We present Python-to-Kubernetes (PTK), a framework that automates the provisioning and deployment of task-based Python applications on Kubernetes. PTK introduces compact source-code annotations for tasks, resource needs, grouping, and data-size hints. From these annotations, it provisions an application-specific cluster, builds container images, generates manifests, and selects the data-transfer mechanism based on placement. A scoring-based mapping co-locates bandwidth-heavy neighbors to reduce cross-node traffic and right-sizes nodes after placement. On a six-task machine learning (ML) ResNet50 image-classification pipeline (ImageNet 5%/10% subsets), PTK achieves up to 6.43x faster runtime and 7.26x lower cost per run than the Kubernetes Default Scheduler, with higher CPU/memory utilization and fewer/smaller nodes. These results indicate that lightweight annotations plus application-aware provisioning can substantially improve price-performance for Kubernetes-based ML pipelines.

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