This document contains a tutorial on how to provision a spark cluster with Rstudio. You will need a machine that can run bash scripts and a functioning account on AWS.
Make sure you have an AWS account with billing. Next make sure that you have downloaded your .pem files and that you have your keys ready.
| > 'http://localhost:8000/all_data' %>% GET %>% content | |
| [[1]] | |
| [[1]]$x | |
| [1] -1.5148 | |
| [[1]]$y | |
| [1] 0.2093 | |
| [[2]] |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css"> | |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap-theme.min.css"> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js'></script> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/3.10.1/lodash.min.js'></script> | |
| <div class="container"> | |
| <div class="row"> | |
| <h1>This is a page</h1> | |
| <div class="col-md-6" id="left"> |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css"> | |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap-theme.min.css"> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js'></script> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/3.10.1/lodash.min.js'></script> | |
| <div class="container"> | |
| <div class="row"> | |
| <h1>This is a page</h1> | |
| <div class="col-md-6" id="left"> |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css"> | |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap-theme.min.css"> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js'></script> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/3.10.1/lodash.min.js'></script> | |
| <div class="container"> | |
| <div class="row"> | |
| <h1>This is a page</h1> | |
| <div class="col-md-6" id="left"> |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css"> | |
| <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap-theme.min.css"> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js'></script> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/3.10.1/lodash.min.js'></script> | |
| <div class="container"> | |
| <div class="row"> | |
| <h1>This is a page</h1> | |
| <div class="col-md-6" id="left"> |
| https://asciinema.org/ | |
| Fun with Asciinema | |
| ``` | |
| from random import choice | |
| import time | |
| from colorama import Fore, Back, Style | |
| def col(): |
| import tensorflow as tf | |
| import uuid | |
| x = tf.Variable(tf.random_normal(shape=[1,1], mean=5, stddev=2)) | |
| y = tf.sin(x) + tf.sin(x**2/2) - tf.abs(x) | |
| optimizer = tf.train.RMSPropOptimizer(0.001).minimize(-y) | |
| init = tf.global_variables_initializer() | |
| tf.summary.scalar("x-val", tf.reduce_mean(x)) |
| import tensorflow as tf | |
| import numpy as np | |
| import uuid | |
| x = tf.placeholder(shape=[None, 3], dtype=tf.float32) | |
| nn = tf.layers.dense(x, 3, activation=tf.nn.sigmoid) | |
| nn = tf.layers.dense(nn, 5, activation=tf.nn.sigmoid) | |
| encoded = tf.layers.dense(nn, 2, activation=tf.nn.sigmoid) | |
| nn = tf.layers.dense(encoded, 5, activation=tf.nn.sigmoid) | |
| nn = tf.layers.dense(nn, 3, activation=tf.nn.sigmoid) |
| \begin{align*} | |
| E[\pmb{Ax + y}] &= \pmb{A}(E[\pmb{x}]) + \pmb{y} \\ | |
| Cov[\pmb{Ax + y}] &= \pmb{A}(Cov[\pmb{x}])\pmb{A}^T | |
| \end{align*} |