A minimal PyTorch training loop that survives spot / interruptible GPU preemption. Copy train.py and swap in your model and data. 1. Keeps all recovery state (weights, optimizer, RNG, step) in one directory: $NODUSSTATEDIR or./state. 2. Writes checkpoints atomically (temp file, then os.replace). 3. Resumes from the last checkpoint on startup. 4. Saves on a timer, and again immediately when the platform sends a checkpoint request. Nodus copies the state directory off the machine before a reclaim and restores it on the replacement, so preemptions cost minutes instead of the whole run. New accounts get a $30 starter grant (valid for 30 days), so the first runs need no card. Add --dry-run to…
Independent publisher
Viswaretas kotra
Viswa-k
Reinforcement Learning, Trees/XGBoost, Deep Learning, Federated Learning.
Models in Library1
Datasets in Library0
Models on Hugging Face1
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