yplatform
Dagger.jl
| yplatform | Dagger.jl | |
|---|---|---|
| 5 | 4 | |
| 17 | 581 | |
| - | 1.2% | |
| 7.8 | 8.9 | |
| 6 months ago | 3 days ago | |
| Shell | Julia | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
yplatform
- Dagger: a new way to build CI/CD pipelines
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I Prefer Makefiles over Package.json Scripts
Nowadays there's Windows Subsystem for Linux. There's no excuse not to successfully run "Linux" scripts on Windows.
I've been running very complex build systems via https://github.com/ysoftwareab/yplatform (disclaimer: author here) since 2016 on Linux, Mac and Windows without a problem.
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Autodocumenting Makefiles
This is exactly my experience which lead me to create https://github.com/ysoftwareab/yplatform - with a consistent make interface https://github.com/ysoftwareab/yplatform/tree/master/build.m...
PS: quite feature complete but not yet well marketed so to speak. I'm actually recording an asciinema session this week in order for a visitor to grasp quicker the mentioned benefits.
Dagger.jl
- Dagger: a new way to build CI/CD pipelines
-
DTable a new distributed table implementation in Julia using Dagger.jl
Firstly, I'll say that we already have work started to implement out-of-core directly in Dagger: https://github.com/JuliaParallel/Dagger.jl/pull/289.
With that PR in place, it should be possible to define a "storage device" which is backed by a database. I haven't had a chance to actually try this, since the PR still needs quite some work and testing, but it's definitely something on my radar!
- From Julia to Rust
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Cerebras’ New Monster AI Chip Adds 1.4T Transistors
I'm not sure that's necessarily the domain of a low-level package like CUDA.jl though (which I assume you're referring to). That kind of interface is more the domain of higher-level packages like https://github.com/JuliaParallel/Dagger.jl/ and to a lesser extent https://juliagpu.github.io/KernelAbstractions.jl/stable/. Moreover, the jury is still out on whether the built-in Distributed module is an ideal abstraction for every use-case (clusters, heterogeneous compute, etc.)
WRT Nx, my biggest question is how they'll crack the problem of still needing big balls of C++ and the shims everywhere to get acceleration. Creating a compiler that generates efficient GPU or other accelerator code is a massive research project with no clear winners, never mind the challenge of reconciling the very mutation-heavy needs of GPU compute with a mostly immutable language model.
What are some alternatives?
SheetJS js-xlsx - 📗 SheetJS Spreadsheet Data Toolkit -- New home https://git.sheetjs.com/SheetJS/sheetjs
earthly - Super simple build framework with fast, repeatable builds and an instantly familiar syntax – like Dockerfile and Makefile had a baby.
quickjs-emscripten - Safely execute untrusted Javascript in your Javascript, and execute synchronous code that uses async functions
julia - The Julia Programming Language
just - 🤖 Just a command runner
DuckDB.jl
dvc - 🦉 ML Experiments and Data Management with Git
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
dagger - Application Delivery as Code that Runs Anywhere
Metatheory.jl - General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
Scoop - A command-line installer for Windows.
Symbolics.jl - Symbolic programming for the next generation of numerical software