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zeekay
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ML examples: - credit_scoring: Credit risk prediction with HMEQ data - disease_prediction: Healthcare ML on encrypted records - cifar: Encrypted image classification - federated_learning: Distributed FHE training - hybrid_model: Mixed FHE/plaintext inference Deployment: - breast_cancer: Cancer detection API - sentiment_analysis: Encrypted NLP - server: FHE inference server Blockchain: - hardhat-template: Solidity + FHE - react-template: Full dApp with FHE
92 lines
2.5 KiB
Python
92 lines
2.5 KiB
Python
"""Deployment server.
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Routes:
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- Get client.zip
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- Add a key
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- Compute
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"""
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import io
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import os
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import uuid
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from pathlib import Path
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from typing import Dict
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import uvicorn
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from fastapi import FastAPI, Form, HTTPException, UploadFile
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from fastapi.responses import FileResponse, StreamingResponse
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# No relative import here because when not used in the package itself
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from concrete.ml.deployment import FHEModelServer
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if __name__ == "__main__":
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app = FastAPI(debug=False)
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FILE_FOLDER = Path(__file__).parent
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KEY_PATH = Path(os.environ.get("KEY_PATH", FILE_FOLDER / Path("server_keys")))
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CLIENT_SERVER_PATH = Path(os.environ.get("PATH_TO_MODEL", FILE_FOLDER / Path("dev")))
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PORT = os.environ.get("PORT", "5000")
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fhe = FHEModelServer(str(CLIENT_SERVER_PATH.resolve()))
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KEYS: Dict[str, bytes] = {}
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PATH_TO_CLIENT = (CLIENT_SERVER_PATH / "client.zip").resolve()
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PATH_TO_SERVER = (CLIENT_SERVER_PATH / "server.zip").resolve()
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assert PATH_TO_CLIENT.exists()
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assert PATH_TO_SERVER.exists()
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@app.get("/get_client")
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def get_client():
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"""Get client.
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Returns:
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FileResponse: client.zip
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Raises:
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HTTPException: if the file can't be find locally
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"""
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path_to_client = (CLIENT_SERVER_PATH / "client.zip").resolve()
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if not path_to_client.exists():
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raise HTTPException(status_code=500, detail="Could not find client.")
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return FileResponse(path_to_client, media_type="application/zip")
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@app.post("/add_key")
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async def add_key(key: UploadFile):
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"""Add public key.
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Arguments:
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key (UploadFile): public key
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Returns:
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Dict[str, str]
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- uid: uid a personal uid
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"""
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uid = str(uuid.uuid4())
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KEYS[uid] = await key.read()
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return {"uid": uid}
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@app.post("/compute")
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async def compute(model_input: UploadFile, uid: str = Form()): # noqa: B008
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"""Compute the circuit over encrypted input.
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Arguments:
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model_input (UploadFile): input of the circuit
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uid (str): uid of the public key to use
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Returns:
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StreamingResponse: the result of the circuit
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"""
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key = KEYS[uid]
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encrypted_results = fhe.run(
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serialized_encrypted_quantized_data=await model_input.read(),
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serialized_evaluation_keys=key,
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)
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return StreamingResponse(
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io.BytesIO(encrypted_results),
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)
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uvicorn.run(app, host="0.0.0.0", port=int(PORT))
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