Her name is Nania.

A month ago, Ilya Sutskever, the ex co-founder and chief scientist at OpenAI, gave a talk at the NeurIPS 2024 and announced that: PreTraining is Over. He reveals the fact that the available data of internet for training large language models is exhausted, which somehow challenges the scaling law (for short, the performance and accuracy of AI model improves as a function of increasing the scale in model size, dataset size and compute power).

Days ago, somebody asked me why I want to contribute to open source, what do I expect from the involvements, I told that it’s a nature motivation as an engineer, it’s true, but I want to elaborate more here and write down my understandings.

On January 1st, 2024, I outlined three goals for the coming year, and I would like to response to this first:

Panel: Fragmentation of the Scheduling in Kubernetes and Challenges for AI/ML workloads

[Slides]

Scheduler is one of the most frequently customized components in Kubernetes, owing to its expandability. However, too many schedulers lead to decision paralysis among users, which has been discussed extensively in the past KubeCons. To help mitigate the confusion of users, four maintainers from various community (Godel-Scheduler, Koordinator, Kubernetes SIG-Scheduling and Volcano) are invited to profile the background and usecases behind these projects.

He’s a handsome boy now.

San Siro.

Hiking to Harder Kulm, the top of Interlaken.

Session: SIG-Scheduling Intro & Deep Dive

[Slides]

Kube-scheduler is a critical component to Kubernetes, responsible for placing the pod to the most suitable node. But how it works, can we customize it for advanced usage, what’s the best practice in large clusters. To answer these progressive questions, we’ll divide this session into two parts. If you’re a newbie to kube-scheduler, you may interest with the Intro part, if you’re a senior one, you can join our Deep Dive.