| html> | |
| <body> | |
| <script type="text/javascript"> | |
| var urls = [ | |
| "https://duckduckgo.com", | |
| "https://bing.com", | |
| ] |
| '''Compares the time to scan through a large tar.gz file when it is memory mapped vs when it isn't | |
| In this example train_0.tar.gz is a 46 GB tar.gz file that decompresses to 47 GB. In other words its a file that | |
| hasn't compressed much, in this case because it contains a bunch of already compressed files. | |
| $ python3 ./mmap_test.py | |
| seconds to execute w/o mmap: 134.61383328400552 | |
| seconds to execute mmpa: 43.660543900041375 | |
| ''' |
Computation Graph:
Instantiating Variable objects as well as Ops objects adds to a graph of computation that is tracked by tf.
After building your computation graph, where nodes are Variables or Ops or Constants, you can retrieve the executed and resulting value of any of the nodes in the graph.
The difference between Ops and Variables:
| $ python | |
| Python 3.5.2 (default, Jun 28 2016, 08:46:01) | |
| [GCC 6.1.1 20160602] on linux | |
| Type "help", "copyright", "credits" or "license" for more information. | |
| >>> a = [[0,0],[0,0]] | |
| >>> c = (1,1) | |
| >>> a[c[0]][c[1]] = 100 | |
| >>> a | |
| [[0, 0], [0, 100]] | |
| >>> a = [[None]*2]*2 |
| html> | |
| <body> | |
| <script type="text/javascript"> | |
| var urls = [ | |
| "https://duckduckgo.com", | |
| "https://bing.com", | |
| ] |
| import boto | |
| import boto.s3.connection | |
| import os.path | |
| def main(): | |
| aws_key = '' | |
| aws_secret = '' | |
| bucket_name = '' | |
| prefix = '' |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import matplotlib.mlab | |
| def make_bars(): | |
| d = matplotlib.mlab.csv2rec("os_visit_counts.txt",names=('os','visits')) | |
| sorted_indeces = np.argsort(d['visits']) | |
| x = d['os'][sorted_indeces] | |
| y = np.log10(d['visits'][sorted_indeces]) |
| import sys | |
| from boto.s3.connection import S3Connection | |
| def main(args): | |
| c = S3Connection() | |
| l = c.get_bucket(args[0]).list(prefix=args[1]) | |
| print "keyname,size" | |
| for k in l: | |
| print "%s,%d" % (k.name, k.size) |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| '''Makes a simple histogram from a file containing the frequency counts for each number | |
| ''' | |
| def make_histogram(): | |
| #The format of the counts is a file with two columns, separated by commas | |
| #first col: visit number (bin) |
| import argparse | |
| import tensorflow as tf | |
| import sys | |
| def flowfib(n): | |
| ''' f_n = f_n-1 + f_n-2 | |
| Fails in a somewhat interesting way, showing that ints passed to constants are cast to 32 bit signed ints, | |
| causing overflow pretty early in fibonacci sequence. | |