Rockmate
The Rockmate framework is designed for training a PyTorch neural network within a given GPU budget
constraint using automatic re-materialization (activation checkpointing) technique.
Given a PyTorch model, a sample input, and a GPU memory budget, Rockmate builds a new
torch.nn.Module, which performs forward and backward pass keeping activations under the given
budget.
- The new model produces the same outputs and gradients as the original one.
- Model training with a budget constraint, which is lower than the one required by PyTorch Autodiff, is achieved by re-computing some of the activations instead of storing them for gradient calculation.
- Depending on the budget,
Rockmatedefines automatically which activations should be recomputed.
More information on our repository.
1 maintainer
Metadata
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Release files for rockmate 2.0.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| rockmate-2.0.0.tar.gz | 78.8 kB | Without provenance | Details for rockmate-2.0.0.tar.gz |
Release files / rockmate-2.0.0.tar.gz
| Download URL | rockmate-2.0.0.tar.gz |
|---|---|
| Size | 78.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
68c41a21858121c569ea6dbe973c0208b14579c4c5a360fdbcf00e8d7b1ffbe6
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6c8f7052feecb918599323be3fad744b3ac53acea483bb577a907ba9241a6f84
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.10.12
|
Security for rockmate 2.0.0
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