mirror of https://github.com/luxfhe/ml-sdk.git synced 2026-10-30 18:13:04 +00:00

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zeekay 531fc57003 chore(brand): purge Zama refs across docs, README, configs — phase 1

Brand-only sweep. Touches READMEs, docs, package metadata, comments,
notebook markdown, requirements.txt files; does NOT yet touch Python
`concrete.*` imports (those are module-level dependencies requiring the
phase-2 Python module port from `concrete` → `torus`).

Substitutions applied:

  Concrete ML                  → TorusML
  concrete-ml                  → torus-ml
  Concrete-ML                  → Torus-ML
  concrete_ml                  → torus_ml
  zama-ai/concrete-ml          → luxfhe/ml-sdk
  zama-ai/concrete             → luxfhe/torus
  github.com/zama-ai           → github.com/luxfhe
  docs.zama.ai/concrete-ml     → docs.luxfhe.com/ml-sdk
  docs.zama.ai/concrete        → docs.luxfhe.com/torus
  Zama Concrete ML             → Lux TorusML
  Zama Concrete                → Lux Torus
  Zama tfhe-rs / Zama TFHE-rs  → Lux-FHE
  tfhe-rs / TFHE-rs            → Lux-FHE
  Zama fhEVM / fhEVM           → TorusEVM
  Concrete framework           → Torus framework
  Concrete compiler            → Torus compiler
  Concrete by Zama             → Torus by Lux
  the Zama team                → the Lux Industries team
  Zama's                       → Lux Industries'
  (Zama)                       → (Lux)
  CoFHE / Fhenix               → (removed)

LICENSE: original Zama copyright RETAINED per BSD-3-Clause-Clear §1-§2
requirements; Lux Industries modifications copyright added. Per §3 no
name of ZAMA used to endorse the derived work — product marketed
exclusively as TorusML.

README image URLs migrated to github.com/luxfhe/ml-sdk/assets to break
the dependency on Zama's CDN.

Hit count: 350 → 295. Residual hits are Python `from concrete.fhe import`
imports referencing the upstream Zama `concrete` PyPI package; these
require module-level port (phase 2):

  Phase 2 plan:
    1. Fork zama-ai/concrete (the Python compiler) to luxfhe/torus
    2. Rename Python module `concrete` → `torus`; PyPI publish as `torus-fhe`
    3. Update ml-sdk imports from `concrete.fhe` → `torus.fhe`, etc.
    4. Wire `torus.compiler` GPU dispatch through `luxcpp/gpu` vtbl
       (lux-private/gpu-kernels for CUDA/Metal/WebGPU/FPGA targets)
    5. Port concrete-cuda + concrete-mlir kernels to
       lux-private/gpu-kernels/{cuda,metal,wgsl}/ckks/ (matrix-vector
       mul, rescale, rotate, bootstrap for TorusML inference)

  Phase 3 plan (kernels):
    - CKKS/BGV/BFV GPU kernels in lux-private/gpu-kernels/
      (currently only TFHE PBS / keyswitch / blind-rotate exist)
    - FPGA bitstreams in lux-private/fpga/ (currently scaffold only)

Phase 1 = docs/branding sweep. PR not opened; user reviews and merges.

2026-05-29 13:53:42 -07:00

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Deployment Examples

This folder contains examples of how to deploy TorusML models using Fully Homomorphic Encryption (FHE). These examples demonstrate the process of training, compiling, and deploying models for various use cases.

Overview

The deployment process generally follows these steps:

Train the model (optional, depending on the use case)
Compile the model to an FHE circuit
Deploy the model using Docker
Run inference using a client (locally or in Docker)

Available Examples

We provide three different use cases to demonstrate the deployment process:

Getting Started

Each example folder contains its own README with specific instructions. However, the general process is similar:

Train or compile the model using the provided scripts
Deploy the model using deploy_to_docker.py from the server folder
Build the client Docker image
Run the client to interact with the deployed model

For detailed instructions, please refer to the README in each example folder.

Requirements

Docker
Python 3.8 or later
TorusML library installed

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