Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

Overview

Hera

Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

demo

Setting up

Step 1. Plant the spy

Install the package


    pip install heraspy

Add the callback

    herasCallback = HeraCallback(
        'model-key',
        'localhost',
        4000
    )

    model.fit(X_train, Y_train, callbacks=[herasCallback])

Step 2. Start the server

Git clone this repository, then run


    cd server
    npm install
    gulp # optional, for now the build file is kept track in git
    node build/server

Step 3. Start the dashboard


    cd client
    npm install
    npm start

Using RabbitMQ

By default hera uses socket.io for messaging - both from keras callback to server, and from server to dashboard. This is to minimize the number of things one needs to install before getting up and running with hera.

However, in production socket.io is outperformed by a number of alternatives, also it is good in general to decouple the server-client communication from the inter-process communitation (python -> node) so that each can be managed and optimized independently.

To demonstrate how this works Hera ships with the option to use rabbitMQ for interprocess communication. Here's how to use it.

In your model file

    from heraspy.callback import HeraCallback
    from heraspy.dispatchers.rabbitmq import get_rabbitmq_dispatcher

    herasCallback = HeraCallback(
        'model-key', 'localhost', 4000,
        dispatch=get_rabbitmq_dispatcher(
          queue='[my-queue]',
          amqps_url='amqps://[user]:[pass]@my-amqp-address'
        )
    )

In server/src/server.js

Replace the only line in the file with

    getServer({
        dispatcher: 'rabbitmq',
        dispatcherConfig: {
            amqpUrl: 'amqps://[user]:[pass]@my-amqp-address',
            amqpQueue: '[my-queue]'
        }
    }).start();

That's it! Now communication from the python process and the node webserver process goes through rabbitmq.

Credits

Aside from the obvious ones:

Owner
Keplr
Keplr
Pytorch implementation of face attention network

Face Attention Network Pytorch implementation of face attention network as described in Face Attention Network: An Effective Face Detector for the Occ

Hooks 312 Dec 09, 2022
PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

Kate Rakelly 516 Jan 05, 2023
DRIFT is a tool for Diachronic Analysis of Scientific Literature.

About DRIFT is a tool for Diachronic Analysis of Scientific Literature. The application offers user-friendly and customizable utilities for two modes:

Rajaswa Patil 108 Dec 12, 2022
Code for the tech report Toward Training at ImageNet Scale with Differential Privacy

Differentially private Imagenet training Code for the tech report Toward Training at ImageNet Scale with Differential Privacy by Alexey Kurakin, Steve

Google Research 29 Nov 03, 2022
PyTorch wrappers for using your model in audacity!

audacitorch This package contains utilities for prepping PyTorch audio models for use in Audacity. More specifically, it provides abstract classes for

Hugo Flores García 130 Dec 14, 2022
Official implementation of the paper "Topographic VAEs learn Equivariant Capsules"

Topographic Variational Autoencoder Paper: https://arxiv.org/abs/2109.01394 Getting Started Install requirements with Anaconda: conda env create -f en

T. Andy Keller 69 Dec 12, 2022
DeLiGAN - This project is an implementation of the Generative Adversarial Network

This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Net

Video Analytics Lab -- IISc 110 Sep 13, 2022
This repository will be a summary and outlook on all our open, medical, AI advancements.

medical by LAION This repository will be a summary and outlook on all our open, medical, AI advancements. See the medical-general channel in the medic

LAION AI 18 Dec 30, 2022
Reinforcement Learning for finance

Reinforcement Learning for Finance We apply reinforcement learning for stock trading. Fetch Data Example import utils # fetch symbols from yahoo fina

Tomoaki Fujii 159 Jan 03, 2023
Static Features Classifier - A static features classifier for Point-Could clusters using an Attention-RNN model

Static Features Classifier This is a static features classifier for Point-Could

ABDALKARIM MOHTASIB 1 Jan 25, 2022
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks

Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks arXiv preprint: https://arxiv.org/abs/2201.02143. Architec

19 Nov 30, 2022
SegTransVAE: Hybrid CNN - Transformer with Regularization for medical image segmentation

SegTransVAE: Hybrid CNN - Transformer with Regularization for medical image segmentation This repo is the official implementation for SegTransVAE. Seg

Nguyen Truong Hai 4 Aug 04, 2022
A library for using chemistry in your applications

Chemistry in python Resources Used The following items are not made by me! Click the words to go to the original source Periodic Tab Json - Used in -

Tech Penguin 28 Dec 17, 2021
ANN model for prediction a spatio-temporal distribution of supercooled liquid in mixed-phase clouds using Doppler cloud radar spectra.

VOODOO Revealing supercooled liquid beyond lidar attenuation Explore the docs » Report Bug · Request Feature Table of Contents About The Project Built

remsens-lim 2 Apr 28, 2022
Record radiologists' eye gaze when they are labeling images.

Record radiologists' eye gaze when they are labeling images. Read for installation, usage, and deep learning examples. Why use MicEye Versatile As a l

24 Nov 03, 2022
Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.

Neural Magic Eye Preprint | Project Page | Colab Runtime Official PyTorch implementation of the preprint paper "NeuralMagicEye: Learning to See and Un

Zhengxia Zou 56 Jul 15, 2022
A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

Hands-on-Machine-Learning 目的 这份笔记旨在帮助中文学习者以一种较快较系统的方式入门机器学习, 是在学习Hands-on Machine Learning with Scikit-Learn and TensorFlow这本书的 时候做的个人笔记: 此项目的可取之处 原书的

Baymax 1.5k Dec 21, 2022
Generative Adversarial Text-to-Image Synthesis

###Generative Adversarial Text-to-Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee This is the

Scott Ellison Reed 883 Dec 31, 2022
Secure Distributed Training at Scale

Secure Distributed Training at Scale This repository contains the implementation of experiments from the paper "Secure Distributed Training at Scale"

Yandex Research 9 Jul 11, 2022
This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Koltun"

Learning to propose objects This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Ko

Philipp Krähenbühl 90 Sep 10, 2021