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license: apache-2.0 |
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# Template for Concrete ML |
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Concrete ML is Zama's open-source privacy-preserving ML package, based on fully homomorphic encryption (FHE). We refer the reader to fhe.org or Zama's websites for more information on FHE. |
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This directory is used: |
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- by ML practicioners, to create Concrete ML FHE-friendly models, and make them available to HF users |
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- by companies, institutions or people to deploy those models over HF inference endpoints |
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- by developers, to use these entry points to make applications on privacy-preserving ML |
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## Creating models and making them available on HF |
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This is quite easy. Fork this template (maybe use this experimental tool https://huggingface.co/spaces/huggingface-projects/repo_duplicator for that), and then: |
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- install everything with: `pip install -r requirements.txt` |
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- edit `creating_models.py`, and fill the part between "# BEGIN: insert your ML task here" and |
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"# END: insert your ML task here" |
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- run the python file: `python creating_models.py` |
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At the end, if the script is successful, you'll have your compiled model ready in `compiled_model`. Now you can commit and push your repository (with in particular `compiled_model`, `handler.py`, `play_with_endpoint.py` and `requirements.txt`, but you can include the other files as well). |
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We recommend you to tag your Concrete ML compiled repository with `Concrete ML FHE friendly` tag, such that people can find them easily. |
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## Deploying a compiled model on HF inference endpoint |
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If you find an `Concrete ML FHE friendly` repository that 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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- replace the part between "# BEGIN: replace this part with your privacy-preserving application" and |
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"# END: replace this part with your privacy-preserving application" with your application |
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Finally, you'll be able to launch your application with `python play_with_endpoint.py`. |
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