|
--- |
|
license: apache-2.0 |
|
--- |
|
<p align="center"> |
|
<!-- product name logo --> |
|
<img width=600 src="https://cdn-uploads.huggingface.co/production/uploads/6286462340423ef48fb6c45e/ElX3V79ViRx0BUcCPVJQG.png"> |
|
<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> |
|
</p> |
|
<hr> |
|
|
|
# Synthetic dataset classification with a LogisticRegression with Concrete ML |
|
|
|
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. |
|
|
|
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. |
|
|
|
## Deploying a compiled model on HF inference endpoint |
|
|
|
If you would like to deploy, it is very easy. |
|
- click on 'Deploy' button in HF interface |
|
- chose "Inference endpoints" |
|
- chose the right model repository |
|
- (the rest of the options are classical to HF end points; we refer you to their documentation for more information) |
|
and then click on 'Create endpoint' |
|
|
|
And now, your model should be deployed, after few secunds of installation. |
|
|
|
## Using HF entry points on privacy-preserving models |
|
|
|
Now, this is the final step: using the entry point. You should: |
|
- if your inference endpoint is private, set an environment variable HF_TOKEN with your HF token |
|
- edit `play_with_endpoint.py` |
|
- replace `API_URL` by your entry point URL |
|
|
|
Finally, you'll be able to launch your application with `python play_with_endpoint.py`. |
|
|
|
|
