A Python interface for running ML workloads on Kubernetes
Project description
📦Kubetorch🔥
A Python interface for running ML workloads on Kubernetes
Kubetorch enables you to run any Python code on Kubernetes at any scale by specifying required resources, distribution, and scaling directly in code. It provides caching and hot redeployment for 1-2 second iteration cycles, handles hardware faults and preemptions programmatically, and orchestrates complex, heterogeneous workloads with built-in observability and fault tolerance.
What Kubetorch Enables
- 100x faster iteration from 10+ minutes to 1-3 seconds for complex ML applications like RL and distributed training
- 50%+ compute cost savings through intelligent resource allocation, bin-packing, and dynamic scaling
- 95% fewer production faults with built-in fault handling with programmatic error recovery and resource adjustment
🏃♀️ Built by Runhouse 🏠
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