Kyle Kosic kykosic

Setting up a Python Development Environment

This guide explains how to setup a computer for effective Python development, while maintaining organized environments supporting multiple Python versions (if necessary). There are several Python environment managers out there; for this guide we will use Conda. Conda is a general-purpose environment manager that can be used for more than just Python, is cross-platform compatible, and integrates nicely with common terminals.

Install the Conda environment manager

The installation we will use is called "miniconda". It just comes with bare-bones Python without a bunch of unnecessary fluff (as opposed to full Anaconda). For all installs, I will assume you have a 64-bit system since it's not 2001.

Windows

On Windows, you have the option to install natively, which will give you the Anaconda Prompt command line to access python. If you're already familiar with Command Prompt syntax, this will be fine. **However, I highly suggest installing the Ubuntu subsystem

Setup Tensorflow with evcxr

MacOS

Get the `libtensorflow` libraries in the LD_LIBRARY_PATH

Clone the tensorflow/rust repository
Build

cargo build --release

	set term=xterm-256color
	syntax enable
	set number
	set ts=4
	set softtabstop=4
	set expandtab
	let python_highlight_all = 1
	set noundofile
	set autoindent
	set colorcolumn=80

	# Git branch labeling
	find_git_branch() {
	local branch
	if branch=$(git rev-parse --abbrev-ref HEAD 2> /dev/null); then
	if [[ "$branch" == "HEAD" ]]; then
	branch='detached*'
	fi
	git_branch="($branch)"
	else
	git_branch=""

	CONFIG - MAC
	{
	"auto_complete": false,
	"color_scheme": "Packages/Color Scheme - Default/Monokai.sublime-color-scheme",
	"font_size": 12,
	"ignored_packages":
	[
	"Vintage"
	],
	"rulers":

	#!/usr/bin/env python
	"""
	Scrape artifact card prices
	"""
	from __future__ import division
	import os
	import time
	import logging
	from io import StringIO
	from datetime import datetime

	"""
	Keras Callback object for creating model run hardware profile timelines.

	This callback is very slow, and should only be used for debugging.
	"""
	import os
	import json
	import logging
	import tensorflow as tf
	from tensorflow.python.client.timeline import Timeline

	SETTINGS
	{
	"files.trimTrailingWhitespace": true,
	"workbench.tree.indent": 20,
	"window.nativeTabs": true,
	"window.restoreFullscreen": true,
	"window.openFoldersInNewWindow": "on",
	"editor.rulers": [
	100
	],

	sysctl -w vm.drop_caches=3
	date