kotlindl VS okio

This is a really nice sample app (works out of the box) that Jetbrains have made to showcase the capabilities of this Kotlin Deep Learning library https://github.com/Kotlin/kotlindl

There are Java bindings for TensorFlow, but that's quite low level. I tried to see if I can get some Keras API for Scala, but I'm no expert and haven't had enough time to invest in this, so it's stuck in alpha. Maybe I develop it slow burning over the next year. A bit envious that Kotlin has a Keras-like library.

KotlinDL 0.3 is available now on Maven Central with a variety of new features — check out all the changes that are coming to the new release!

Could you please add an issue with your use-case and proposed solution? Or write here more details. Do you need just serialization to objects in memory? or just in bytes?

While KotlinDL seems to be a good solution by Jetbrains, I would personally stick to Java frameworks like DL4J for a better community support and likely more features.

For deep learning this is your best bet: https://github.com/jetbrains/kotlindl

So, the KotlinDL library could solve classification and regression tasks on tabular data and image data.

Thank you for your feedback! Kotlin∇ is a research prototype for building differentiable programs from basic primitives. If you’re looking for a higher-level API, KotlinDL [1] or KInference [2] might be a better fit for building end-user applications such as image, speech or language processing.

[1]: https://github.com/JetBrains/KotlinDL

[2]: https://github.com/JetBrains-Research/kinference/

I am involved in the development of Android application which is a rather "thick" mobile client for a Web service. It heavily communicates with the server but also has a lot of inner logic too. So, I decided to use some features of Google Guava library to simplify development process. Here is a list of features I'm very interested in: immutable collections, base utils, collection extensions, functional programming sugar and idioms (common.collect and common.base), primitives utilities (common.primitives), hashing utilities (common.hash), concurrent utils (futures and AsyncFunction). Things I don't want to use in Android: common.cache (see question below), common.eventbus (we have better Android specific libs for this, such as Otto), common.io (we can use okio for Android now).

Yeah Kotlin's own standard library is a lot smaller than Java's currently so you'll need to use something third-party for this. Okio is a popular option https://square.github.io/okio/ it has a Buffer type which is pretty similar to Java's ByteBuffer

Use okio, it is Multiplatform now. I use this for my own library KStore

For networking, file IO, and streams in general, there's Korio and for Java; for just networking, there's LiteNetLib for C#; for what looks like data streams in general, there's Okio also for Java; and Tokio for multi-threaded IO in Rust.

Okio is nice for input/output streams, and sockets.

Sounds like you want https://square.github.io/okio/

On JVM, there is plenty of existing solution already on for multiplatform uses I'd suggest checking amazing Okio library by Square, that seems to cover most of basic use-cases.

I think the main multiplatform library for I/O currently is okio, https://github.com/square/okio. As for compression, you should probably create an expect class for the compressor and use platform specific calls for actual compression. Take a look at https://kotlinlang.org/docs/mpp-connect-to-apis.html.