A Fast, Pythonic, 'Serverless' Interface for Running ML Workloads on Kubernetes
Project description
📦Kubetorch🔥
A Fast, Pythonic, "Serverless" Interface for Running ML Workloads on Kubernetes
Kubetorch lets you programmatically build, iterate, and deploy ML applications on Kubernetes at any scale - directly from Python.
It brings your cluster's compute power into your local development environment, enabling extremely fast iteration (1-2 seconds). Logs, exceptions, and hardware faults are automatically propagated back to you in real-time.
Since Kubetorch has no local runtime or code serialization, you can access large-scale cluster compute from any Python environment - your IDE, notebooks, CI pipelines, or production code - just like you would use a local process pool.
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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