January 29, 2026
AFK? The bot’s got your GPUs
Show HN: Autonomous recovery for distributed training jobs
Robots babysit your AI runs while you sleep — trust or terror
TLDR: TensorPool Agent promises to auto-recover long, multi-computer AI training jobs when they fail mid-run, saving costly GPU hours. Commenters love the convenience but worry about “silent” stalls and how much control to grant an agent, demanding smarter progress checks so it doesn’t quietly nurse a zombie job.
On Show HN, the TensorPool Agent showed up like a night shift babysitter for marathon AI training runs, promising to auto-recover mid-run failures and save precious GPU hours. The crowd loved the idea of fewer 3 a.m. restarts, but the mood quickly split: how much power should a bot have over your job? Fans pointed to its whitelist-only actions and root-cause notes as “adult supervision,” while skeptics cracked jokes about Skynet rage-deleting checkpoints. The spiciest thread hit a nerve: so-called “silent” failures, where nothing crashes but the run stops improving. One engineer admitted they still struggle to “detect ‘silent’ failures where the job doesn’t crash but stops making progress,” calling out hangs in NCCL (Nvidia’s team-communication library) and loss meltdowns that don’t trigger OOM (out-of-memory) errors. War stories flew: network hiccups, storage timeouts, and the infamous “job is technically running, progress spiritually deceased.” Devs asked for failure horror stories and got them, with demands for smarter watchdogs that monitor loss curves and heartbeat signals, not just error logs. Bottom line: this tool promises hands-off recovery from deep-in-training disasters, but the hive wants proof it won’t just babysit a zombie run forever.
Key Points
- •TensorPool Agent autonomously monitors and recovers long-running distributed training jobs on Kubernetes, Slurm, or TensorPool Jobs.
- •It focuses on runtime failures that occur after the first checkpoint and can attempt recovery from the latest checkpoint using user-whitelisted actions.
- •Supported failure modes include GPU hardware faults (e.g., NVIDIA Xid errors), NCCL communication errors, infrastructure failures, storage I/O issues and S3 timeouts, network problems, and GPU memory issues.
- •The workflow includes registration (credentials and permissions), continuous monitoring, recovery attempts with log analysis, and resolution with alerts and recovery status.
- •Job states include enabled, credential_error, recovering, and completed, with text/email notifications when entering recovering.