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.serialization: Serialization module.

• concrete.ml.common.serialization.decoder: Custom decoder for serialization.

• concrete.ml.common.serialization.dumpers: Dump functions for serialization.

• concrete.ml.common.serialization.encoder: Custom encoder for serialization.

• concrete.ml.common.serialization.loaders: Load functions for serialization.

• 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.pandas: Public API for encrypted data-frames.

• concrete.ml.pandas.client_engine: Define the framework used for managing keys (encrypt, decrypt) for encrypted data-frames.

• concrete.ml.pandas.dataframe: Define the encrypted data-frame framework.

• 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.linear_op_glwe_backend: GLWE backend for some supported layers.

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

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

• concrete.ml.quantization.quantized_module_passes: Optimization passes for QuantizedModules.

• 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.search_parameters: Modules for p_error search.

• concrete.ml.search_parameters.p_error_search: p_error binary search for classification and regression tasks.

• concrete.ml.sklearn: Import sklearn models.

• concrete.ml.sklearn.base: Base classes for all estimators.

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

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

• concrete.ml.sklearn.neighbors: Implement sklearn neighbors model.

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

• concrete.ml.sklearn.qnn_module: Sparse Quantized Neural Network torch module.

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

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

• concrete.ml.sklearn.tree: Implement DecisionTree 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.hybrid_backprop_linear: Linear layer implementations for backprop FHE-compatible models.

• concrete.ml.torch.hybrid_model: Implement the conversion of a torch model to a hybrid fhe/torch inference.

• concrete.ml.torch.lora: This module contains classes for LoRA (Low-Rank Adaptation) FHE training and custom layers.

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

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

Classes

• decoder.ConcreteDecoder: Custom json decoder to handle non-native types found in serialized Concrete ML objects.

• encoder.ConcreteEncoder: Custom json encoder to handle non-native types found in serialized Concrete ML objects.

• utils.CiphertextFormat: Type of ciphertext used as input/output for a model.

• utils.FheMode: Enum representing the execution mode.

• utils.HybridFHEMode: Simple enum for different modes of execution of HybridModel.

• fhe_client_server.DeploymentMode: Mode for the FHE API.

• 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.

• ops_impl.RawOpOutput: Type construct that marks an ndarray as a raw output of a quantized op.

• client_engine.ClientEngine: Define a framework that manages keys.

• dataframe.EncryptedDataFrame: Define an encrypted data-frame framework that supports Pandas operators and parameters.

• torch_models.AddNet: Torch model that performs a simple addition between two inputs.

• torch_models.AllZeroCNN: A CNN class that has all zero weights and biases.

• 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.ConcatFancyIndexing: Concat with fancy indexing.

• torch_models.Conv1dModel: Small model that uses a 1D convolution operator.

• torch_models.DoubleQuantQATMixNet: Torch model that with two different quantizers on the input.

• torch_models.EmbeddingModel: A torch model with an embedding layer.

• torch_models.EncryptedMatrixMultiplicationModel: PyTorch module for performing matrix multiplication between two encrypted values.

• torch_models.ExpandModel: Minimalist network that expands the input tensor to a larger size.

• 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.IdentityExpandModel: Model that only adds an empty dimension at axis 0.

• torch_models.IdentityExpandMultiOutputModel: Model that only adds an empty dimension at axis 0, and returns the initial input as well.

• torch_models.ManualLogisticRegressionTraining: PyTorch module for performing SGD training.

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

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

• torch_models.MultiInputNNDifferentSize: Torch model to test multiple inputs with different shape in the forward pass.

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

• torch_models.MultiOutputModel: Multi-output model.

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

• torch_models.NetWithConstantsFoldedBeforeOps: Torch QAT model that does not quantize the inputs.

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

• torch_models.PaddingNet: Torch QAT model that applies various padding patterns.

• torch_models.PartialQATModel: A model with a QAT Module.

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

• torch_models.QuantCustomModel: A small quantized network with Brevitas, trained on make_classification.

• torch_models.ShapeOperationsNet: Torch QAT model that reshapes the input.

• torch_models.SimpleNet: Fake torch model used to generate some onnx.

• 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.StepFunctionPTQ: 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 data-set.

• torch_models.TorchCustomModel: A small network with Brevitas, trained on make_classification.

• torch_models.TorchDivide: Torch model that performs a encrypted division between two inputs.

• torch_models.TorchMultiply: Torch model that performs a encrypted multiplication between two inputs.

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

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

• torch_models.WhereNet: Simple network with a where operation for testing.

• 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.

• linear_op_glwe_backend.GLWELinearLayerExecutor: GLWE execution helper for pure linear layers.

• post_training.CalibrationMode: Simple enum for different modes of execution of HybridModel.

• 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_module_passes.PowerOfTwoScalingRoundPBSAdapter: Detect neural network patterns that can be optimized with round PBS.

• quantized_ops.ONNXConstantOfShape: ConstantOfShape operator.

• quantized_ops.ONNXGather: Gather operator.

• quantized_ops.ONNXShape: Shape operator.

• quantized_ops.ONNXSlice: Slice operator.

• 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: Quantized Division operator.

• quantized_ops.QuantizedElu: Quantized Elu op.

• quantized_ops.QuantizedEqual: Comparison operator ==.

• quantized_ops.QuantizedErf: Quantized erf op.

• quantized_ops.QuantizedExp: Quantized Exp op.

• quantized_ops.QuantizedExpand: Expand operator for quantized tensors.

• 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: Quantized 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.QuantizedSqueeze: Squeeze operator.

• quantized_ops.QuantizedSub: Subtraction operator.

• quantized_ops.QuantizedTanh: Quantized Tanh op.

• quantized_ops.QuantizedTranspose: Transpose operator for quantized inputs.

• quantized_ops.QuantizedUnfold: Quantized Unfold op.

• 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.TorchUniformQuantizer: Uniform quantizer with a PyTorch implementation.

• quantizers.UniformQuantizationParameters: Quantization parameters for uniform quantization.

• quantizers.UniformQuantizer: Uniform quantizer.

• p_error_search.BinarySearch: Class for p_error hyper-parameter search for classification and regression tasks.

• base.BaseClassifier: Base class for linear and tree-based classifiers in Concrete ML.

• base.BaseEstimator: Base class for all estimators in Concrete ML.

• 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.SklearnKNeighborsClassifierMixin: A Mixin class for sklearn KNeighbors classifiers with FHE.

• base.SklearnKNeighborsMixin: A Mixin class for sklearn KNeighbors models with FHE.

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

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

• base.SklearnLinearRegressorMixin: A Mixin class for sklearn linear regressors with FHE.

• base.SklearnSGDClassifierMixin: A Mixin class for sklearn SGD classifiers with FHE.

• base.SklearnSGDRegressorMixin: A Mixin class for sklearn SGD regressors 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.

• linear_model.SGDClassifier: An FHE linear classifier model fitted with stochastic gradient descent.

• linear_model.SGDRegressor: An FHE linear regression model fitted with stochastic gradient descent.

• neighbors.KNeighborsClassifier: A k-nearest neighbors classifier model with FHE.

• qnn.NeuralNetClassifier: A Fully-Connected Neural Network classifier with FHE.

• qnn.NeuralNetRegressor: A Fully-Connected Neural Network regressor with FHE.

• qnn_module.SparseQuantNeuralNetwork: Sparse Quantized Neural Network.

• 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.

• xgb.XGBClassifier: Implements the XGBoost classifier.

• xgb.XGBRegressor: Implements the XGBoost regressor.

• hybrid_backprop_linear.BackwardModuleLinear: Backward module for linear layers.

• hybrid_backprop_linear.CustomLinear: Custom linear module.

• hybrid_backprop_linear.ForwardBackwardModule: Custom autograd function for forward and backward passes.

• hybrid_backprop_linear.ForwardModuleLinear: Forward module for linear layers.

• hybrid_model.HybridFHEModel: Convert a model to a hybrid model.

• hybrid_model.HybridFHEModelServer: Hybrid FHE Model Server.

• hybrid_model.LoggerStub: Placeholder type for a typical logger like the one from loguru.

• hybrid_model.RemoteModule: A wrapper class for the modules to be evaluated remotely with FHE.

• lora.LoraTrainer: Trainer class for LoRA fine-tuning with FHE support.

• lora.LoraTraining: LoraTraining module for fine-tuning with LoRA in a hybrid model setting.

• 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_X_y_and_assert_multi_output: sklearn.utils.check_X_y with an assert and multi-output handling.

• 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.

• decoder.object_hook: Define a custom object hook that enables loading any supported serialized values.

• dumpers.dump: Dump any Concrete ML object in a file.

• dumpers.dumps: Dump any object as a string.

• encoder.dump_name_and_value: Dump the value into a custom dict format.

• loaders.load: Load any Concrete ML object that provide a load_dict method.

• loaders.loads: Load any Concrete ML object that provide a dump_dict method.

• utils.all_values_are_floats: Indicate if all unpacked values are of a supported float dtype.

• utils.all_values_are_integers: Indicate if all unpacked values are of a supported integer dtype.

• utils.all_values_are_of_dtype: Indicate if all unpacked values are of the specified dtype(s).

• utils.array_allclose_and_same_shape: Check if two numpy arrays are equal within a tolerances and have the same shape.

• utils.check_compilation_device_is_valid_and_is_cuda: Check whether the device string for compilation or FHE execution is CUDA or CPU.

• utils.check_device_is_valid: Check whether the device string is valid or raise an exception.

• utils.check_dtype_and_cast: Convert any allowed type into an array and cast it if required.

• utils.check_execution_device_is_valid_and_is_cuda: Check whether the circuit can be executed on the required device.

• 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.compute_bits_precision: Compute the number of bits required to represent x.

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

• utils.get_model_class: Return the class of the model (instantiated or not), which can be a partial() instance.

• utils.get_model_name: Return the name of the model, which can be a partial() instance.

• utils.get_onnx_opset_version: Return the ONNX opset_version.

• utils.is_brevitas_model: Check if a model is a Brevitas type.

• utils.is_classifier_or_partial_classifier: Indicate if the model class represents a classifier.

• utils.is_model_class_in_a_list: Indicate if a model class, which can be a partial() instance, is an element of a_list.

• utils.is_pandas_dataframe: Indicate if the input container is a Pandas DataFrame.

• utils.is_pandas_series: Indicate if the input container is a Pandas Series.

• utils.is_pandas_type: Indicate if the input container is a Pandas DataFrame or Series.

• utils.is_regressor_or_partial_regressor: Indicate if the model class represents a regressor.

• utils.manage_parameters_for_pbs_errors: Return (p_error, global_p_error) that we want to give to Concrete.

• utils.process_rounding_threshold_bits: Check and process the rounding_threshold_bits parameter.

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

• utils.to_tuple: Make the input a tuple if it is not already the case.

• fhe_client_server.check_concrete_versions: Check that current versions match the ones used in development.

• convert.fuse_matmul_bias_to_gemm: Fuse sequence of matmul -> add into a gemm node.

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

• convert.get_equivalent_numpy_forward_from_onnx_tree: Get the numpy equivalent forward of the provided ONNX model for tree-based models only.

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

• convert.preprocess_onnx_model: Preprocess the ONNX model to be used for numpy execution.

• 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_impl_utils.rounded_comparison: Comparison operation using round_bit_pattern function.

• onnx_model_manipulations.clean_graph_after_node_op_type: Remove the nodes following first node matching node_op_type from the ONNX graph.

• onnx_model_manipulations.clean_graph_at_node_op_type: Remove the first node matching node_op_type and its following nodes from the ONNX graph.

• onnx_model_manipulations.convert_first_gather_to_matmul: Convert the first Gather node to a matrix multiplication node.

• 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.check_onnx_model: Check an ONNX model, handling large models (>2GB) by using external data.

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

• onnx_utils.execute_onnx_with_numpy_trees: Execute the provided ONNX graph on the given inputs for tree-based models only.

• 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_conv: Compute N-D convolution using Torch.

• 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_equal_float: Compute equal in numpy according to ONNX spec and cast outputs to floats.

• 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_gemm: Compute Gemm 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_unfold: Compute Unfold using Torch.

• 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.

• ops_impl.rounded_numpy_equal_for_trees: Compute rounded equal in numpy according to ONNX spec for tree-based models only.

• ops_impl.rounded_numpy_less_for_trees: Compute rounded less in numpy according to ONNX spec for tree-based models only.

• ops_impl.rounded_numpy_less_or_equal_for_trees: Compute rounded less or equal in numpy according to ONNX spec for tree-based models only.

• pandas.load_encrypted_dataframe: Load a serialized encrypted data-frame.

• pandas.merge: Merge two encrypted data-frames in FHE using Pandas parameters.

• utils.check_serialization: Check that the given object can properly be serialized.

• utils.data_calibration_processing: Reduce size of the given data-set.

• utils.get_random_samples: Select n_sample random elements from a 2D NumPy array.

• utils.get_sklearn_all_models_and_datasets: Get the pytest parameters to use for testing all models available in Concrete ML.

• utils.get_sklearn_linear_models_and_datasets: Get the pytest parameters to use for testing linear models.

• utils.get_sklearn_neighbors_models_and_datasets: Get the pytest parameters to use for testing neighbor models.

• utils.get_sklearn_neural_net_models_and_datasets: Get the pytest parameters to use for testing neural network models.

• utils.get_sklearn_tree_models_and_datasets: Get the pytest parameters to use for testing tree-based models.

• utils.instantiate_model_generic: Instantiate any Concrete ML model type.

• utils.load_torch_model: Load an object saved with torch.save() from a file or dict.

• utils.pandas_dataframe_are_equal: Determine if both data-frames are identical.

• utils.values_are_equal: Indicate if two values are equal.

• linear_op_glwe_backend.has_glwe_backend: Check if the GLWE backend is installed.

• 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.

• p_error_search.compile_and_simulated_fhe_inference: Get the quantized module of a given model in FHE, simulated or not.

• tree_to_numpy.add_transpose_after_last_node: Add transpose after last node.

• tree_to_numpy.assert_add_node_and_constant_in_xgboost_regressor_graph: Assert if an Add node with a specific constant exists in the ONNX graph.

• tree_to_numpy.get_onnx_model: Create ONNX model with Hummingbird convert method.

• tree_to_numpy.onnx_fp32_model_to_quantized_model: Build a FHE-compliant onnx-model using a fitted scikit-learn model.

• tree_to_numpy.preprocess_tree_predictions: Apply post-processing from the graph.

• tree_to_numpy.tree_onnx_graph_preprocessing: Apply pre-processing onto the ONNX graph.

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

• tree_to_numpy.tree_values_preprocessing: Pre-process tree values.

• tree_to_numpy.workaround_squeeze_node_xgboost: Workaround to fix torch issue that does not export the proper axis in the ONNX squeeze node.

• compile.build_quantized_module: Build a quantized module from a Torch or ONNX model.

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

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

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

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

• compile.has_any_qnn_layers: Check if a torch model has QNN layers.

• hybrid_model.convert_conv1d_to_linear: Convert all Conv1D layers in a module or a Conv1D layer itself to nn.Linear.

• hybrid_model.tuple_to_underscore_str: Convert a tuple to a string representation.

• hybrid_model.underscore_str_to_tuple: Convert a a string representation of a tuple to a tuple.

• lora.get_remote_names: Get names of modules to be executed remotely.

• lora.grad_to: Move parameter gradient to device.

• lora.optimizer_to: Move optimizer object to device.

• lora.setup_logger: Set up a logger that logs to both console and a file.