This document discusses enabling infrastructure from Python code through PyIFC. PyIFC aims to empower Python programmers to develop, deploy, and operate cloud software easily without vendor lock-in. It addresses issues with current infrastructure as code approaches like low abstraction, complexity, and vendor specificity. The PyIFC solution treats cloud resources as first-class citizens in Python code through adapters. This allows optimizing deployment locations and managing resources through pure Python. The presenter argues the potential is much bigger if Python is fully adapted for cloud-native development and common standards are established.

1. Cloud
Infrastructure from
Python Code
How far could we go?
Asher Sterkin
SVP Engineering, GM

2. ● A Software Engineer since 1978
● SVP Engineering and GM @ BST LABS, BlackSwan Technologies
● Technology lead of the Cloud AI Operating System (CAIOS) project
● Contacts:
○ Linkedin: https://www.linkedin.com/in/asher-sterkin-10a1063
○ Twitter: https://www.twitter.com/asterkin
○ Email: asher.sterkin@gmail.com
○ Slideshare: https://www.slideshare.net/AsherSterkin
○ Medium: https://asher-sterkin.medium.com/
○ Mastodon: https://mastodon.social/@asterkin
○ Reddit: https://www.reddit.com/user/ashersterkin
○ Github: https://github.com/BstLabs
About Myself

3. Disclaimer
● I’m a senior manager of one of the IfC Products - CAIOS
● Best effort to avoid direct marketing against competitors
● It not an not official position of my employer BlackSwan Technologies
● Specific examples use CAIOS for illustration purposes only
● AWS icons usage is for illustration purposes only as well

4. “Infrastructure from Python Code” Mission
“Your Computer is Cloud”
Ease of use
Development
Deployment
Operation
Cost
Security
Reliability
Sustainability
PyIFC
Empowering mainstream Python programmers to develop, deploy, and operate
cost-effective, secure, reliable, and sustainable cloud software without a hassle

6. What’s the Problem?
IaC
SDK Engineering Silos
● Low level of abstraction
● Vendor-specific
● Complicated
● Error-, cost-, and security-prone
● Special technical knowledge
● Lot of coordination needed
● Far from core business

7. What About CDKs?
Don't solve the core problem anew – but rather,
wrap the same low-level abstractions in a
programming language

8. Raise the Level, Bridge the Gap
The Solution

9. The PyIfC Solution
PyIFC
Service Configuration
Logic” Template
Out Ports
Ports Binding
● Cost-effective
● Secure
● Reliable
● Sustainable
In Ports
Endpoint

10. Here, how could it look like
Warning: vendor-specific example

11. Pure Python Code

13. Build, release, run
service.py
service_config.py
module.py
build
module.py
function.py
stack.json
release <user-storage> run
<service-stack>
Based on port-to-resource binding specified in
configuration:
● Generate resource template descriptor(s)
● Incorporate resource-specific out port adaptors
to convert high-level application calls to
resource SDK calls
Warning: vendor-specific example

14. Live Demo …

15. One Inch Depth
Dive into PyIFC
Machinery

16. I
F
C
services vendors APIs location
acquire
configure
consume
operate
4 Pillars of the “Infrastructure From Code”
Service
Configuration

17. 3 most important
things in
real-estate
business

18. 1. Location
2. Location
3. Location

19. Deployment Location
Device gateway Edge location
Region
AWS Wavelength AWS Local Zone
AWS Outposts servers
Privacy
Latency
Aggregation
Flexibility
Operation cost
Sustainability!!!
PyIFC
Code

20. Computation Location
Endpoint Instance
Lambda function Application
AWS Step
Functions
workflow
Service
PyIFC
Cost
Flexibility
Available RAM
CPU/GPU power
Legacy
Sustainability !!!
Code
Batch EMR Crawler

21. Storage Location
Volume Bucket
File system Athena DB OpenSearch
Index
Redshift DB Table Aurora DB DocumentDB Neptune
QLDB
Keyspaces
MemoryDB
Timestream
ElastiCache
PyIFC
Volume
Durability
Access Time
Cost
Flexibility
Legacy
Sustainability !!! Kafka
Code

22. Messaging Channel
PyIFC
Volume
Durability
Latency
Throughput
Cost
Flexibility
Legacy
Sustainability!!!
Kafka
SNS SQS MQ SES Kinesis
Code

23. Optimizing deployment locations manually will become
increasingly challenging over time, as new technology options
emerge and add more variables to consider.

24. Where We Are Today
and Where Could We
Go?

25. IfC Technology Landscape
nitric
klotho
nitric
klotho
caios
nitric
klotho
vercel
modal
chalice
cloudflare ampt
nitric
klotho
cloudflare
cloudflare wing dark
nitric
klotho
vercel
encore
shuttle

26. frontend
core
backend
extensions
The Potential is Much Bigger

27. Application Logic
Core
Domain
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Supportive
Subdomain
Internet
Services
Cloud Services
Generic
Subdomain
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PyIFC
PyIFC
PyIFC
Where IfC Meets DDD

28. Open-end Architecture
Application Logic
Generic,
Supportive,
Core
Subdomains
Generic,
Supportive,
Core
Subdomains
Out of proc
Sidecar
In-proc
extension
Adapters

29. As far, as there is a market for this
How Far Could We Go?

30. Let Python be Python
What is Needed?

31. Protocols for PEPs and Standard Library

32. Protocols for Popular Open Source Solutions

33. Standard Schema for High-Level
Resource Configuration
What is Needed?

34. Python IfC Objective
Unlock full Python potential
as a truly cloud-native
programming language by
adapting its runtime and
compiler to work optimally
with cloud resources

35. Things to Takeaway …

36. “Infrastructure from Python Code” Mission
“Your Computer is Cloud”
Ease of use
Development
Deployment
Operation
Cost
Security
Reliability
Sustainability
PyIFC
Empowering mainstream Python programmers to develop, deploy, and operate
cost-effective, secure, reliable, and sustainable cloud software without a hussle

37. It’s not Academic

40. Questions