| var request = require('request'); | |
| var unzip = require('unzip'); | |
| var csv2 = require('csv2'); | |
| request.get('http://s3.amazonaws.com/alexa-static/top-1m.csv.zip') | |
| .pipe(unzip.Parse()) | |
| .on('entry', function (entry) { | |
| entry.pipe(csv2()).on('data', console.log); | |
| }) | |
| ; |
| #!/usr/bin/python | |
| # Modified by Travis Lee | |
| # Last Updated: 4/21/14 | |
| # Version 1.16 | |
| # | |
| # -changed output to display text only instead of hexdump and made it easier to read | |
| # -added option to specify number of times to connect to server (to get more data) | |
| # -added option to send STARTTLS command for use with SMTP/POP/IMAP/FTP/etc... | |
| # -added option to specify an input file of multiple hosts, line delimited, with or without a port specified (host:port) |
| error_page 500 /500.html; | |
| location /500.html{ | |
| return 500 '{"error": {"status_code": 500,"status": "Internal Server Error"}}'; | |
| } | |
| error_page 502 /502.html; | |
| location /502.html{ | |
| return 502 '{"error": {"status_code": 502,"status": "Bad Gateway"}}'; | |
| } |
#Container Resource Allocation Options in docker-run
now see: https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources
You have various options for controlling resources (cpu, memory, disk) in docker. These are principally via the docker-run command options.
##Dynamic CPU Allocation
-c, --cpu-shares=0
CPU shares (relative weight, specify some numeric value which is used to allocate relative cpu share)
| // make a sphere | |
| ofSpherePrimitive sph; | |
| sph.set(100, 10); | |
| ofMesh s = sph.getMesh(); | |
| // grab the current camera | |
| // this is to figure out where a camera "should" be, ie, for this overall picture where is the camera. | |
| ofCamera mainCam; | |
| mainCam.setupPerspective(); | |
| """ | |
| ash_ffffind.py | |
| v1.1 (September 14, 2015) | |
| by me@aaronhildebrandt.com | |
| Automatically downloads all images from ffffound saved by a specific user. | |
| Will first try to download the image from the original source (to get the highest quality possible). | |
| If that fails, it'll download the cached version from ffffound. | |
| # Hello, and welcome to makefile basics. | |
| # | |
| # You will learn why `make` is so great, and why, despite its "weird" syntax, | |
| # it is actually a highly expressive, efficient, and powerful way to build | |
| # programs. | |
| # | |
| # Once you're done here, go to | |
| # http://www.gnu.org/software/make/manual/make.html | |
| # to learn SOOOO much more. |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| <?xml version='1.0' encoding='utf-8'?> | |
| <?grc format='1' created='3.7.8'?> | |
| <flow_graph> | |
| <timestamp>Tue Aug 25 19:04:21 2015</timestamp> | |
| <block> | |
| <key>options</key> | |
| <param> | |
| <key>author</key> | |
| <value></value> | |
| </param> |
##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