alexcasalboni

#!/bin/bash

BUCKET="YOUR_BUCKET_NAME" # bucket name
FILENAME="deployment-package.zip" # upload key
TMP_FOLDER="/tmp/lambda-env-tmp/" # will be cleaned
OUTPUT_FOLDER="/tmp/lambda-env/" # will be cleaned


HERE=${BASH_SOURCE%/*} # relative path to this file's folder
LAMBDA_FOLDER="$HERE/lambda/" # relative path

alexcasalboni

import boto3

iam = boto3.resource('iam')

def get_policy_body(arn, version_id=None):
    """ Return IAM Policy JSON body """
    if version_id:
        version = iam.PolicyVersion(arn, version_id)
    else:
        policy = iam.Policy(arn)

alexcasalboni

Bridge Function between Kinesis Streams and Step Functions

For each record read from the Kinesis Stream, a StepFunction state machine will be executed asynchronously.

Required Environment Variables

• region: the AWS region where your StepFunction state machine is defined.
• stateMachineArn: the ARN of the StepFunction state machine you want to execute.

Notes

alexcasalboni

Amazon Rekognition - Python Code Samples

1. Labels Detection
2. Faces Detection
3. Faces Comparison
4. Faces Indexing
5. Faces Search

alexcasalboni

AWS Lambda@Edge Experiment

Requirements

• AWS Lambda@Edge (enabled Preview)
• One Amazon CloudFront Distribution (origin doesn't matter)
• IAM role (basic execution is enough)
• npm to install Node.js dependencies

alexcasalboni

[
  "https://twitter.com//goserverless/status/879729046568882177",
  "https://twitter.com//zdne/status/879726263417663490",
  "https://twitter.com//jeffconf/status/879722357899894785",
  "https://twitter.com//AzureFunctions/status/879458907453767680",
  "https://twitter.com//adampash/status/877549817471279105",
  "https://twitter.com//MongoDB/status/877186132404031488",
  "https://twitter.com//alex_casalboni/status/875866754177748993",
  "https://twitter.com//gmolto/status/872691887936811008",
  "https://twitter.com//jeffconf/status/872451904642703360",

alexcasalboni

import boto3
from boto3.session import Session

session = Session(aws_access_key_id='AWS_ACCESS_KEY_ID', aws_secret_access_key='AWS_SECRET_ACCESS_KEY')
machinelearning = session.client('machinelearning', region_name='us-east-1')
labels = {'1': 'walking', '2': 'walking upstairs', '3': 'walking downstairs', '4': 'sitting', '5': 'standing', '6': 'laying'}

# fill your model ID and Endpoint here!
model_id = 'MODEL_ID'
prediction_endpoint = 'MODEL_ENDPOINT'

alexcasalboni

Resources:
  WorkshopKinesisStream:
    Type: "AWS::Kinesis::Stream"
    Properties: 
      ShardCount: 1

  IdentityPool:
    Type: "AWS::Cognito::IdentityPool"
    Properties:
      IdentityPoolName: WorkshopIdentityPool

alexcasalboni

How to use Python3 Type Hints in AWS Lambda

TL;DR

Static Type Checkers help you find simple (but subtle) bugs in your Python code. Check out lambda_types.py and incrementally improve your code base and development/debugging experience with type hints.

Your Lambda Function code will go from this:

alexcasalboni

How to Build and Deploy Serverless Apps [AWS Summit]

This demo was presented at the AWS Summit @ Cape Town on Jul 12th.

You can find the slides here.

What's included in this Gist?

• index.js: The node.js code used for AWS Lambda
• sam_template.yaml: The AWS SAM template in YAML format (i.e. CloudFormation)