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.
2.7 KiB
Nearest neighbors
This document introduces the nearest neighbors non-parametric classification models that TorusML provides with a scikit-learn interface through the KNeighborsClassifier class.
| TorusML | scikit-learn |
|---|---|
| KNeighborsClassifier | KNeighborsClassifier |
Ciphertext format compatibility
These models only support Concrete ciphertexts. See the ciphertexts format documentation for more details.
Example
from concrete.ml.sklearn import KNeighborsClassifier
concrete_classifier = KNeighborsClassifier(n_bits=2, n_neighbors=3)
Quantization parameters
The KNeighborsClassifier class quantizes the training data-set provided to .fit using the specified number of bits (n_bits). To comply with accumulator size constraints, you must keep this value low. The model's accuracy will depend significantly on a well-chosen n_bits value and the dimensionality of the data.
The predict method of the KNeighborsClassifier performs the following steps:
- Quantize the test vectors on clear data
- Compute the top-k class indices of the closest training set vector on encrypted data
- Vote for the top-k class labels to find the class for each test vector, performed on clear data
Inference time considerations
The FHE inference latency of this model is heavily influenced by the n_bits and the dimensionality of the data. Additionally, the data-set size has a linear impact on the data complexity. The number of nearest neighbors (n_neighbors) also affects performance.
The KNN computation executes in FHE in O(Nlog^2k) steps, where N is the training data-set size and k is n_neighbors. Each step requires several PBS operations, with their runtime affected by the factors listed above. These factors determine the precision needed to represent the distances between test vectors and training data-set vectors. The PBS input precision required by the circuit is related to the precision of the distance values.