tsai

tsai copied to clipboard

My 15 GB GPU Crashes when making predictions. Upon further reading, (link attached) I found that the function get_X_preds does some very inefficient reloading of gigabytes of data to make predictions whereas this could be done one at a time at a fraction of the memory costs. This may have already been fixed in fastai, but the fix hasn't made it to tsai. Please read the attached forum for details.

https://forums.fast.ai/t/learn-get-preds-memory-inefficiency-quick-fix/84029

Does anyone know how to get around this issue? or how I can load my model in batches perhaps?

Hello @lesego94, I have resolved another problem (#695) that could potentially be responsible for the GPU memory spike. It would be great if you could install tsai from GitHub using: pip install git+https://github.com/timeseriesAI/tsai.git

and verify if the problem still persists.

Hi Oguiza, appreciate you looking into this. It did not work. I realized I made a mistake earlier, the memory spike is occurring in my CPU ram, not GPU.

Let me give you some information about what I'm running. Im running the PatchTST model notebook 15_PatchTST_a_new_transformer_for_LTSF.ipynb using my own dataset, with 288,670 total parameters. The spike only occurs when I use:

learn = load_learner('models/patchTST.pt') scaled_preds, *_ = learn.get_X_preds(X[splits[1]])

Ok, I understand. Have you pip installed tsai from the gh repo? Here are a couple of things to test:

• Try doing just X[splits[1]] (the issue might be this is too big). If that’s the case you could create a loop to cast all predictions.
• Have you tried reducing the batch size parameter in get_X_preds? You can pass bs=1 to test it.

Thanks Oguiza, I will try your suggestions and get back to you.