backend.ai

As React becomes the standard for frontends, how should Python backends keep up? Learn how to support the GraphQL and Relay specifications to collaborate with the frontend.

Jungseok Kang

Jungseok Kang is a software engineer at Rableup. He started programming with Python and has been using it with love ever since.

Key Note : AI and open source: the dynamics of innovation, capital, and contributors
Jeonkyu Shin, CEO, Lablup
https://eostday.kr

#EOST2023 #ETRI #오픈소스테크데이

This talk presents the challenges of tracking and state management of time-consuming tasks in distributed systems implemented in Python, and some abstract ideas for solving them.
There will be code examples, but please keep in mind that this is a lighthearted talk with no detailed Python code implementations, only high-level abstractions.

About Speaker
3rd year Python developer. A junior with a lot of questions about what makes good software. TMI: Likes crossfit, meat, and bread.

The most important thing in debugging is observability and reproducibility. Despite the steady improvement of the asyncio standard library, it's still a challenge to see what's going on inside a real production-level, complex asyncio application. Resource issues caused by silently swallowed cancalleation signals or arbitrarily created callbacks and coroutines inside some external code are very hard to debug, especially when you have a mix of 3rd-party libraries and frameworks running over which you have no control. Moreover, these issues tend to only occur in production environments with real workloads, not in development environments.

In this announcement, we present the aiomonitor-ng library, which is an enhancement to the previously released aiomonitor library. While the original library was based on a simple telnet server and REPL to help us see into the asyncio processes currently running, and can even help us in real production debugging, after using it for over a year, we realized what it lacked and took it upon ourselves to add a number of features, including the ability to directly create tasks and trace the cancel-terminate stack chain. I also added a terminal UI with autocomplete for ease of use.

I've been able to leverage aiomonitor and these improvements to aiomonitor-ng to discover and analyze a number of production issues in practice, and I hope you can use this experience to create more reliable asyncio applications.

About Speaker
He is currently the CTO of Lablup ("Lablup"), where he is developing Backend.AI, and has experience analyzing and implementing backend systems of various sizes. Through his open source activities, he has contributed to projects such as Textcube, iPuTTY, CPython, DPDK, pyzmq, aiodocker, and aiohttp.

This talk will cover the technical details of the Python bindings that I've spent the most time thinking about since joining Lablup, and that I'm still working on.

More specifically, the talk will focus on the technical details of exposing traces and handling exceptions, as well as the challenges of abstracting reference types to overcome the memory management differences between Rust and Python.

Due to the technical details, prior knowledge of Rust or PyO3 may be helpful to understand the presentation, but even if you don't, you can still get a general idea of what to expect.

Gyubong Lee
DevOps / Developer at Lablup. A developer who is interested in various open source activities. Currently, I am working on various issues related to distributed systems at my company. In this talk, I will cover the technical details of the Python binding, which I have spent most of my time thinking about and working on since joining Lablup.

Sokovan is a Python-based container orchestrator that addresses the challenges of running resource-intensive batch workloads in a containerized environment. It offers acceleration-aware, multi-tenant, batch-oriented job scheduling and fully integrates multiple hardware acceleration technologies into various system layers. It consists of two layers of schedulers. The cluster-level scheduler allows users to customize job placement strategies and control the density and priority of workloads. The node-level scheduler optimizes per-container performance by automatically detecting and mapping underlying hardware accelerators to individual containers, improving the performance of AI workloads compared to Slurm and other existing tools. Sokovan has been deployed on a large scale in various industries for a range of GPU workloads, including AI training and services. It helps container-based MLOps platforms unleash the potential of the latest hardware technologies.

Speakers:
Jeongkyu Shin
Joongi Kim

Creating a Serverless Jupyter Notebook App for Python Education for Kids, Jeongkyu Shin - PyCon Korea 2022