ZparkIO | Lantern | |
---|---|---|
1 | 1 | |
173 | 165 | |
- | - | |
3.2 | 0.0 | |
19 days ago | about 3 years ago | |
Scala | Cuda | |
MIT License | BSD 3-clause "New" or "Revised" License |
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ZparkIO
Lantern
-
Recommendations for specializing in Spark (Scala)
Yeah. The point here is that the machine learning algorithms in libraries like TensorFlow and PyTorch ultimately rely on differentiating functions. The idea behind "differentiable programming" is to make the central mathematical aspects of machine learning more first- or at least second-class citizens. So at the library level you find "autodifferentiation" in Haskell, an implementation as part of Rainier in Scala, autodiff in Rust, etc. More ambitiously, you have the Lantern system providing autodifferentiation-as-metaprogramming in Scala (but generating C++), another metaprogramming approach in Scala, and autodifferentiation as a language feature in Swift.
What are some alternatives?
zio-spark - A functional wrapper around Spark to make it works with ZIO
frameless - Expressive types for Spark.
zio-prelude - A lightweight, distinctly Scala take on functional abstractions, with tight ZIO integration
zio-http - A next-generation Scala framework for building scalable, correct, and efficient HTTP clients and servers
zio-akka-cluster - ZIO wrapper for Akka Cluster
snowpark-scala-template - Scala project template for Snowpark development
zio-entity - Zio-Entity, a distributed, high performance, functional event sourcing library
tapir - Declarative, type-safe web endpoints library
DIStage - Productivity-oriented collection of lightweight fancy stuff for Scala toolchain
zio-temporal-hello - Sample hello application using Scala 3 and zio-temporal