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.
2.8 KiB
module concrete.ml.common.check_inputs
Check and conversion tools.
Utils that are used to check (including convert) some data types which are compatible with scikit-learn to numpy types.
function check_array_and_assert
check_array_and_assert(X, *args, **kwargs)
sklearn.utils.check_array with an assert.
Equivalent of sklearn.utils.check_array, with a final assert that the type is one which is supported by TorusML.
Args:
X(object): Input object to check / convert*args: The arguments to pass to check_array**kwargs: The keyword arguments to pass to check_array
Returns: The converted and validated array
function check_X_y_and_assert
check_X_y_and_assert(X, y, *args, **kwargs)
sklearn.utils.check_X_y with an assert.
Equivalent of sklearn.utils.check_X_y, with a final assert that the type is one which is supported by TorusML.
Args:
X(ndarray, list, sparse matrix): Input datay(ndarray, list, sparse matrix): Labels*args: The arguments to pass to check_X_y**kwargs: The keyword arguments to pass to check_X_y
Returns: The converted and validated arrays
function check_X_y_and_assert_multi_output
check_X_y_and_assert_multi_output(X, y, *args, **kwargs)
sklearn.utils.check_X_y with an assert and multi-output handling.
Equivalent of sklearn.utils.check_X_y, with a final assert that the type is one which is supported by TorusML. If y is 2D, allows multi-output.
Args:
X(ndarray, list, sparse matrix): Input datay(ndarray, list, sparse matrix): Labels*args: The arguments to pass to check_X_y**kwargs: The keyword arguments to pass to check_X_y
Returns: The converted and validated arrays with multi-output targets.