PyTorch Checkpoint Lost After a Colab Disconnect: Can It Be Recovered?
TL;DR:
A PyTorch checkpoint lost during a Google Colab disconnect cannot be recovered by local desktop tools if it was never saved outside the temporary virtual machine runtime.
● Saving an .ipynb notebook to Google Drive preserves only the code file, meaning training progress must be explicitly written to a mounted Drive or cloud bucket using torch.save() for the model state_dict, optimizer state_dict, epoch, and loss.
● Look for existing .pt or .pth files in your explicitly defined persistent storage paths before restarting the training process from the last available checkpoint.
● Recoverit File Recovery is only applicable if the checkpoint was previously downloaded to local Windows or Mac storage and subsequently deleted, functioning as a best-effort scan since .pt, .pth, and .ckpt are not explicitly guaranteed formats.
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In this Colab training-loss case, the user saved the notebook and dataset to Google Drive but lost model weights, optimizer state, and training progress whenever the Colab session disconnected. If those states existed only in the Colab runtime and were never written to persistent storage, a desktop recovery tool cannot retrieve them from your computer.
Why Saving the Notebook Did Not Save the Training State
Google's Colab FAQ explains that code runs in a private virtual machine and that those VMs are deleted after being idle for a while and also have a maximum lifetime. Saving an .ipynb notebook to Drive preserves the notebook file, not automatically every file or in-memory state created inside the temporary runtime.
Key Point:
If no checkpoint file was ever saved outside the expired Colab VM, there is no local deleted file for Recoverit to scan. Resume from the last persistent checkpoint or retrain the missing portion.
Check Persistent Storage Before Starting Over
Look in the mounted Google Drive folder, cloud bucket, network volume, or any location your training code explicitly used as the checkpoint path. If a checkpoint exists there, load that copy rather than trying to recover runtime storage.
PyTorch's official saving and loading guide recommends saving a resumable checkpoint with the model state_dict, optimizer state_dict, epoch, loss, and other needed state using torch.save(). PyTorch also notes that .pt and .pth are common model-file extensions.
When Recoverit Can Apply
If you had already downloaded or copied a checkpoint to a Windows, Mac, external drive, or other readable local storage and that local file was later deleted, Recoverit File Recovery can scan the local device for deleted data. Current public Recoverit format documentation does not explicitly list PyTorch .pt, .pth, or .ckpt as recognized signatures, so treat this as a best-effort scan rather than guaranteed format recovery. Save any recovered candidate to a different drive and test it with the original training code before relying on it.