Chore/white paper experiment #683
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jfrery
requested changes
May 17, 2024
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| # Reproducing Paper **Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks** : Benchmarking Results | |||
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| This notebook replicates experiments from the paper [_Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks_](https://whitepaper.zama.ai/), published in 2021. | |||
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Worth a word about timing in the paper being obsolete I think.
| <!--pytest-codeblocks:skip--> | ||
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| ```bash | ||
| python3.8 -m venv .venv |
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Maybe just
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| python3.8 -m venv .venv | |
| python -m venv .venv |
We shouldn't assume a python version.
| <!--pytest-codeblocks:skip--> | ||
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| ```bash | ||
| pip3 install -U pip wheel setuptools --ignore-installed |
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Same here. I think pip is default now.
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| print("starting") | ||
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| DEVICE = "cpu" # torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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I don't understand the comment here.
| # Seed to ensure reproducibility | ||
| SEED = 42 | ||
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| # Wether the experiments are run on PC or other machines, like HP7C on AWS |
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| # Wether the experiments are run on PC or other machines, like HP7C on AWS | |
| # Whether the experiments are run on PC or other machines, like HP7C on AWS |
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| # The article presents 3 neural network depths. In this notebook, we focus NN-20 and NN-50 |
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We should mention why we did not do the NN-100
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| # Make a grid from batch and rotate to get a valid shape for imshow | ||
| images = make_grid(images[:n], nrow=n).permute((1, 2, 0)) | ||
| # Remove the previous normalization |
jfrery
reviewed
May 17, 2024
| ```bash | ||
| pip3 install -U pip wheel setuptools --ignore-installed | ||
| pip3 install -r requirements.txt --ignore-installed | ||
| ``` |
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I think we can have a nice table display old and current results.
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@andrei-stoian-zama, as discussed yesterday, I removed the useless files.
Could you please take over this PR