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arXiv Machine Learning By Hui Sun, Anderson Uch\^oa, Wesley K. G. Assun\c{c}\~ao

From GPU to TPU with Coding Agents: What Works, What Fails, and What It Costs?

Read the original on arXiv Machine Learning →

The study investigates whether coding agents can reliably migrate PyTorch machine learning projects from GPUs to TPUs. Using Claude Code, ten projects were migrated, with eight meeting the project‑goal criterion but 76% of execution attempts failing, often due to compilation issues or silent changes in computation. The research highlights significant human effort (average 2.4 interventions) and variable costs, noting that while some TPUs can be faster and cheaper, failures and accuracy drops remain common.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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