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--- |
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license: apache-2.0 |
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tags: |
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- FHE |
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- concrete-ml |
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--- |
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<p align="center"> |
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<!-- product name logo --> |
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<img width=600 src="https://cdn-uploads.huggingface.co/production/uploads/6286462340423ef48fb6c45e/ElX3V79ViRx0BUcCPVJQG.png"> |
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<a href="https://github.com/zama-ai/concrete-ml"> 📁 Github</a> | <a href="https://docs.zama.ai/concrete-ml"> 📒 Documentation</a> | <a href="https://zama.ai/community"> 💛 Community support</a> | <a href="https://github.com/zama-ai/awesome-zama"> 📚 FHE resources by Zama</a> |
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</p> |
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<hr> |
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# Synthetic dataset classification with a LogisticRegression with Concrete ML |
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In this repository, we classify a synthetic dataset. Inputs are sent encrypted to the HF endpoints, and are classified (with a logistic regression) without the server seeing them in the clear, thanks to fully homomorphic encryption (FHE). This is done thanks to Zama's Concrete ML. |
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Concrete ML is Zama's open-source privacy-preserving ML package, FHE. We refer the reader to fhe.org or Zama's websites for more information on FHE. |
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## Deploying a compiled model on HF inference endpoint |
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If you would like to deploy, it is very easy. |
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- click on 'Deploy' button in HF interface |
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- chose "Inference endpoints" |
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- chose the right model repository |
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- (the rest of the options are classical to HF end points; we refer you to their documentation for more information) |
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and then click on 'Create endpoint' |
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And now, your model should be deployed, after few secunds of installation. |
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## Using HF entry points on privacy-preserving models |
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Now, this is the final step: using the entry point. You should: |
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- if your inference endpoint is private, set an environment variable HF_TOKEN with your HF token |
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- edit `play_with_endpoint.py` |
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- replace `API_URL` by your entry point URL |
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Finally, you'll be able to launch your application with `python play_with_endpoint.py`. |
