simple Base.Array. Instead Julia has an AbstractArray interface definition (https://docs.julialang.org/en/v1/manual/interfaces/#man-inte...) which the Base.Array conforms to, and many effectively standard library packages like StaticArrays.jl, OffsetArrays.jl, etc. conform to, and thus they can be used in any other Julia package, like the differential equation solvers, solving nonlinear systems, optimization libraries, etc. There is a higher chance that packages depend on these packages then that they do not. They are only not part of the Julia distribution because the core idea is to move everything possible out to packages. There's not only a plan to make SuiteSparse and sparse matrix support be a package in 2.0, but also ideas about making the rest of linear algebra and arrays themselves into packages where Julia just defines memory buffer intrinsic (with likely the Arrays.jl package still shipped with the default image). At that point, are arrays not built into the language? I can understand using such a narrow definition for systems like Fortran or C where the standard library is essentially a fixed concept, but that just does not make sense with Julia. It's inherently fuzzy.
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MlJ.jl: A Julia Machine Learning Framework
This is exacerbated by the fact that Julia's Pkg.jl does not yet support conditional/optional dependencies [0]. A lot of these meta packages tend to pull everything but the kitchen sink.
[0]: https://github.com/JuliaLang/Pkg.jl/issues/1285
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Adding packages in Julia extremely painful
The LTS release is over two years old, and Julia has received a lot of developer attention since then, resulting in new features and performance improvements that tutorial authors don't want to do without. You can safely use the latest stable release (v1.5.3), although you may also want to apply the Git registry fix (https://github.com/JuliaLang/Pkg.jl/issues/2014#issuecomment-730676631) for further improvements in download/setup speed.
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A note from our sponsor - SaaSHub
www.saashub.com | 24 Apr 2024
Stats
Basic Pkg.jl repo stats
5
598
9.0
6 days ago
JuliaLang/Pkg.jl is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of Pkg.jl is Julia.
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