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Online Learning with Akka

This repository contains a simple collection of actors which we use to train a convolutional neural network using with images from the MNIST database. The system consists of three actors:

  • Producer is responsible for sending images to Consumer
  • Consumer trains a neural network after receiving messages
  • Coordinator is used to facilitate communcation between Consumer and Producer, and the outside world.

The classifier is a neural net, build via Deeplearning4j.

To build the application, run:

sbt assembly

This will produce target/scala-2.13/akkatrain.jar, which can be used like any other jar. To start a demo application, run:

java -cp target/scala-2.13/akkatrain.jar org.cmhh.Main

Note that dl4j is huge, so a fat jar will weigh in at well over 1GB. Modify build.sbt if you have dl4j already in your classpath. Also note that the dependencies are configured to use the CUDA back-end for dl4j, but the CPU back-end can be used instead (and is in project/dependencies.scala, but commented out).

About

Simple online learning example using Akka

cmhh.github.io/2021/2021-08-08-online-learning-with-akka/

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akkaakka-actordeeplearning4jmnist-classification

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Online Learning with Akka
This repository contains a simple collection of actors which we use to train a convolutional neural network using with images from the MNIST database. The system consists of three actors:
  • Producer is responsible for sending images to Consumer
  • Consumer trains a neural network after receiving messages
  • Coordinator is used to facilitate communcation between Consumer and Producer, and the outside world.
The classifier is a neural net, build via Deeplearning4j.
To build the application, run:
sbt assembly
This will produce target/scala-2.13/akkatrain.jar, which can be used like any other jar. To start a demo application, run:
java -cp target/scala-2.13/akkatrain.jar org.cmhh.Main
Note that dl4j is huge, so a fat jar will weigh in at well over 1GB. Modify build.sbt if you have dl4j already in your classpath. Also note that the dependencies are configured to use the CUDA back-end for dl4j, but the CPU back-end can be used instead (and is in project/dependencies.scala, but commented out).
About
Simple online learning example using Akka
cmhh.github.io/2021/2021-08-08-online-learning-with-akka/
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akkaakka-actordeeplearning4jmnist-classification
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Repository files navigation
Online Learning with Akka
This repository contains a simple collection of actors which we use to train a convolutional neural network using with images from the MNIST database. The system consists of three actors:
  • Producer is responsible for sending images to Consumer
  • Consumer trains a neural network after receiving messages
  • Coordinator is used to facilitate communcation between Consumer and Producer, and the outside world.
The classifier is a neural net, build via Deeplearning4j.
To build the application, run:
sbt assembly
This will produce target/scala-2.13/akkatrain.jar, which can be used like any other jar. To start a demo application, run:
java -cp target/scala-2.13/akkatrain.jar org.cmhh.Main
Note that dl4j is huge, so a fat jar will weigh in at well over 1GB. Modify build.sbt if you have dl4j already in your classpath. Also note that the dependencies are configured to use the CUDA back-end for dl4j, but the CPU back-end can be used instead (and is in project/dependencies.scala, but commented out).
About
Simple online learning example using Akka
cmhh.github.io/2021/2021-08-08-online-learning-with-akka/
Topics
akkaakka-actordeeplearning4jmnist-classification
Resources
Readme
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Repository files navigation
Online Learning with Akka
This repository contains a simple collection of actors which we use to train a convolutional neural network using with images from the MNIST database. The system consists of three actors:
  • Producer is responsible for sending images to Consumer
  • Consumer trains a neural network after receiving messages
  • Coordinator is used to facilitate communcation between Consumer and Producer, and the outside world.
The classifier is a neural net, build via Deeplearning4j.
To build the application, run:
sbt assembly
This will produce target/scala-2.13/akkatrain.jar, which can be used like any other jar. To start a demo application, run:
java -cp target/scala-2.13/akkatrain.jar org.cmhh.Main
Note that dl4j is huge, so a fat jar will weigh in at well over 1GB. Modify build.sbt if you have dl4j already in your classpath. Also note that the dependencies are configured to use the CUDA back-end for dl4j, but the CPU back-end can be used instead (and is in project/dependencies.scala, but commented out).
About
Simple online learning example using Akka
cmhh.github.io/2021/2021-08-08-online-learning-with-akka/
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akkaakka-actordeeplearning4jmnist-classification
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// Strip utm_, fbclid, gclid, etc. from all links on page
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'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
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});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
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});
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}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
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Repository files navigation
Online Learning with Akka
This repository contains a simple collection of actors which we use to train a convolutional neural network using with images from the MNIST database. The system consists of three actors:
  • Producer is responsible for sending images to Consumer
  • Consumer trains a neural network after receiving messages
  • Coordinator is used to facilitate communcation between Consumer and Producer, and the outside world.
The classifier is a neural net, build via Deeplearning4j.
To build the application, run:
sbt assembly
This will produce target/scala-2.13/akkatrain.jar, which can be used like any other jar. To start a demo application, run:
java -cp target/scala-2.13/akkatrain.jar org.cmhh.Main
Note that dl4j is huge, so a fat jar will weigh in at well over 1GB. Modify build.sbt if you have dl4j already in your classpath. Also note that the dependencies are configured to use the CUDA back-end for dl4j, but the CPU back-end can be used instead (and is in project/dependencies.scala, but commented out).
About
Simple online learning example using Akka
cmhh.github.io/2021/2021-08-08-online-learning-with-akka/
Topics
akkaakka-actordeeplearning4jmnist-classification
Resources
Readme
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// Auto-enable theater mode on YouTube
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observer.observe(document.body, { childList: true, subtree: true });
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}
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Online Learning with Akka
This repository contains a simple collection of actors which we use to train a convolutional neural network using with images from the MNIST database. The system consists of three actors:
  • Producer is responsible for sending images to Consumer
  • Consumer trains a neural network after receiving messages
  • Coordinator is used to facilitate communcation between Consumer and Producer, and the outside world.
The classifier is a neural net, build via Deeplearning4j.
To build the application, run:
sbt assembly
This will produce target/scala-2.13/akkatrain.jar, which can be used like any other jar. To start a demo application, run:
java -cp target/scala-2.13/akkatrain.jar org.cmhh.Main
Note that dl4j is huge, so a fat jar will weigh in at well over 1GB. Modify build.sbt if you have dl4j already in your classpath. Also note that the dependencies are configured to use the CUDA back-end for dl4j, but the CPU back-end can be used instead (and is in project/dependencies.scala, but commented out).
About
Simple online learning example using Akka
cmhh.github.io/2021/2021-08-08-online-learning-with-akka/
Topics
akkaakka-actordeeplearning4jmnist-classification
Resources
Readme
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document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
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Online Learning with Akka
This repository contains a simple collection of actors which we use to train a convolutional neural network using with images from the MNIST database. The system consists of three actors:
  • Producer is responsible for sending images to Consumer
  • Consumer trains a neural network after receiving messages
  • Coordinator is used to facilitate communcation between Consumer and Producer, and the outside world.
The classifier is a neural net, build via Deeplearning4j.
To build the application, run:
sbt assembly
This will produce target/scala-2.13/akkatrain.jar, which can be used like any other jar. To start a demo application, run:
java -cp target/scala-2.13/akkatrain.jar org.cmhh.Main
Note that dl4j is huge, so a fat jar will weigh in at well over 1GB. Modify build.sbt if you have dl4j already in your classpath. Also note that the dependencies are configured to use the CUDA back-end for dl4j, but the CPU back-end can be used instead (and is in project/dependencies.scala, but commented out).
About
Simple online learning example using Akka
cmhh.github.io/2021/2021-08-08-online-learning-with-akka/
Topics
akkaakka-actordeeplearning4jmnist-classification
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var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
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}
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style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
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Online Learning with Akka
This repository contains a simple collection of actors which we use to train a convolutional neural network using with images from the MNIST database. The system consists of three actors:
  • Producer is responsible for sending images to Consumer
  • Consumer trains a neural network after receiving messages
  • Coordinator is used to facilitate communcation between Consumer and Producer, and the outside world.
The classifier is a neural net, build via Deeplearning4j.
To build the application, run:
sbt assembly
This will produce target/scala-2.13/akkatrain.jar, which can be used like any other jar. To start a demo application, run:
java -cp target/scala-2.13/akkatrain.jar org.cmhh.Main
Note that dl4j is huge, so a fat jar will weigh in at well over 1GB. Modify build.sbt if you have dl4j already in your classpath. Also note that the dependencies are configured to use the CUDA back-end for dl4j, but the CPU back-end can be used instead (and is in project/dependencies.scala, but commented out).
About
Simple online learning example using Akka
cmhh.github.io/2021/2021-08-08-online-learning-with-akka/
Topics
akkaakka-actordeeplearning4jmnist-classification
Resources
Readme
MIT license
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