kuberay and kubetorch
About kuberay
ray-project/kuberay
A toolkit to run Ray applications on Kubernetes
For platform engineers or MLOps teams managing large-scale AI/ML workloads, KubeRay simplifies running distributed Ray applications on Kubernetes. It takes your Ray application code and desired cluster configurations, then provides automated deployment, scaling, and lifecycle management for your Ray clusters. This helps you efficiently execute tasks like large language model inference, batch processing, and model training.
About kubetorch
run-house/kubetorch
Distribute and run AI workloads on Kubernetes magically in Python, like PyTorch for ML infra.
This tool helps machine learning engineers and data scientists efficiently build and deploy AI applications on Kubernetes. You write your ML code in Python locally, and it runs remotely on your cluster, providing faster iteration and real-time feedback. It's for anyone managing ML infrastructure who wants to streamline their development workflow and reduce compute costs.
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