API

Modules

• concrete.ml.common: Module for shared data structures and code.

• concrete.ml.common.check_inputs: Check and conversion tools.

• concrete.ml.common.debugging: Module for debugging.

• concrete.ml.common.debugging.custom_assert: Provide some variants of assert.

• concrete.ml.common.utils: Utils that can be re-used by other pieces of code in the module.

• concrete.ml.deployment: Module for deployment of the FHE model.

• concrete.ml.deployment.fhe_client_server: APIs for FHE deployment.

• concrete.ml.onnx: ONNX module.

• concrete.ml.onnx.convert: ONNX conversion related code.

• concrete.ml.onnx.onnx_impl_utils: Utility functions for onnx operator implementations.

• concrete.ml.onnx.onnx_model_manipulations: Some code to manipulate models.

• concrete.ml.onnx.onnx_utils: Utils to interpret an ONNX model with numpy.

• concrete.ml.onnx.ops_impl: ONNX ops implementation in python + numpy.

• concrete.ml.pytest: Module which is used to contain common functions for pytest.

• concrete.ml.pytest.torch_models: Torch modules for our pytests.

• concrete.ml.pytest.utils: Common functions or lists for test files, which can't be put in fixtures.

• concrete.ml.quantization: Modules for quantization.

• concrete.ml.quantization.base_quantized_op: Base Quantized Op class that implements quantization for a float numpy op.

• concrete.ml.quantization.post_training: Post Training Quantization methods.

• concrete.ml.quantization.quantized_module: QuantizedModule API.

• concrete.ml.quantization.quantized_ops: Quantized versions of the ONNX operators for post training quantization.

• concrete.ml.quantization.quantizers: Quantization utilities for a numpy array/tensor.

• concrete.ml.sklearn: Import sklearn models.

• concrete.ml.sklearn.base: Module that contains base classes for our libraries estimators.

• concrete.ml.sklearn.glm: Implement sklearn's Generalized Linear Models (GLM).

• concrete.ml.sklearn.linear_model: Implement sklearn linear model.

• concrete.ml.sklearn.protocols: Protocols.

• concrete.ml.sklearn.qnn: Scikit-learn interface for concrete quantized neural networks.

• concrete.ml.sklearn.rf: Implements RandomForest models.

• concrete.ml.sklearn.svm: Implement Support Vector Machine.

• concrete.ml.sklearn.torch_modules: Implement torch module.

• concrete.ml.sklearn.tree: Implement the sklearn tree models.

• concrete.ml.sklearn.tree_to_numpy: Implements the conversion of a tree model to a numpy function.

• concrete.ml.sklearn.xgb: Implements XGBoost models.

• concrete.ml.torch: Modules for torch to numpy conversion.

• concrete.ml.torch.compile: torch compilation function.

• concrete.ml.torch.numpy_module: A torch to numpy module.

• concrete.ml.version: File to manage the version of the package.

Classes

• fhe_client_server.FHEModelClient: Client API to encrypt and decrypt FHE data.

• fhe_client_server.FHEModelDev: Dev API to save the model and then load and run the FHE circuit.

• fhe_client_server.FHEModelServer: Server API to load and run the FHE circuit.

• ops_impl.ONNXMixedFunction: A mixed quantized-raw valued onnx function.

• torch_models.BranchingGemmModule: Torch model with some branching and skip connections.

• torch_models.BranchingModule: Torch model with some branching and skip connections.

• torch_models.CNN: Torch CNN model for the tests.

• torch_models.CNNGrouped: Torch CNN model with grouped convolution for compile torch tests.

• torch_models.CNNInvalid: Torch CNN model for the tests.

• torch_models.CNNMaxPool: Torch CNN model for the tests with a max pool.

• torch_models.CNNOther: Torch CNN model for the tests.

• torch_models.FC: Torch model for the tests.

• torch_models.FCSeq: Torch model that should generate MatMul->Add ONNX patterns.

• torch_models.FCSeqAddBiasVec: Torch model that should generate MatMul->Add ONNX patterns.

• torch_models.FCSmall: Torch model for the tests.

• torch_models.MultiInputNN: Torch model to test multiple inputs forward.

• torch_models.MultiOpOnSingleInputConvNN: Network that applies two quantized operations on a single input.

• torch_models.NetWithConcatUnsqueeze: Torch model to test the concat and unsqueeze operators.

• torch_models.NetWithLoops: Torch model, where we reuse some elements in a loop.

• torch_models.QATTestModule: Torch model that implements a simple non-uniform quantizer.

• torch_models.SimpleQAT: Torch model implements a step function that needs Greater, Cast and Where.

• torch_models.SingleMixNet: Torch model that with a single conv layer that produces the output, e.g. a blur filter.

• torch_models.StepActivationModule: Torch model implements a step function that needs Greater, Cast and Where.

• torch_models.TinyCNN: A very small CNN.

• torch_models.TinyQATCNN: A very small QAT CNN to classify the sklearn digits dataset.

• torch_models.TorchSum: Torch model to test the ReduceSum ONNX operator in a leveled circuit.

• torch_models.TorchSumMod: Torch model to test the ReduceSum ONNX operator in a circuit containing a PBS.

• torch_models.UnivariateModule: Torch model that calls univariate and shape functions of torch.

• base_quantized_op.QuantizedMixingOp: An operator that mixes (adds or multiplies) together encrypted inputs.

• base_quantized_op.QuantizedOp: Base class for quantized ONNX ops implemented in numpy.

• base_quantized_op.QuantizedOpUnivariateOfEncrypted: An univariate operator of an encrypted value.

• post_training.ONNXConverter: Base ONNX to Concrete ML computation graph conversion class.

• post_training.PostTrainingAffineQuantization: Post-training Affine Quantization.

• post_training.PostTrainingQATImporter: Converter of Quantization Aware Training networks.

• quantized_module.QuantizedModule: Inference for a quantized model.

• quantized_ops.QuantizedAbs: Quantized Abs op.

• quantized_ops.QuantizedAdd: Quantized Addition operator.

• quantized_ops.QuantizedAvgPool: Quantized Average Pooling op.

• quantized_ops.QuantizedBatchNormalization: Quantized Batch normalization with encrypted input and in-the-clear normalization params.

• quantized_ops.QuantizedBrevitasQuant: Brevitas uniform quantization with encrypted input.

• quantized_ops.QuantizedCast: Cast the input to the required data type.

• quantized_ops.QuantizedCelu: Quantized Celu op.

• quantized_ops.QuantizedClip: Quantized clip op.

• quantized_ops.QuantizedConcat: Concatenate operator.

• quantized_ops.QuantizedConv: Quantized Conv op.

• quantized_ops.QuantizedDiv: Div operator /.

• quantized_ops.QuantizedElu: Quantized Elu op.

• quantized_ops.QuantizedErf: Quantized erf op.

• quantized_ops.QuantizedExp: Quantized Exp op.

• quantized_ops.QuantizedFlatten: Quantized flatten for encrypted inputs.

• quantized_ops.QuantizedFloor: Quantized Floor op.

• quantized_ops.QuantizedGemm: Quantized Gemm op.

• quantized_ops.QuantizedGreater: Comparison operator >.

• quantized_ops.QuantizedGreaterOrEqual: Comparison operator >=.

• quantized_ops.QuantizedHardSigmoid: Quantized HardSigmoid op.

• quantized_ops.QuantizedHardSwish: Quantized Hardswish op.

• quantized_ops.QuantizedIdentity: Quantized Identity op.

• quantized_ops.QuantizedLeakyRelu: Quantized LeakyRelu op.

• quantized_ops.QuantizedLess: Comparison operator <.

• quantized_ops.QuantizedLessOrEqual: Comparison operator <=.

• quantized_ops.QuantizedLog: Quantized Log op.

• quantized_ops.QuantizedMatMul: Quantized MatMul op.

• quantized_ops.QuantizedMax: Quantized Max op.

• quantized_ops.QuantizedMaxPool: Quantized Max Pooling op.

• quantized_ops.QuantizedMin: Quantized Min op.

• quantized_ops.QuantizedMul: Multiplication operator.

• quantized_ops.QuantizedNeg: Quantized Neg op.

• quantized_ops.QuantizedNot: Quantized Not op.

• quantized_ops.QuantizedOr: Or operator ||.

• quantized_ops.QuantizedPRelu: Quantized PRelu op.

• quantized_ops.QuantizedPad: Quantized Padding op.

• quantized_ops.QuantizedPow: Quantized pow op.

• quantized_ops.QuantizedReduceSum: ReduceSum with encrypted input.

• quantized_ops.QuantizedRelu: Quantized Relu op.

• quantized_ops.QuantizedReshape: Quantized Reshape op.

• quantized_ops.QuantizedRound: Quantized round op.

• quantized_ops.QuantizedSelu: Quantized Selu op.

• quantized_ops.QuantizedSigmoid: Quantized sigmoid op.

• quantized_ops.QuantizedSign: Quantized Neg op.

• quantized_ops.QuantizedSoftplus: Quantized Softplus op.

• quantized_ops.QuantizedSub: Subtraction operator.

• quantized_ops.QuantizedTanh: Quantized Tanh op.

• quantized_ops.QuantizedTranspose: Transpose operator for quantized inputs.

• quantized_ops.QuantizedUnsqueeze: Unsqueeze operator.

• quantized_ops.QuantizedWhere: Where operator on quantized arrays.

• quantizers.MinMaxQuantizationStats: Calibration set statistics.

• quantizers.QuantizationOptions: Options for quantization.

• quantizers.QuantizedArray: Abstraction of quantized array.

• quantizers.UniformQuantizationParameters: Quantization parameters for uniform quantization.

• quantizers.UniformQuantizer: Uniform quantizer.

• base.BaseTreeClassifierMixin: Mixin class for tree-based classifiers.

• base.BaseTreeEstimatorMixin: Mixin class for tree-based estimators.

• base.BaseTreeRegressorMixin: Mixin class for tree-based regressors.

• base.QuantizedTorchEstimatorMixin: Mixin that provides quantization for a torch module and follows the Estimator API.

• base.SklearnLinearClassifierMixin: A Mixin class for sklearn linear classifiers with FHE.

• base.SklearnLinearModelMixin: A Mixin class for sklearn linear models with FHE.

• glm.GammaRegressor: A Gamma regression model with FHE.

• glm.PoissonRegressor: A Poisson regression model with FHE.

• glm.TweedieRegressor: A Tweedie regression model with FHE.

• linear_model.ElasticNet: An ElasticNet regression model with FHE.

• linear_model.Lasso: A Lasso regression model with FHE.

• linear_model.LinearRegression: A linear regression model with FHE.

• linear_model.LogisticRegression: A logistic regression model with FHE.

• linear_model.Ridge: A Ridge regression model with FHE.

• protocols.ConcreteBaseClassifierProtocol: Concrete classifier protocol.

• protocols.ConcreteBaseEstimatorProtocol: A Concrete Estimator Protocol.

• protocols.ConcreteBaseRegressorProtocol: Concrete regressor protocol.

• protocols.Quantizer: Quantizer Protocol.

• qnn.FixedTypeSkorchNeuralNet: A mixin with a helpful modification to a skorch estimator that fixes the module type.

• qnn.NeuralNetClassifier: Scikit-learn interface for quantized FHE compatible neural networks.

• qnn.NeuralNetRegressor: Scikit-learn interface for quantized FHE compatible neural networks.

• qnn.QuantizedSkorchEstimatorMixin: Mixin class that adds quantization features to Skorch NN estimators.

• qnn.SparseQuantNeuralNetImpl: Sparse Quantized Neural Network classifier.

• rf.RandomForestClassifier: Implements the RandomForest classifier.

• rf.RandomForestRegressor: Implements the RandomForest regressor.

• svm.LinearSVC: A Classification Support Vector Machine (SVM).

• svm.LinearSVR: A Regression Support Vector Machine (SVM).

• tree.DecisionTreeClassifier: Implements the sklearn DecisionTreeClassifier.

• tree.DecisionTreeRegressor: Implements the sklearn DecisionTreeClassifier.

• tree_to_numpy.Task: Task enumerate.

• xgb.XGBClassifier: Implements the XGBoost classifier.

• xgb.XGBRegressor: Implements the XGBoost regressor.

• numpy_module.NumpyModule: General interface to transform a torch.nn.Module to numpy module.

Functions

• check_inputs.check_X_y_and_assert: sklearn.utils.check_X_y with an assert.

• check_inputs.check_array_and_assert: sklearn.utils.check_array with an assert.

• custom_assert.assert_false: Provide a custom assert to check that the condition is False.

• custom_assert.assert_not_reached: Provide a custom assert to check that a piece of code is never reached.

• custom_assert.assert_true: Provide a custom assert to check that the condition is True.

• utils.check_there_is_no_p_error_options_in_configuration: Check the user did not set p_error or global_p_error in configuration.

• utils.generate_proxy_function: Generate a proxy function for a function accepting only *args type arguments.

• utils.get_onnx_opset_version: Return the ONNX opset_version.

• utils.manage_parameters_for_pbs_errors: Return (p_error, global_p_error) that we want to give to Concrete-Numpy and the compiler.

• utils.replace_invalid_arg_name_chars: Sanitize arg_name, replacing invalid chars by _.

• convert.get_equivalent_numpy_forward: Get the numpy equivalent forward of the provided ONNX model.

• convert.get_equivalent_numpy_forward_and_onnx_model: Get the numpy equivalent forward of the provided torch Module.

• onnx_impl_utils.compute_conv_output_dims: Compute the output shape of a pool or conv operation.

• onnx_impl_utils.compute_onnx_pool_padding: Compute any additional padding needed to compute pooling layers.

• onnx_impl_utils.numpy_onnx_pad: Pad a tensor according to ONNX spec, using an optional custom pad value.

• onnx_impl_utils.onnx_avgpool_compute_norm_const: Compute the average pooling normalization constant.

• onnx_model_manipulations.clean_graph_after_node_name: Clean the graph of the onnx model by removing nodes after the given node name.

• onnx_model_manipulations.clean_graph_after_node_op_type: Clean the graph of the onnx model by removing nodes after the given node type.

• onnx_model_manipulations.keep_following_outputs_discard_others: Keep the outputs given in outputs_to_keep and remove the others from the model.

• onnx_model_manipulations.remove_identity_nodes: Remove identity nodes from a model.

• onnx_model_manipulations.remove_node_types: Remove unnecessary nodes from the ONNX graph.

• onnx_model_manipulations.remove_unused_constant_nodes: Remove unused Constant nodes in the provided onnx model.

• onnx_model_manipulations.simplify_onnx_model: Simplify an ONNX model, removes unused Constant nodes and Identity nodes.

• onnx_utils.execute_onnx_with_numpy: Execute the provided ONNX graph on the given inputs.

• onnx_utils.get_attribute: Get the attribute from an ONNX AttributeProto.

• onnx_utils.get_op_type: Construct the qualified type name of the ONNX operator.

• onnx_utils.remove_initializer_from_input: Remove initializers from model inputs.

• ops_impl.cast_to_float: Cast values to floating points.

• ops_impl.numpy_abs: Compute abs in numpy according to ONNX spec.

• ops_impl.numpy_acos: Compute acos in numpy according to ONNX spec.

• ops_impl.numpy_acosh: Compute acosh in numpy according to ONNX spec.

• ops_impl.numpy_add: Compute add in numpy according to ONNX spec.

• ops_impl.numpy_asin: Compute asin in numpy according to ONNX spec.

• ops_impl.numpy_asinh: Compute sinh in numpy according to ONNX spec.

• ops_impl.numpy_atan: Compute atan in numpy according to ONNX spec.

• ops_impl.numpy_atanh: Compute atanh in numpy according to ONNX spec.

• ops_impl.numpy_avgpool: Compute Average Pooling using Torch.

• ops_impl.numpy_batchnorm: Compute the batch normalization of the input tensor.

• ops_impl.numpy_cast: Execute ONNX cast in Numpy.

• ops_impl.numpy_celu: Compute celu in numpy according to ONNX spec.

• ops_impl.numpy_concatenate: Apply concatenate in numpy according to ONNX spec.

• ops_impl.numpy_constant: Return the constant passed as a kwarg.

• ops_impl.numpy_cos: Compute cos in numpy according to ONNX spec.

• ops_impl.numpy_cosh: Compute cosh in numpy according to ONNX spec.

• ops_impl.numpy_div: Compute div in numpy according to ONNX spec.

• ops_impl.numpy_elu: Compute elu in numpy according to ONNX spec.

• ops_impl.numpy_equal: Compute equal in numpy according to ONNX spec.

• ops_impl.numpy_erf: Compute erf in numpy according to ONNX spec.

• ops_impl.numpy_exp: Compute exponential in numpy according to ONNX spec.

• ops_impl.numpy_flatten: Flatten a tensor into a 2d array.

• ops_impl.numpy_floor: Compute Floor in numpy according to ONNX spec.

• ops_impl.numpy_greater: Compute greater in numpy according to ONNX spec.

• ops_impl.numpy_greater_float: Compute greater in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_greater_or_equal: Compute greater or equal in numpy according to ONNX spec.

• ops_impl.numpy_greater_or_equal_float: Compute greater or equal in numpy according to ONNX specs and cast outputs to floats.

• ops_impl.numpy_hardsigmoid: Compute hardsigmoid in numpy according to ONNX spec.

• ops_impl.numpy_hardswish: Compute hardswish in numpy according to ONNX spec.

• ops_impl.numpy_identity: Compute identity in numpy according to ONNX spec.

• ops_impl.numpy_leakyrelu: Compute leakyrelu in numpy according to ONNX spec.

• ops_impl.numpy_less: Compute less in numpy according to ONNX spec.

• ops_impl.numpy_less_float: Compute less in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_less_or_equal: Compute less or equal in numpy according to ONNX spec.

• ops_impl.numpy_less_or_equal_float: Compute less or equal in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_log: Compute log in numpy according to ONNX spec.

• ops_impl.numpy_matmul: Compute matmul in numpy according to ONNX spec.

• ops_impl.numpy_max: Compute Max in numpy according to ONNX spec.

• ops_impl.numpy_maxpool: Compute Max Pooling using Torch.

• ops_impl.numpy_min: Compute Min in numpy according to ONNX spec.

• ops_impl.numpy_mul: Compute mul in numpy according to ONNX spec.

• ops_impl.numpy_neg: Compute Negative in numpy according to ONNX spec.

• ops_impl.numpy_not: Compute not in numpy according to ONNX spec.

• ops_impl.numpy_not_float: Compute not in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_or: Compute or in numpy according to ONNX spec.

• ops_impl.numpy_or_float: Compute or in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_pow: Compute pow in numpy according to ONNX spec.

• ops_impl.numpy_relu: Compute relu in numpy according to ONNX spec.

• ops_impl.numpy_round: Compute round in numpy according to ONNX spec.

• ops_impl.numpy_selu: Compute selu in numpy according to ONNX spec.

• ops_impl.numpy_sigmoid: Compute sigmoid in numpy according to ONNX spec.

• ops_impl.numpy_sign: Compute Sign in numpy according to ONNX spec.

• ops_impl.numpy_sin: Compute sin in numpy according to ONNX spec.

• ops_impl.numpy_sinh: Compute sinh in numpy according to ONNX spec.

• ops_impl.numpy_softmax: Compute softmax in numpy according to ONNX spec.

• ops_impl.numpy_softplus: Compute softplus in numpy according to ONNX spec.

• ops_impl.numpy_sub: Compute sub in numpy according to ONNX spec.

• ops_impl.numpy_tan: Compute tan in numpy according to ONNX spec.

• ops_impl.numpy_tanh: Compute tanh in numpy according to ONNX spec.

• ops_impl.numpy_thresholdedrelu: Compute thresholdedrelu in numpy according to ONNX spec.

• ops_impl.numpy_transpose: Transpose in numpy according to ONNX spec.

• ops_impl.numpy_where: Compute the equivalent of numpy.where.

• ops_impl.numpy_where_body: Compute the equivalent of numpy.where.

• ops_impl.onnx_func_raw_args: Decorate a numpy onnx function to flag the raw/non quantized inputs.

• utils.sanitize_test_and_train_datasets: Sanitize datasets depending on the model type.

• post_training.get_n_bits_dict: Convert the n_bits parameter into a proper dictionary.

• quantizers.fill_from_kwargs: Fill a parameter set structure from kwargs parameters.

• base.get_sklearn_linear_models: Return the list of available linear models in Concrete-ML.

• base.get_sklearn_models: Return the list of available models in Concrete-ML.

• base.get_sklearn_neural_net_models: Return the list of available neural net models in Concrete-ML.

• base.get_sklearn_tree_models: Return the list of available tree models in Concrete-ML.

• tree_to_numpy.tree_to_numpy: Convert the tree inference to a numpy functions using Hummingbird.

• compile.compile_brevitas_qat_model: Compile a Brevitas Quantization Aware Training model.

• compile.compile_onnx_model: Compile a torch module into a FHE equivalent.

• compile.compile_torch_model: Compile a torch module into a FHE equivalent.

• compile.convert_torch_tensor_or_numpy_array_to_numpy_array: Convert a torch tensor or a numpy array to a numpy array.