In this episode, guest host and AI correspondent Mofi Rahman interviews Richard Liaw and Kai-Hsun Chen from Anyscale about Ray and KubeRay. Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads, while KubeRay integrates Ray’s capabilities into Kubernetes clusters.

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News of the week

Links from the interview

Kai-Hsun Chen on LinkedIn
Richard Liaw on LinkedIn
Ray from the RISE Lab at UC Berkeley
Ray: A Distributed System for AI by Robert Nishihara and Philipp Moritz - Jan 9, 2018
KubeRay Docs
KubeRay on GitHub
PyTorch
Apache Airflow
Apache Spark
Kubeflow
Apache Submarine (retired)
Jupyter Notebooks
VS Code
Examples of schedulers for Batch/AI workloads in Kubernetes

Examples of observability tools for Batch/AI workloads in Kubernetes

Examples of loadbalancers

Nginx
Istio

Ray Data: Scalable Datasets for ML
Dask Python - Parallel Python
Ray Serve: Scalable and Programmable Serving
HPA - Horizontal Pod Autoscaling in Kubernetes
Karpenter - “Just-in-time nodes for any Kubernetes cluster”
Lazy Computation Graphs with the Ray DAG API
Types of hardware accelerators

Pandas
Numpy
KubeCon EU 2024 - Accelerators(FPGA/GPU) Chaining to Efficiently Handle Large AI/ML Workloads in K8s - Sampath Priyankara, Nippon Telegraph and Telephone Corporation & Masataka Sonoda, Fujitsu Limited
NVidia Megatron

Links from the post-interview chat

Ray & KubeRay, with Richard Liaw and Kai-Hsun Chen 54:42 Share

Kubernetes Podcast from Google

Shownotes Transcript

Ray & KubeRay, with Richard Liaw and Kai-Hsun Chen