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Snakes And Lambdas

what is lambda
why is lambda (for data science)
how is lambda (to actually use)
when is lambda (the right choice)

---~

What is lambda?

serverless
- it runs on servers, you just don't deal with that
scaleable
- it only costs when running, you just pay more as it does more
micro-service
- it only does one small thing, you just have lots of different ones

---~

What is lambda?

Start a new lambda
Set up lambda with user code - "cold start"
Accept event 1 and process
Wait
1. Accept event 2 and process - "warmed up"
2. Timeout and kill lambda

There can be multiple concurrent machines too

---~

What is lambda?

Pay only for what you use
Manage only what you have to
Deal with extra/new/bursty traffic seamlessly

---~

What is lambda?

Invocation payload (request and response)
- 6 MB (synchronous)
- 256 KB (asynchronous)
Deployment package size
- 50 MB (zipped, for direct upload)
- 250 MB (unzipped, including layers)
- 3 MB (console editor)

---~

Why is lambda?

(for data science)

data-scientists != dev-ops professionals
- but our work needs to be 'released'
all data projects != ensemble xg-boost Keras TPU shenanigans
- "No ML is easier to manage than no ML" © @julsimon
data-projects != single-goal monolithic systems
- separate concerns, code bases and complication

---~

How is lambda?

(to actually use)

write your python
lambda your python
???
profit

1. Write your python

from scipy import stats
np.random.seed(12345678)
x = np.random.random(10)
y = 1.6*x + np.random.random(10)
slope, intercept, r_value, p_value, std_err = 
    stats.linregress(x, y)

2. Lambda your python

event driven
- an event is passed to a handler function
json formatted
- events are json
- handler functions return json

---~

2. Lambda your python

import json
from scipy import stats
import numpy as np

def lambda_handler(event, context):
    np.random.seed(12345678)
    x = np.random.random(10)
    y = 1.6*x + np.random.random(10)
    slope, intercept, r_value, p_value, std_err = stats.linregress(x, y) 
    return_body = {
        "m": slope, "c": intercept,"r2": r_value ** 2, 
        "p": p_value, "se": std_err
    }
    return {"body": json.dumps(return_body)}

---~

2. Lambda your python

---~

2. Lambda your python

---~

2. Lambda your python

3. ???????

(1. Layers)

json is built in by default
- so it boto3

PROBLEM

lambda doesn't pip install ....

SOLUTION

use layers
- numpy, scipy are published by aws

---~

3.1 Layers

---~

3.1 Layers

3. ???????

(2 Custom Layers)

PROBLEM

New requirement needs pandas

SOLUTION

Create custom layer
- pre-compiled code on a specific path deployed as a .zip
- for 'any package' * using some shell and docker
  - * YMMV

---~

3.2 Custom Layers

requirements.txt

pandas==0.23.4
pytz==2018.7

get_layer_packages.sh

#!/bin/bash

export PKG_DIR="python"

rm -rf ${PKG_DIR} && mkdir -p ${PKG_DIR}

docker run --rm -v $(pwd):/foo -w /foo lambci/lambda:build-python3.6 \
    pip install -r requirements.txt --no-deps -t ${PKG_DIR}

---~

3.2 Custom Layers

execute.sh

chmod +x get_layer_packages.sh
./get_layer_packages.sh
zip -r pandas.zip . -i "python/*"

Then upload + create as layer with aws-cli or manually with console

---~

3.2 Custom Layers

'any package' *

pandas
pymysql
- lambda needs to be inside the same VPC
statsmodels

3. ???????

(3 api gateway)

PROBLEM

How does your team use your work?

SOLUTION

use api gateway
- AWS service that puts REST api in front of the lambda

---~

3.3 Api gateway

---~

3.3 Api gateway

---~

3.3 Api gateway

Get help from an adult (dev-ops professional)

---~

3.3 Api gateway

If you can't find an adult

be careful about exposing the api
- not obvious how and where it can be accessed
  - resource policies
swagger is an api templating syntax
- cloud formation
click the 'deploy api' button after every change
- use multiple stages

3. ???????

(4 local dev - cloud deploy)

PROBLEM

copy-pasta code into console is bad

SOLUTION

use AWS SAM cli
- local development + testing with docker
- 'cloudy' deployment with cloudformation cli

---~

3.4 local dev - cloud deploy

---~

3.4 local dev - cloud deploy

Usage: sam [OPTIONS] COMMAND [ARGS]...

Commands:
  local     Run your Serverless application locally 
            for quick development & testing.
  logs      Fetch logs for a function
  deploy    Deploy an AWS SAM application. This is an alias 
            for 'aws cloudformation deploy'.
  build     Build your Lambda function code
  publish   Publish a packaged AWS SAM template to the AWS 
            Serverless Application Repository.
  init      Initialize a serverless application.
  validate  Validate an AWS SAM template.
  package   Package an AWS SAM application. This is an alias 
            for 'aws cloudformation package'.

---~

3.4 local dev - cloud deploy

Workflow

sam init
sam local generate-event apigateway aws-proxy
- sam build
  - ⇵
- sam local invoke -e event.json ---~

3.4 local dev - cloud deploy

alias playitsam='sam build && sam local invoke -e event.json'
alias playitagainsam='sam build && sam local invoke -e'

---~

3.4 local dev - cloud deploy

sam validate
sam package
sam deploy

---~

3.4 local dev - cloud deploy

Transform: 'AWS::Serverless-2016-10-31'
Resources:
  RegressionFunction:
    # This resource creates a Lambda function.
    Type: 'AWS::Serverless::Function'
    Properties:
      # This function uses the python 3.7 runtime.
      Runtime: python3.7
      # This is the Lambda function's handler.
      Handler: app.lambda_handler
      # The location of the Lambda function code.
      CodeUri: ./regression
      # Event sources to attach to this function. In this case, we are attaching
      # one API Gateway endpoint to the Lambda function. The function is
      # called when a HTTP request is made to the API Gateway endpoint.
      Events:
        RegressionApi:
            # Define an API Gateway endpoint that responds to HTTP GET at /regression
            Type: Api
            Properties:
                Path: /regression
                Method: GET

This enables CI/CD, which is a Good Thing ™

---~

3.4 local dev - cloud deploy

Get help from an adult (dev-ops professional)

but if you can't, list 'em and flip 'em

aws lambda list-functions | cfn-flip

4. Profit

Surple have 3 lambda data services

---~

linear regression

'Degree Days vs Energy = Efficiency'

user triggered event
queries specific data based on user selection
user facing visualisation
vpc cold starts

---~

time-series analysis

'Smart Targets'

scheduled for all meters as ETL to DB
highlight 'out of character' energy use
user facing visualisation and notifications

---~

anomaly detection

'Smart Alarms'

scheduled for all meters as ETL to DB
highlight 'extreme' energy use
user email and notifications
this was extra fun/complicated
- Ask me how

Practical notes

tweaking cpu load has made more a difference than tweaking timeout
taking the time to set up SAM correctly has saved at least the time of browser console work alone
A Cloud Guru is built on lambda (cheaply?)
- And has some great material on it
Deployment from SageMaker is possible
CI/CD from GitLab is possible

---~

When is lambda?

(the right choice)

Good case

'traditional' models
- regression, timeseries, hopefully more...
per 'reasonable' data set
- for each
'now in a minute'
(not actually a minute, more like seconds)
'bursty'
- some, or lots of people need it then no one does

---~

When is lambda?

(the right choice)

Bad case

'fancy' models
- RAM limits, CPU limits
whole scale
- across all
immediate response
- can't afford a cold start: 'lambda your lambda'
24:7 flat load
- need 100% load 100% of the time

---~

Thank you

Slides available: https://github.com/DaveParr/snakes_and_lambdas Twitter: @DaveParr

'Smart Alarms' actually runs in

---~

Runtime Layers

PROBLEM

The thing I want to use isn't in Python
- or Go, NodeJS, C#, Java

SOLUTION

use Runtime Layers
- any language compiled into a layer
- accessed via bash or similar that processes event and passes to runtime
- bakdata/aws-lambda-r-runtime

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