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I recognize most of them, they're all pretty common orchestration, distributed computation, or experiment management tools. Maybe you're just not as integrated on the operations portion of the ML space?

[I work at Protect AI] - The goal here was initially relatively common tooling around MLOps/Data Science work. All ears here if you have some ideas for other projects to explore.

The purpose of the repo seems to be to collect an archive of what real-world vulnerabilities look like, to inform service implementors and security researchers in their future work.

I suppose I’m idly curious about the answer to your question too, but paying too much attention to the specific targets feels like it’s missing the point and purpose of the collection.

h2o is definitely somewhat popular specifically for LLMs, but ray is certainly widely used for distributed training workloads