donigian

import tensorflow as tf

with tf.device('/cpu:0'):
    a_c = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a-cpu')
    b_c = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b-cpu')
    c_c = tf.matmul(a_c, b_c, name='c-cpu')

with tf.device('/gpu:0'):
    a_g = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a-gpu')
    b_g = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b-gpu')

donigian

#!/bin/bash
echo "Checking for CUDA and installing."
# Check for CUDA and try to install.
if ! dpkg-query -W cuda; then
  # The 16.04 installer works with 16.10.
  curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
  dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
  apt-get update
  apt-get install cuda -y
fi

donigian

configure the python2.7 environment:

conda create -n py27 python=2.7
source activate py27
conda install notebook ipykernel
ipython kernel install


configure the python3.5 environment:

donigian

Building a Production Data Warehouse

So I hear business is going well...customers are flocking to your site/app...people just can't get enough? Congratulations, this is a huge step most companies don't reach.

What now? Well, business analysts, modelers, engineers, customer support and marketing want to access an authoritative, consistent, timely access to your organization's data. Perhaps we can build some beautiful dashboards to visualize KPIs for executive management.

You're in need of a Data Warehouse? Maybe a Data Lake? Where do you start? What sort of considerations & alternatives should you think about.

Turns out there are a variety of things you must take into consideration depending on your requirements. A good place to start is to interview & establish a relationship with team leads to better understand their needs. As you're going through the discovery process, here's an algorithm of the types of things you'll need to consider in your discovery phase.

What is a Data Warehouse? Data Lak

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class InterviewQuestions
  def initialize(count=0)
    @count = 0
  end

  def count_duplicates(arr)
    arr.sort! # your favorite sorting algo (n log n)
    arr.each_with_index {|index|
      if (index + 1 < arr.length)
        if (arr[index+1]==arr[index])

donigian

Advice to my junior software engineering students

Since starting to teach Computer Science at Pasadena Community College, I've often wondered what advice I would give to my previous self 15 years ago when I first embarked on this journey to become a software engineer.

Here's what I would tell previous myself (ie. current students)...

####Best career advice I ever got

• Find what you Love...that you can be Great at...for which others will Pay you!
• Love it or leave it

####Be a Craftsman

donigian

#!/usr/bin/env ruby
require 'gli'
include GLI::App

program_desc 'Implementing Command Suite Application for Git'

version Git::VERSION

subcommand_option_handling :normal
arguments :strict

donigian

/* Consider the following table...*/
create table data (
     id integer primary key, 
     status char(1) not null,
     data1 varchar2(10) not null, 
     data2 varchar2(10) not null);
 
/* You can split via most frequent vs rarely used table columns */

create table data_main (

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/* Purpose of DomainShardRecord is to store/correlate Domain Partition for a particular userId. 
To surpass the max column width limitation with RDBMs, you can implement additional case classes for other Entities. */
case class DomainShardRecord(userId: String, password: String, userName: String)

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/* Purpose of UserShardRecord is to store/correlate shardId for a particular userId */
case class UserShardRecord(userId: String, shardId: String)