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AI developer in K8S: either in Jupyter notebook or LLM serving: Python Dependency is always a headache : - Prepare a set of base Images? The maintenance amounts & efforts will be a nightmare: Since (1) packages in AI world are rapidly version bumping, (2) diff llm codes require diff packages permutation/combination. - Leave users to `pip install` by themselves ? The resigned waiting blocks productivity and efficiency. You may agree if you did it. - If on a GPU Cloud, the pkg preparation time may even cost a lot: you rent a GPU but wasted in waiting pip downloading... - you may choose to D.I.Y: docker-commit your own base-images, but you have to worry about the Dockerfile, registry and additional cloud cost if you don't have local docker env.
---- So we introduce https://github.com/BaizeAI/dataset.
The solution: 1. A CRD to describe the dependency and env. 2. K8S Job to pre-load the packages. 3. PVC to store and mount 4. `conda` to switch from envs 5. share between namespaces
Cloud native developer, AI researcher, Gopher with 5 years of experience in loads of development fields across AI, data science, backend, frontend. Co-founder of https://github.com/nolebase
