ghBack

Moved to git repository: https://github.com/denji/nginx-tuning

NGINX Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

	import tensorflow as tf

	class RBM(object):
	def __init__(self, visible_dim, hidden_dim, learning_rate, number_of_iterations):

	self._graph = tf.Graph()

	#Initialize graph
	with self._graph.as_default():

	class Kalman:
	"""
	USAGE:

	# e.g., tracking an (x,y) point over time
	k = Kalman(state_dim = 6, obs_dim = 2)

	# when you get a new observation —
	someNewPoint = np.r_[1,2]
	k.update(someNewPoint)

	-- Debian packages nginx-extras, lua-zlib required

	ngx.ctx.max_chunk_size = tonumber(ngx.var.max_chunk_size)
	ngx.ctx.max_body_size = tonumber(ngx.var.max_body_size)

	function create_error_response (code, description)
	local message = string.format('{"status":400,"statusReason":"Bad Request","code":%d,"exception":"","description":"%s","message":"HTTP 400 Bad Request"}', code, description)
	ngx.status = ngx.HTTP_BAD_REQUEST
	ngx.header.content_type = "application/json"
	ngx.say(message)

	#! /bin/bash
	# Ask user for sudo password (to be used when needed)
	read -s -p "Enter Password for sudo: " sudoPW

	# update CRAN repository below if needed
	REPO="'https://cran.rstudio.com/'"

	# Based on the instructions on Dean Attali's website:
	# https://deanattali.com/2015/05/09/setup-rstudio-shiny-server-digital-ocean/#user-libraries