Junjue Wang junjuew

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

$ uname -r

	#!/bin/bash -e
	# A SHORT DESCRIPTION OF YOUR SCRIPT GOES HERE
	# USAGE:
	# DESCRIPTION OF ENV VARS HERE
	###############################################################################
	set -e # exit on command errors (so you MUST handle exit codes properly!)
	set -o pipefail # capture fail exit codes in piped commands
	#set -x # execution tracing debug messages

	# Get command info

	# 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.

	# zmqimage.py -- classes to send, receive and display cv2 images via zmq
	# based on serialization in pyzmq docs and pyzmq/examples/serialization

	'''
	PURPOSE:
	These classes allow a headless (no display) computer running OpenCV code
	to display OpenCV images on another computer with a display.
	For example, a headless Raspberry Pi with no display can run OpenCV code
	and can display OpenCV images on a Mac with a display.
	USAGE:

	# Typical setup to include TensorFlow.
	import tensorflow as tf

	# Make a queue of file names including all the JPEG images files in the relative
	# image directory.
	filename_queue = tf.train.string_input_producer(
	tf.train.match_filenames_once("./images/*.jpg"))

	# Read an entire image file which is required since they're JPEGs, if the images
	# are too large they could be split in advance to smaller files or use the Fixed

	{0: 'tench, Tinca tinca',
	1: 'goldfish, Carassius auratus',
	2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
	3: 'tiger shark, Galeocerdo cuvieri',
	4: 'hammerhead, hammerhead shark',
	5: 'electric ray, crampfish, numbfish, torpedo',
	6: 'stingray',
	7: 'cock',
	8: 'hen',
	9: 'ostrich, Struthio camelus',

	# For Windows users# Note: <> denotes changes to be made

	#Create a conda environment
	conda create --name <environment-name> python=<version:2.7/3.5>

	#To create a requirements.txt file:
	conda list #Gives you list of packages used for the environment

	conda list -e > requirements.txt #Save all the info about packages to your folder

	#!/bin/bash

	# install CUDA Toolkit v8.0
	# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
	CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
	wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
	sudo dpkg -i ${CUDA_REPO_PKG}
	sudo apt-get update
	sudo apt-get -y install cuda