Asher Sterkin

Discover how Infrastructure From Code (IfC) can revolutionize Cloud DevOps automation by generating cloud deployment templates directly from Python code. Learn how this technology empowers Python developers to easily deploy and operate cost-effective, secure, reliable, and sustainable cloud software. Join us to explore the strategic potential of IfC.

## Audience The talk is a call for action towards the whole Python community to take an active part in unlocking full Python potential as a truly cloud-native programming language by adapting its runtime and compiler to work optimally with cloud resources. ## Why SDK Programming and Infrastructure as Code are not enough anymore? Developing cloud software using cloud SDK combined with deployment automation using Infrastructure as Code templates has some serious limitations. The both SDK and IaC are at realively low level, require special expertise which takes time to acquire, are disconnected from each other and too often prepared by separate enigineering teams. Applying SDK+IaC to multiple test, staging, and production environments can exacerbate complexity and size issues. As a result, there is a need for a more efficient and automated approach to cloud infrastructure management that integrates tightly with application code. ## What is Infrastructure From Code? Infrastructure from Code (IfC) is a newer and more advanced approach than IaC. It interprets mainstream programming language code and automatically generates the specifications needed to configure a cloud environment. Advanced solutions like ServerlessCloud, Ampt, and Nitric have been proposed for the TypeScript ecosystem. This talk will explore the current state of IfC for Python, its potential, and what needs to be done to make Python a truly cloud-native programming language. ## Talk Outline 1. Infrastructure from Python Code (PyIfC) Mission 2. The Challenges of SDK programming combined with Infrastructure as Code (IaC) 3. The PyIfC Approach: How It Works and Its Benefits 4. Sample Code and Demo 5. A Closer Look at PyIfC's Inner Workings 6. Overcoming Deployment Location Optimization and Sustainability Challenges 7. Overview of Existing Solutions Landscape for PyIfC 8. Unleashing the Full Potential of Python ecosystem 9. The Intersection of PyIfC and Domain-Driven Design 10. Advancing PyIfC: What Needs to Be Done 11. Key Takeaways and Next Steps 12. Q&A # Tags Cloud, Deployment, Automation, Serverless, Infrastructure as Code, IaC, Infrastructure From Code, IfC, Python

Other people: Etzik Bega

Infrastructure From Code (IfC) is a relatively new trend in Cloud DevOps automation aiming at the automatic generation of cloud deployment templates directly from the application code. There are already several solutions primarily focused on the TypeScript programming language. This talk is about what could be done for the Python ecosystem.

Infrastructure as Code - the use of repeatable scripts or SDKs to configure the cloud environment for an application - has expanded the possibilities of Serverless with Continuous Delivery. However, there are fundamental issues with IaC: it is a time-consuming and tedious process to prepare the scripts, the activity requires the involvement of DevSecOps experts, and the scripts are only loosely related to the application code. For any non-trivial application, these scripts grow very quickly in complexity and size. Applying IaC to optimize the configuration of the test, staging, and production environments only exacerbates these issues. Infrastructure from Code is a recent advancement over IaC that addresses these obstacles. IfC interprets a mainstream programming language code and automatically generates the specifications needed to configure a cloud environment. So far, the most advanced solutions, such as [ServerlessCloud](https://www.serverless.com/cloud), [Ampt](https://getampt.com/), or [Nitric](https://nitric.io/), were proposed for the TypeScript ecosystem. While, for Python, [AWS Chalice](https://aws.github.io/chalice/index) is a cloud-vendor-specific solution and offers a relatively limited feature set. In this talk, I'm going to clarify the IaC paradigm and its limitations, analyze the exact problems that IfC is supposed to solve, compare recent IfC offerings, and propose what could and, in my opinion, should be done for the Python ecosystem. I will also share my personal experience of implementing such a system, code-named [Cloud AI Operating System (CAIOS)](https://www.caios.io/home): what went well, what still needs to be resolved, and where, in my view, the Python community could contribute in making Python as the number one choice for cloud programming language.