AI-Hypercomputer / AI-Hypercomputer/ray-tpu

Maybe fork a separate process to monitor execution

Aperta
#13 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub
Lingua principale
Python
Stelle
15
Fork
8
Metriche di merge delle PR
Nessuna PR unita negli ultimi 30g

Descrizione

A main use case for Ray-on-TPU is for orchestrating JAX training jobs. XLA likes to abort the process when things go wrong, whichh ends the process outside of Python control flow and means that Ray can't tell the difference between the Ray process crashing due to some internal Ray error or XLA killing it. To help with retry logic, we have found it useful to fork a process to run the main payload function.

We're happy to keep this in our library if you don't think it's necessary. Just raising it for now.

Our implementation and justification: https://github.com/stanford-crfm/levanter/blob/94afdc17f6091249e70e90cb27b4378d0553ff56/src/levanter/infra/ray_tpu.py#L459

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