We just concluded the inaugural ArgoCon! We'd like to share some impressive numbers from this conference and recap on the four sessions from the Akuity team.
First, let's look at some numbers that came out from this conference.
These are really impressive numbers and we just got started for this first-ever Argo conference!
We are proud to be one of the diamond sponsors of ArgoCon and 4 of the talks were given by our team members at Akuity! s Our team consists of the founding members and core maintainers of Argo. We've been involved in every perspective of the Argo projects and we will invest in what’s needed to foster the project’s growth. This includes continuing our contributions to the project, supporting users with their issues, facilitating discussions and meetings, and promoting Argo every chance we get. If this interests you, please reach out and we are actively hiring!
The Argo community is growing fast and has been leading the chart with a large number of open source contributors and development activities. Argo is ranked as one of the top projects among all the CNCF projects in terms of development velocity, based on activities on GitHub commits/PRs/issues (see the screenshot below).
Hong and Kelsey takes a deep-dive into the Argo project and explores how we got here, and where we’re going.
In this talk, Argo Rollouts maintainers Jesse Suen and Hui Kang speak about the performance and scalability testing they conducted on Argo Rollouts when compared against the native Kubernetes Deployment kind. The talk begins with some background on Argo Rollouts architecture. It discusses the methodology of the experiments, and the final results. The results uncovered some bottlenecks which were subsequenltly fixed bringing the performance back on par with Deployments.
Speaker: Yuan Tang - Founding Engineer at Akuity
In this talk, Yuan provides an overview of the Python scientific system, machine learning frameworks, and workflow orchestration tools. He also presents various best practices and challenges on building large, efficient, scalable, and reliable distributed machine learning pipelines using cloud-native technologies such as Argo Workflows and Kubeflow as well as how they fit into Python ecosystem with cutting-edge distributed machine learning frameworks such as TensorFlow and PyTorch.
A lot has happened with the Argo project in 2021 and there are some impressive stats to back that up:
But of course the most important statistic of all, is that 500 Argo plushies were delivered at KubeCon. To see the recap, as well as what's in store for 2022, watch the recordings of the maintainers updates:
In addition, there's also a curated list of projects and resources related to Argo if you'd like to learn more about the Argo core projects and ecosystem projects.
Join our growing Argo community by finding us at regular community meetings, conferences, and Slack!
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