mirror of
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zeekay
531fc57003
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.
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1.7 KiB
Deployment
In this folder we show how to deploy a TorusML model that classifies images from CIFAR-10, either through Docker or Amazon Web Services. We use the model showcased in CIFAR QAT training from scratch.
Get started
To run this example on AWS you will also need to have the AWS CLI properly setup on your system. To do so please refer to AWS documentation. One can also run this example locally using Docker, or just by running the scripts locally.
Deployment this model on your personal machine is not recommended as running a VGG in FHE is computationally intensive. It is recommended to run this on a m6i.metal instance from AWS.
- To compile your model you can use
compile.py, orcompile_with_docker.pyto use Docker. This will compile the model to an FHE circuit and serialize it. This will result in a new folder called./dev. - Once that's done you can use the script provided in TorusML in
use_case_examples/deployment/server/, usedeploy_to_docker.py.
python use_case_examples/deployment/server/deploy_to_docker.py
- Once that's done you can launch the
build_docker_client_image.pyscript to build a client Docker image. - You can then run the client by using the
client.shscript. This will run the container in interactive mode. To interact with the server you can launch theclient.pyscript usingURL="<my_url>" python client.pywhere<my_url>is the content of theurl.txtfile.
And here it is you deployed a deep-learning model and ran an inference using Fully Homormophic Encryption.