mirror of
https://github.com/luxfhe/ml-sdk.git
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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.
2.7 MiB
2.7 MiB
In [1]:
from functools import partial
# The simulation mode allows to measure the impact of FHE execution on accuracy
# without paying the cost of FHE computations.
# However, data is not encrypted when using the simulation: the model performs inference
# on clear data.
def make_classifier_comparison_from_sklearn(*args, **kwargs):
return args, kwargs
%run utils/classifier_comparison_utils.pyIn [2]:
from concrete.ml.sklearn import (
DecisionTreeClassifier,
LinearSVC,
LogisticRegression,
RandomForestClassifier,
XGBClassifier,
)In [3]:
%%time
linear_classifiers = [
(partial(LinearSVC, C=0.025), "Linear SVC"),
(LogisticRegression, "Logistic Regression"),
]
# pylint: disable-next=undefined-variable
make_classifier_comparison_from_sklearn(
"Linear Classifiers", linear_classifiers, 0, simulate=True, h=1
) # noqa
CPU times: user 27 s, sys: 5.96 s, total: 32.9 s Wall time: 6.2 s
In [4]:
%%time
tree_classifiers = [
(partial(DecisionTreeClassifier, max_depth=5), "Decision Tree"),
(partial(RandomForestClassifier, max_depth=4, n_estimators=5), "Random Forest"),
(partial(XGBClassifier, n_jobs=1, max_depth=4, n_estimators=5), "XGB"),
]
# pylint: disable-next=undefined-variable
make_classifier_comparison_from_sklearn( # noqa
"Tree-Based Classifiers", tree_classifiers, 0.5, simulate=True, h=0.1
)
CPU times: user 1d 3h 26min 25s, sys: 51min 48s, total: 1d 4h 18min 14s Wall time: 14min 6s