Kubetorch
Build scalable and reproducible ML workflows in Python, with fast iteration and debuggability.
What is Kubetorch?
Kubetorch is for infra people who like Kubernetes and ML people who don’t. It is to Kubeflow as PyTorch was to Tensorflow — a Pythonic, debuggable, scalable ML execution engine for Kubernetes, shared from research through production.
It is comprised of a Python library and a Kubernetes operator, which work together to provide a simple, powerful, and flexible way to build, deploy, and manage AI/ML applications. You can use Kubetorch incrementally within an existing ML stack and codebase, or as a complete replacement for training, batch processing, inference, HPO, and pipelining tools in systems like Kubeflow, SageMaker, Vertex, etc.
Why Kubetorch?
Kubetorch lets ML teams go from research to production 10x faster, with less infrastructure pain and lower costs.
- Fast iteration loops: 1-2 seconds vs. 30-60 minute waits on legacy infrastructure
- Lower compute costs: save 50% compared to rigid hosted tools like SageMaker
- Higher reliability: reduce production pipeline failure rates by 95%
- Developer focus: spend time on research, not YAML, infrastructure, or glue code
Who This is For
Kubetorch is built for ML Engineers, Research & Data Scientists, and Platform Teams who are tired of:
- Waiting 30+ minutes for every code update to hit production
- Writing endless YAML and Dockerfiles just to test a model
- Debugging training failures across black-box infrastructure
- Rebuilding the same pipeline glue for every project
Key Features
- Python APIs: abstract away Kubernetes & infrastructure complexity
- Run anywhere Python runs: local notebooks, CI, apps, etc
- Push-button deployment: scale and deploy easily
- No “translation to production” step: the same code runs in dev and prod
- Built-in robustness: fault-tolerance, reliability, and observability