OpenVector
  • OpenVector
  • Introduction
    • Our Thesis
    • Our Solution
    • Other Approaches
    • Benchmarks
  • Network Architecture
    • Overview
    • Networking
    • Deploy Compute Node
    • Deploy CoFHE Node
    • Deploy Client Node
  • Quick start
    • Overview
    • Setting up Client Node
    • Encrypting Data
    • Tensor Multiplication
    • Decrypting the Ouput
    • Verifying the Output
    • Running the Program
  • Tutorials
    • Building MLP Block
  • Use Cases
    • Confidential LLM Inference
    • Training on Encrypted Data
    • Vector Search on Encrypted Data
    • Encrypted Data Access Control
  • API references
    • CryptoSystem Interface
    • ClientNode Class
    • ComputeRequest Class
    • ComputeResponse Class
  • PYTHON API REFERENCES
    • Overview
    • Tensor
    • nn.Module
    • nn.Functional
  • Contribution
    • Call for Research
    • CoFHE Library
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  1. PYTHON API REFERENCES

Overview

CoFHE offers a seamless integration with PyTorch-like APIs to provide encrypted computation capabilities directly within your Python code. The following are the currently supported API features available in the Python bindings for CoFHE. This API closely mirrors the PyTorch functionality, ensuring an easy transition for PyTorch users to CoFHE for privacy-preserving machine learning.

We are actively working on expanding this documentation, and will provide a more detailed guide once the Python bindings for CoFHE are publicly released. This section covers some of the already supported API.

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Last updated 6 months ago