jstayco

MacOS (Apple Silicon)

In the terminal, run

git clone https://github.com/bmaltais/kohya_ss.git
cd kohya_ss
# Patch these files into top level/root project folder
# Then run the next command
bash ./macos.sh

dylanmckay

#! /usr/bin/env ruby

# NOTE: Requires Ruby 2.1 or greater.

# This script can be used to parse and dump the information from
# the 'html/contact_info.htm' file in a Facebook user data ZIP download.
#
# It prints all cell phone call + SMS message + MMS records, plus a summary of each.
#
# It also dumps all of the records into CSV files inside a 'CSV' folder, that is created

irabinovitch

Day 1

The Tidyverse and the Future of the Monitoring Toolchain

John Rauser

Martyrs on Film: Learning to hate the #oncallselfie

Alice Goldfuss (GitHub)

Monitoring Things At Your Day Job

Bryan Liles (CapitalOne)

dennybritz

# Helper function to plot a decision boundary.
# If you don't fully understand this function don't worry, it just generates the contour plot below.
def plot_decision_boundary(pred_func):
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    # Predict the function value for the whole gid

edasque

<style type="text/css">
#dashboard_list ul {
    margin:20px, 40px, 40px, 10px;
    overflow:hidden;
}
#dashboard_list li {
    line-height:1.5em;
    float:left;
    display:inline;
}

MLnick

// Python RDD creation functions //
  
// SequenceFile converted to Text and then to String
def sequenceFileAsText(path: String) = {
    implicit val kcm = ClassManifest.fromClass(classOf[Text])
    implicit val fcm = ClassManifest.fromClass(classOf[SequenceFileAsTextInputFormat])
    
    new JavaPairRDD(sc
      .newAPIHadoopFile[Text, Text, SequenceFileAsTextInputFormat](path)
      .map{ case (k, v) => (k.toString, v.toString) }

granturing

public class HCatInputFormat extends InputFormat<SerializableWritable<Writable>, HCatRecord> {
	
	private final org.apache.hcatalog.mapreduce.HCatInputFormat input;
	
	public HCatInputFormat() {
		input = new org.apache.hcatalog.mapreduce.HCatInputFormat();
	}

	@Override
	public RecordReader<SerializableWritable<Writable>, HCatRecord> createRecordReader(

diogomonica

require 'rubygems'
require 'mechanize'

FIRST_NAME = 'FIRST_NAME'
LAST_NAME = 'LAST_NAME'
PHONE = 'PHONE'
EMAIL = '[email protected]'

PARTY_SIZE = 2
SCHEDULE_RANGE = { :start_time => '19:00', :end_time => '20:30' }

MLnick

// An Example is an observation with optional target value and features in the form of a vector of Doubles
case class Example(target: Option[Double] = None, features: Vector[Double])

// Base model API looks something like:
abstract class BaseModel(val modelSettings: Settings)
  extends Serializable
  with Logging {

  def fit(data: RDD[Example])

MLnick

import spark.streaming.{Seconds, StreamingContext}
import spark.storage.StorageLevel
import spark.streaming.examples.twitter.TwitterInputDStream
import com.twitter.algebird._
import spark.streaming.StreamingContext._
import spark.SparkContext._

/**
 * Example of using CountMinSketch monoid from Twitter's Algebird together with Spark Streaming's
 * TwitterInputDStream