Konduit | A declarative UI Framework for android using Kotlin | Android library
kandi X-RAY | Konduit Summary
kandi X-RAY | Konduit Summary
Konduit - is a React inspired way to bind data to Android Views. Konduit is a layer on top of MVP where a Presenter talks to a View interface. Instead of calling the View interface directly you declare your UI using Widgets and Konduit will inform the View about changes automatically. In traditional MVP implementations you have to update the View when. Widgets are immutable data objects which describe the state of a View and provides listeners for user interactions. Each Widget will be bound to the corresponding Android View, a 1:1 mapping. The immutability of the Widgets allows clever diffing and reduces unnecessary updates of the Views. Instead of calling the View directly you have to implement the build() function which should return a collection of Widgets describing your UI state. The build() function will be automatically called by Konduit when you change the state by calling setState { }. You don't have to worry anymore if your view is currently detached (view == null) causing NullPointerExceptions. When Konduit detects changes compared to the previous Widgets it will inform the View by calling the single render(List) method. This method receives all Widgets at once which is crucial for testing (see later). Konduit then performs a second diff per Widget and binds changed properties to the correct Android View.
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Konduit Examples and Code Snippets
Lines of Code : 15License : Permissive (Apache-2.0)
Copyoverride fun build(context: BuildContext): List {
return widgetList {
text {
key = R.id.counter_label
text = "Clicked $count times"
}
button {
key = R.id.increment
text @Test
fun `button click increments`() {
val ui = MainPresenter().testUi()
// Given the UI shows the initial state
assertThat(ui.widget(R.id.counter_label).text).contains("0")
// When clicking the increment bu Copyright 2018 Pascal Welsch
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unl Community Discussions
Trending Discussions on Konduit
QUESTION
I'm trying tu buld projetct with DL4J .
I did the following steps from ths website : https://deeplearning4j.konduit.ai/getting-started/quickstart
When I do this command mvn clean install
I get the following error :
...ANSWER
Answered 2021-Jun-08 at 21:36It looks like you modified something? Sorry if I'm not quite following what you did, but that error message indicates a missing pom.xml. The specific steps you follow are:
- Clone the repository
QUESTION
After several weeks of development, I have finally created an AI in an Android App that works using the matrix manipulation API provided by the Nd4j library. These were imported into the project with gradle, following this documentation.
Unfortunately, I'm finding out that Nd4j depends on some release-killingly large run time libraries, especially libnd4jcpu.so, which is about 150mb per abi platform, leading to apk sizes upwards of half a gigabyte! The average app size you'll find on the Google Play store is about 11.5MB.
The compressed download size limit of Android App Bundles on Google Play is 150MB.
The problem of how to reduce the size of the Dl4j dependencies was raised in a previous StackOverflow question. No solution could be offered however, except just to be more selective about what platforms you support. Again, per abi platform, that still means a minimum APK size of at minimum ~200MB.
One has to wonder why the Deeplearning4J community has gone to the effort of supporting Android mobile development in the first place, and why the inevitable problem of runtime library dependency sizes isn't so much as mentioned anywhere in the documentation.
Surely I am missing something here?
...ANSWER
Answered 2021-Mar-02 at 13:17"Nd4j" is actually libraries + self contained c++ library compiled to native binaries per platform bundled in to the jar for fast performance. You typically want to strip down those dependencies in your build. You can see how to do that here:
https://github.com/bytedeco/javacpp-presets/wiki/Reducing-the-Number-of-Dependencies
Nd4j relies on javacpp for packaging. In short, you can either specify -Dplatform=android-x86_64 or android-arm64 (depending on the architecture) in your maven/gradle build if you use nd4j-native-platform or you can just use the nd4j-native dependency (no classifier) + the classifier for your platform.
Editing my response a bit sorry I didn't get the time to fully read your question this morning. Let me respond point by point.
First of all this: "One has to wonder why the Deeplearning4J community has gone to the effort of supporting Android mobile development in the first place.."
First of all fair point and I want to work on this. Please be open minded when working with us here a bit. Generally people have specific requirements and work with us on their specific circumstances. Sometimes we help them minimize binary size via #2.
Regarding this: "especially libnd4jcpu.so, which is about 150mb per abi platform, " as it stands generally folks come with different spins of their apps. We've admittedly focused more on op coverage than binary size. There's a minifier we have that could help: https://github.com/eclipse/deeplearning4j/tree/master/libnd4j/minifier I'm happy to help with your use case if you can be more specific, but it's not quite a "just read the docs and go about it on your own" experience.
Surely I am missing something here?
QUESTION
I was following this tutorial trying to visualize my models training progress: https://deeplearning4j.konduit.ai/tuning-and-training/visualization The simple code for the server setup is:
...ANSWER
Answered 2021-Feb-23 at 12:3712:39:05.487 [vert.x-eventloop-thread-0] INFO org.deeplearning4j.ui.VertxUIServer - Deeplearning4j UI server started at: http://localhost:9000`
12:39:05.490 [main] INFO org.deeplearning4j.ui.VertxUIServer - StatsStorage instance attached to UI: InMemoryStatsStorage(uid=bd548909)
12:39:05.803 [Thread-5] INFO org.deeplearning4j.ui.VertxUIServer - Deeplearning4j UI server is auto-stopping after thread (name: main) died.
QUESTION
I trained my model in python keras. I am trying to load that in java code but getting following error How to fix this problem.
Ref:
https://towardsdatascience.com/deploying-keras-deep-learning-models-with-java-62d80464f34a
...ANSWER
Answered 2020-Apr-02 at 08:34You are using the functionality for the sequential model import, but are creating the model using a functional API.
To import models created with the functional API you need to use a different importer. https://deeplearning4j.konduit.ai/keras-import/model-functional shows how to do that.
The TL;DR of it is that you have to use
KerasModelImport.importKerasModelAndWeights(simpleMlp);
instead of
KerasModelImport.importKerasSequentialModelAndWeights(simpleMlp);
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