garnaat

import boto.vpc
import time

REGION_NAME = 'us-west-2'
AMI_ID = 'ami-8e27adbe'  # Amazon Linux AMI

conn = boto.vpc.connect_to_region(REGION_NAME)

# Create a VPC
vpc = conn.create_vpc('10.0.0.0/16')

mlconnor

Session Management in an Autoscaling Environment

Problem Statement

User sessions in J2EE and LAMP stacks have traditionally been handled in memory by the application server handling the user request. Because of that, load balancers have been configured to use sticky sessions. By sticky sessions we mean that once the user has visited the site, they will be assigned an app server and will return to that server for subsequent requests. The load balancers typically handle that by referencing the users session cookie.

Elastic cloud environments differ from traditional server configurations in that they have a variable number of servers based on traffic loads whereas traditional configurations had a fixed number of servers. When traffic volumes decline it is necessary to vaporize servers. In doing so, we would lose user sessions (essentially forcing a logout) unless we come up with a new strategy for session management.

A new approach

After much research, it is clear that the best

shawnsi

#!/usr/bin/env python

from __future__ import print_function
import requests

def merge_prefixes(payload):
    """
    Converts the AWS services JSON payload into a list of tuples.

    Arguments:

mpneuried

# import config.
# You can change the default config with `make cnf="config_special.env" build`
cnf ?= config.env
include $(cnf)
export $(shell sed 's/=.*//' $(cnf))

# import deploy config
# You can change the default deploy config with `make cnf="deploy_special.env" release`
dpl ?= deploy.env
include $(dpl)

stevenringo

Sessions

Code	Title	Duration	Link
Keynote	Andy Jassy Keynote Announcement Recap	0:01	https://www.youtube.com/watch?v=TZCxKAM2GtQ
Keynote	AWS re:Invent 2016 Keynote: Andy Jassy	2:22	https://www.youtube.com/watch?v=8RrbUyw9uSg
Keynote	AWS re:Invent 2016 Keynote: Werner Vogels	2:16	https://www.youtube.com/watch?v=ZDScBNahsL4
Keynote	[Tuesday Night Live with Jame

pahud

specify the region

export AWS_DEFAULT_REGION=ap-northeast-1
aws logs describe-log-groups --query 'logGroups[*].logGroupName' --output table | \
awk '{print $2}' | grep -v ^$ | while read x; do  echo "deleting $x" ; aws logs delete-log-group --log-group-name $x; done

only delete loggroup name starting with /aws/lambda

jgautheron

      Tags:
      - Key: Name
        Value:
          !Join ['_', [ ec2, "Fn::ImportValue" : {"Fn::Sub":'${BaseStackName}-Environment'}, test2]]
          #This will create a name tag with value: ec2_<yourValueFromOtherStack>_test2

ericmjl

UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli

How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!