Apache SINGA Training in Sheffield

SINGA is a general distributed deep learning platform for training big deep learning models over large datasets.

Sheffield

2nd Floor, The Portergate Ecclesall Road
Sheffield, SYK S11 8NX
United Kingdom
South Yorkshire GB
Sheffield
The Ecclesall Road Centre is located within The Portergate building, Sheffield's premier business address. Despite its town centre location the terraced...Read more

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Course Outlines

Code Name Duration Outline
singa Mastering Apache SINGA 21 hours

SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework.

After completing this course, delegates will:

  • understand SINGA’s structure and deployment mechanisms
  • be able to carry out installation / production environment / architecture tasks and configuration
  • be able to assess code quality, perform debugging, monitoring
  • be able to implement advanced production like training models, embedding terms, building graphs and logging

 

Introduction

Installation

Quick Start

Programming

  • NeuralNet
    • Layer
    • Param
  • TrainOneBatch
  • Updater 

Distributed Training

Data Preparation

Checkpoint and Resume

Python Binding

Performance test and Feature extraction

Training on GPU

Examples

  • Feed-forward models
    • CNN
    • MLP
  • RBM + Auto-encoder
  • Vanilla RNN for language modelling
  • Char-RNN

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