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.
115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
"""Tests lists of models in TorusML."""
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from concrete.ml.pytest.utils import MODELS_AND_DATASETS, UNIQUE_MODELS_AND_DATASETS
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from concrete.ml.sklearn import (
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_get_sklearn_all_models,
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_get_sklearn_linear_models,
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_get_sklearn_neighbors_models,
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_get_sklearn_neural_net_models,
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_get_sklearn_tree_models,
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)
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from concrete.ml.sklearn.glm import GammaRegressor, PoissonRegressor, TweedieRegressor
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from concrete.ml.sklearn.linear_model import (
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ElasticNet,
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Lasso,
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LinearRegression,
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LogisticRegression,
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Ridge,
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SGDClassifier,
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SGDRegressor,
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)
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from concrete.ml.sklearn.neighbors import KNeighborsClassifier
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from concrete.ml.sklearn.qnn import NeuralNetClassifier, NeuralNetRegressor
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from concrete.ml.sklearn.rf import RandomForestClassifier, RandomForestRegressor
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from concrete.ml.sklearn.svm import LinearSVC, LinearSVR
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from concrete.ml.sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from concrete.ml.sklearn.xgb import XGBClassifier, XGBRegressor
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def test_get_sklearn_models():
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"""List all available models in TorusML."""
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all_models = _get_sklearn_all_models()
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linear_models = _get_sklearn_linear_models()
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tree_models = _get_sklearn_tree_models()
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neural_network_models = _get_sklearn_neural_net_models()
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neighbor_models = _get_sklearn_neighbors_models()
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print("All models: ")
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for m in all_models:
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print(f" {m}")
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print("Linear models: ")
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for m in linear_models:
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print(f" {m}")
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print("Tree models: ")
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for m in tree_models:
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print(f" {m}")
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print("Neural net models: ")
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for m in neural_network_models:
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print(f" {m}")
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print("Neighbors models: ")
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for m in neighbor_models:
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print(f" {m}")
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# Check values
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expected_neural_network_models = {NeuralNetClassifier, NeuralNetRegressor}
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assert (
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set(neural_network_models) == expected_neural_network_models
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), "Please change the expected number of models if neural network models have been added"
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expected_tree_models = {
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DecisionTreeClassifier,
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DecisionTreeRegressor,
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RandomForestClassifier,
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RandomForestRegressor,
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XGBClassifier,
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XGBRegressor,
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}
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assert (
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set(tree_models) == expected_tree_models
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), "Please change the expected number of tree models if new models have been added"
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expected_linear_models = {
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ElasticNet,
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GammaRegressor,
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Lasso,
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LinearRegression,
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LinearSVC,
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LinearSVR,
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LogisticRegression,
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PoissonRegressor,
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Ridge,
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SGDRegressor,
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SGDClassifier,
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TweedieRegressor,
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}
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assert (
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set(linear_models) == expected_linear_models
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), "Please change the expected number of linear models if new models have been added"
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expected_neighbor_models = {KNeighborsClassifier}
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# Check number
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expected = set(all_models)
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obtained = (
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expected_linear_models
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| expected_tree_models
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| expected_neural_network_models
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| expected_neighbor_models
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)
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assert expected == obtained, (
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"Please change the expected number of models if new models have been added\n"
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f"Missing: {expected ^ obtained}"
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)
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def test_models_and_datasets():
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"""Check that the tested model's configuration lists remain fixed."""
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assert len(MODELS_AND_DATASETS) == 34
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assert len(UNIQUE_MODELS_AND_DATASETS) == 22
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