Nick Walsh nmwalsh

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Developer Relations at Amazon Web Services (@aws)

nmwalsh / template.html

Created July 20, 2017 09:37

Facebook Instant Article Template

	<!doctype html>
	<html lang="en" prefix="op: http://media.facebook.com/op#">
	<head>
	<meta charset="utf-8">
	<!-- URL of the web version of this article -->
	<!-- TODO: Change the domain to match the domain of your website -->
	<link rel="canonical" href="http://example.com/article.html">
	<meta property="op:markup_version" content="v1.0">
	</head>
	<body>

nmwalsh / predict.py

Created December 6, 2017 23:00

Standalone predict function for a saved Python ML model pickled in `model.dat`

	# predict.py
	# Script that should consist of a single method (predict) - passing data in a presumed parsimonious syntax to your model for prediction
	#
	# In this exaple, predict would require data of the following datatype:
	# Pandas DataFrame with features
	# X_test= [[ 6.9, 3.2, 5.7, 2.3]]
	import os
	import pickle
	import pandas as pd
	import random

nmwalsh / falcon_gateway.py

Created December 7, 2017 02:23

Falcon API gateway for a simple machine learning API

	# falcon_gateway.py

	import falcon
	import json
	from data_handler import invoke_predict

	# Falcon follows the REST architectural style, meaning (among
	# other things) that you think in terms of resources and state
	# transitions, which map to HTTP verbs.

nmwalsh / data_handler.py

Created December 7, 2017 02:34

	# data_handler.py
	#
	# Argument handler that does 4 things.
	#
	# 1. Decode: deserialize raw input from API POST request received in `falcon_gateway.py`
	# 2. Preprocess: convert input data into form required for model, as specified in `predict.py`
	# 3. Postprocess: convert prediction from model (from `predict.py`) into form that can be serializable for serving API response
	# 4. Encode: serialize postprocessed data into valid JSON-esque format for API response, and pass back to `falcon_gateway.py`

	import json

nmwalsh / InfoResource

Created December 7, 2017 02:53

	class InfoResource(object):
	def on_get(self, req, resp):
	"""Handles GET requests"""
	resp.status = falcon.HTTP_200 # This is the default status
	resp.body = ('\nThis is an API for a deployed Datmo model, '
	'where it takes flower lengths as input and returns the predicted Iris species.\n'
	'To learn more about this model, send a GET request to the /predicts endpoint or visit the repository online at: \n\n'
	'https://datmo.com/nmwalsh/falcon-api-model\n\n')

nmwalsh / PredictsResource

Created December 7, 2017 02:53

	class PredictsResource(object):
	def on_get(self, req, resp):
	"""Handles GET requests"""
	resp.status = falcon.HTTP_200 # This is the default status
	resp.body = ('\nThis is the PREDICT endpoint. \n'
	'Both requests and responses are served in JSON. \n'
	'\n'
	'INPUT: Flower Lengths (in cm) \n'
	' "sepal_length":[num] \n'
	' "sepal_width": [num] \n'

nmwalsh / FalconMainCode

Last active December 7, 2017 03:00

	# falcon.API instances are callable WSGI apps. Never change this.
	app = falcon.API()

	# Resources are represented by long-lived class instances. Each Python class becomes a different "URL directory"
	info = InfoResource()
	predicts = PredictsResource()
	# things will handle all requests to the '/info' or '/predicts' URL path
	app.add_route('/info', info)
	app.add_route('/predicts', predicts)

nmwalsh / torch_wipe.sh

Created December 18, 2017 22:55

	if [[ `uname` == 'Linux' ]]; then
	echo 'Removing old Torch files from your Linux...'
	# Removing folders
	sudo rm -rf /usr/local/lib/{luarocks/,lua/,torch/,torchrocks/}
	sudo rm -rf /usr/local/share/{torch,cmake/torch/,lua}
	sudo rm -rf /usr/local/etc/{luarocks/,torchrocks/}
	sudo rm -rf /usr/local/include/{torch,TH,THC,lauxlib.h,lua.h,lua.hpp,luaT.h,luaconf.h,luajit.h,lualib.h,qtlua}
	sudo rm -rf ~/.luarocks
	sudo rm -rf ~/.cache/luarocks*
	# Removing files

nmwalsh / select system drivers

Created July 19, 2018 04:05

select system drivers

	(1) gpu
	(2) cpu
	Please select one of the above environment type (e.g. 1 or gpu):

nmwalsh / select an environment

Created July 19, 2018 04:13

select an environment

	(1) data-analytics : has libraries such as xgboost, lightgbm, sklearn etc.
	(2) mxnet : has libraries for mxnet(v1.1.0) along with sklearn, opencv etc.
	(3) caffe2 : has libraries for caffe2(v0.8.0) along with sklearn, opencv etc.
	(4) keras-tensorflow : has libraries for keras(v2.1.6) and tensorflow(v1.9.0) along with sklearn, opencv etc.
	(5) kaggle : has the environment provided by kaggle
	(6) pytorch : has libraries for pytorch(v0.4.0) along with sklearn, opencv etc.
	(7) python-base : has base python image with no libraries installed
	(8) r-base : has base R image with no libraries installed. Use this environment for rstudio workspace
	Please select one of the above environments (e.g. 1 or data-analytics):