gdtm86

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/

gdtm86

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

gdtm86

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

gdtm86

Producer

Setup
bin/kafka-topics.sh --zookeeper esv4-hcl197.grid.linkedin.com:2181 --create --topic test-rep-one --partitions 6 --replication-factor 1
bin/kafka-topics.sh --zookeeper esv4-hcl197.grid.linkedin.com:2181 --create --topic test --partitions 6 --replication-factor 3

Single thread, no replication

bin/kafka-run-class.sh org.apache.kafka.clients.tools.ProducerPerformance test7 50000000 100 -1 acks=1 bootstrap.servers=esv4-hcl198.grid.linkedin.com:9092 buffer.memory=67108864 batch.size=8196

gdtm86

/**
 * D Holbrook
 *
 * Code Club: PO1
 *
 * (*) Define a binary tree data structure and related fundamental operations.
 *
 * Use whichever language features are the best fit (this will depend on the language you have selected).  The following operations should be supported:
 *
 *    Constructors

gdtm86

FROM    ubuntu:precise
MAINTAINER Bohdan Mushkevych

# Installing Oracle JDK
RUN apt-get -y install python-software-properties ;\
	add-apt-repository ppa:webupd8team/java ;\
	apt-get update && apt-get -y upgrade ;\
	echo oracle-java7-installer shared/accepted-oracle-license-v1-1 select true | /usr/bin/debconf-set-selections ;\
	apt-get -y install oracle-java7-installer && apt-get clean ;\
	update-alternatives --display java ;\

gdtm86

/*
 * Copyright (c) 2013. Regents of the University of California
 *
 * 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
 *
 * Unless required by applicable law or agreed to in writing, software

gdtm86

package object mail {

  implicit def stringToSeq(single: String): Seq[String] = Seq(single)
  implicit def liftToOption[T](t: T): Option[T] = Some(t)

  sealed abstract class MailType
  case object Plain extends MailType
  case object Rich extends MailType
  case object MultiPart extends MailType

gdtm86

Tuning Storm+Trident

Tuning a dataflow system is easy:

The First Rule of Dataflow Tuning:
* Ensure each stage is always ready to accept records, and
* Deliver each processed record promptly to its destination

gdtm86

Install Steps for RMR

These steps have been tested with

• Oracle Linux 6.4
• RHEL 6.5
• CDH 5.1

Note I wish this was parceled up.

Step 0 (all nodes)