| import logging | |
| import socket | |
| log = logging.getLogger('udp_server') | |
| def udp_server(host='127.0.0.1', port=1234): | |
| s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) | |
| s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) |
rbackup is a lightweight, rsync-powered backup utility that creates time-stamped backups.
It can save backups to your local filesystem (probably an attached USB device) or to a remote host.
Backups are saved in time-stamped directories. Backups are cheap on storage, because hard-links are used for files that haven't changed between backups.
| #! /usr/bin/python | |
| # Dispatch - synchronize two folders | |
| import os | |
| import filecmp | |
| import shutil | |
| from stat import * | |
| class Dispatch: | |
| ''' This class represents a synchronization object ''' |
Just wanted a quick start guide for myself so that I wouldn't have to keep rooting through Google to remember all this stuff. Hopefully it helps other people.
If you had other ideas or suggestions please leave a comment.
The first time I bought a Pi I was enormously frustrated with myself because I didn't own all of this stuff. Kept having to order things off of Amazon and wait to get started... very irritating. This is all good stuff to have laying around anyway:
| # -*- coding: utf-8 -*- | |
| """ | |
| Compiled, mashed and generally mutilated 2014-2015 by Denis Pleic | |
| Made available under GNU GENERAL PUBLIC LICENSE | |
| # Modified Python I2C library for Raspberry Pi | |
| # as found on http://www.recantha.co.uk/blog/?p=4849 | |
| # Joined existing 'i2c_lib.py' and 'lcddriver.py' into a single library | |
| # added bits and pieces from various sources | |
| # By DenisFromHR (Denis Pleic) |
raspivid -o - -t 0 -vf -hf -fps 30 -b 6000000 | ffmpeg -re -ar 44100 -ac 2 -acodec pcm_s16le -f s16le -ac 2 -i /dev/zero -f h264 -i - -vcodec copy -acodec aac -ab 128k -g 50 -strict experimental -f flv rtmp://a.rtmp.youtube.com/live2/<SESSION>
you can tweak -b and -fps to your liking. the settings above work well for 1080p. by not specifying width or height we get the full 1920x1080 resolution from the raspi camera
#Unix Toolbox
##Unix Toolbox revision 14.4
| #!/usr/bin/python3 | |
| #required libraries | |
| import sys | |
| import ssl | |
| import paho.mqtt.client as mqtt | |
| #called while client tries to establish connection with the server | |
| def on_connect(mqttc, obj, flags, rc): | |
| if rc==0: |
| # Note – this is not a bash script (some of the steps require reboot) | |
| # I named it .sh just so Github does correct syntax highlighting. | |
| # | |
| # This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5 | |
| # | |
| # The CUDA part is mostly based on this excellent blog post: | |
| # http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/ | |
| # Install various packages | |
| sudo apt-get update |
With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.
The following section is divided in to two parts. Caffe's documentation suggest
