Concrete ML
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1.1
1.1
  • What is Concrete ML?
  • Getting Started
    • Installation
    • Key Concepts
    • Inference in the Cloud
    • Demos and Tutorials
  • Built-in Models
    • Linear Models
    • Tree-based Models
    • Neural Networks
    • Pandas
    • Built-in Model Examples
  • Deep Learning
    • Using Torch
    • Using ONNX
    • Step-by-step Guide
    • Deep Learning Examples
    • Debugging Models
    • Optimizing Inference
  • Advanced topics
    • Quantization
    • Pruning
    • Compilation
    • Prediction with FHE
    • Production Deployment
    • Advanced Features
    • Serialization
  • Developer Guide
    • Workflow
      • Set Up the Project
      • Set Up Docker
      • Documentation
      • Support and Issues
      • Contributing
    • Inner Workings
      • Importing ONNX
      • Quantization Tools
      • FHE Op-graph Design
      • External Libraries
    • API
      • concrete.ml.common.check_inputs.md
      • concrete.ml.common.debugging.custom_assert.md
      • concrete.ml.common.debugging.md
      • concrete.ml.common.md
      • concrete.ml.common.serialization.decoder.md
      • concrete.ml.common.serialization.dumpers.md
      • concrete.ml.common.serialization.encoder.md
      • concrete.ml.common.serialization.loaders.md
      • concrete.ml.common.serialization.md
      • concrete.ml.common.utils.md
      • concrete.ml.deployment.deploy_to_aws.md
      • concrete.ml.deployment.deploy_to_docker.md
      • concrete.ml.deployment.fhe_client_server.md
      • concrete.ml.deployment.md
      • concrete.ml.deployment.server.md
      • concrete.ml.deployment.utils.md
      • concrete.ml.onnx.convert.md
      • concrete.ml.onnx.md
      • concrete.ml.onnx.onnx_impl_utils.md
      • concrete.ml.onnx.onnx_model_manipulations.md
      • concrete.ml.onnx.onnx_utils.md
      • concrete.ml.onnx.ops_impl.md
      • concrete.ml.pytest.md
      • concrete.ml.pytest.torch_models.md
      • concrete.ml.pytest.utils.md
      • concrete.ml.quantization.base_quantized_op.md
      • concrete.ml.quantization.md
      • concrete.ml.quantization.post_training.md
      • concrete.ml.quantization.quantized_module.md
      • concrete.ml.quantization.quantized_ops.md
      • concrete.ml.quantization.quantizers.md
      • concrete.ml.search_parameters.md
      • concrete.ml.search_parameters.p_error_search.md
      • concrete.ml.sklearn.base.md
      • concrete.ml.sklearn.glm.md
      • concrete.ml.sklearn.linear_model.md
      • concrete.ml.sklearn.md
      • concrete.ml.sklearn.qnn.md
      • concrete.ml.sklearn.qnn_module.md
      • concrete.ml.sklearn.rf.md
      • concrete.ml.sklearn.svm.md
      • concrete.ml.sklearn.tree.md
      • concrete.ml.sklearn.tree_to_numpy.md
      • concrete.ml.sklearn.xgb.md
      • concrete.ml.torch.compile.md
      • concrete.ml.torch.md
      • concrete.ml.torch.numpy_module.md
      • concrete.ml.version.md
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  1. Developer Guide

API

PreviousExternal LibrariesNextconcrete.ml.common.check_inputs.md

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Modules

  • : Module for shared data structures and code.

  • : Check and conversion tools.

  • : Module for debugging.

  • : Provide some variants of assert.

  • : Serialization module.

  • : Custom decoder for serialization.

  • : Dump functions for serialization.

  • : Custom encoder for serialization.

  • : Load functions for serialization.

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

  • : Module for deployment of the FHE model.

  • : Methods to deploy a client/server to AWS.

  • : Methods to deploy a server using Docker.

  • : APIs for FHE deployment.

  • : Deployment server.

  • : Utils.

  • : ONNX module.

  • : ONNX conversion related code.

  • : Utility functions for onnx operator implementations.

  • : Some code to manipulate models.

  • : Utils to interpret an ONNX model with numpy.

  • : ONNX ops implementation in Python + NumPy.

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

  • : Torch modules for our pytests.

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

  • : Modules for quantization.

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

  • : Post Training Quantization methods.

  • : QuantizedModule API.

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

  • : Quantization utilities for a numpy array/tensor.

  • : Modules for p_error search.

  • : p_error binary search for classification and regression tasks.

  • : Import sklearn models.

  • : Base classes for all estimators.

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

  • : Implement sklearn linear model.

  • : Scikit-learn interface for fully-connected quantized neural networks.

  • : Sparse Quantized Neural Network torch module.

  • : Implement RandomForest models.

  • : Implement Support Vector Machine.

  • : Implement DecisionTree models.

  • : Implements the conversion of a tree model to a numpy function.

  • : Implements XGBoost models.

  • : Modules for torch to numpy conversion.

  • : torch compilation function.

  • : A torch to numpy module.

  • : File to manage the version of the package.

Classes

Functions

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

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

: Enum representing the execution mode.

: AWSInstance.

: Client API to encrypt and decrypt FHE data.

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

: Server API to load and run the FHE circuit.

: A mixed quantized-raw valued onnx function.

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

: Torch model with some branching and skip connections.

: Torch model with some branching and skip connections.

: Torch CNN model for the tests.

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

: Torch CNN model for the tests.

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

: Torch CNN model for the tests.

: Concat with fancy indexing.

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

: Torch model for the tests.

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

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

: Torch model for the tests.

: Torch model to test multiple inputs forward.

: Torch model to test multiple inputs forward.

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

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

: Torch model to test the concat and unsqueeze operators.

: Torch QAT model that does not quantize the inputs.

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

: Torch QAT model that applies various padding patterns.

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

: A small quantized network with Brevitas for FashionMNIST classification.

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

: Torch QAT model that reshapes the input.

: Fake torch model used to generate some onnx.

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

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

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

: A very small CNN.

: A very small QAT CNN to classify the sklearn digits data-set.

: A small network with Brevitas, trained on make_classification.

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

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

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

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

: Base class for quantized ONNX ops implemented in numpy.

: An univariate operator of an encrypted value.

: Base ONNX to Concrete ML computation graph conversion class.

: Post-training Affine Quantization.

: Converter of Quantization Aware Training networks.

: Inference for a quantized model.

: ConstantOfShape operator.

: Gather operator.

: Shape operator.

: Slice operator.

: Quantized Abs op.

: Quantized Addition operator.

: Quantized Average Pooling op.

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

: Brevitas uniform quantization with encrypted input.

: Cast the input to the required data type.

: Quantized Celu op.

: Quantized clip op.

: Concatenate operator.

: Quantized Conv op.

: Div operator /.

: Quantized Elu op.

: Quantized erf op.

: Quantized Exp op.

: Quantized flatten for encrypted inputs.

: Quantized Floor op.

: Quantized Gemm op.

: Comparison operator >.

: Comparison operator >=.

: Quantized HardSigmoid op.

: Quantized Hardswish op.

: Quantized Identity op.

: Quantized LeakyRelu op.

: Comparison operator <.

: Comparison operator <=.

: Quantized Log op.

: Quantized MatMul op.

: Quantized Max op.

: Quantized Max Pooling op.

: Quantized Min op.

: Multiplication operator.

: Quantized Neg op.

: Quantized Not op.

: Or operator ||.

: Quantized PRelu op.

: Quantized Padding op.

: Quantized pow op.

: ReduceSum with encrypted input.

: Quantized Relu op.

: Quantized Reshape op.

: Quantized round op.

: Quantized Selu op.

: Quantized sigmoid op.

: Quantized Neg op.

: Quantized Softplus op.

: Squeeze operator.

: Subtraction operator.

: Quantized Tanh op.

: Transpose operator for quantized inputs.

: Unsqueeze operator.

: Where operator on quantized arrays.

: Calibration set statistics.

: Options for quantization.

: Abstraction of quantized array.

: Quantization parameters for uniform quantization.

: Uniform quantizer.

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

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

: Base class for all estimators in Concrete ML.

: Mixin class for tree-based classifiers.

: Mixin class for tree-based estimators.

: Mixin class for tree-based regressors.

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

: A Mixin class for sklearn linear classifiers with FHE.

: A Mixin class for sklearn linear models with FHE.

: A Mixin class for sklearn linear regressors with FHE.

: A Gamma regression model with FHE.

: A Poisson regression model with FHE.

: A Tweedie regression model with FHE.

: An ElasticNet regression model with FHE.

: A Lasso regression model with FHE.

: A linear regression model with FHE.

: A logistic regression model with FHE.

: A Ridge regression model with FHE.

: A Fully-Connected Neural Network classifier with FHE.

: A Fully-Connected Neural Network regressor with FHE.

: Sparse Quantized Neural Network.

: Implements the RandomForest classifier.

: Implements the RandomForest regressor.

: A Classification Support Vector Machine (SVM).

: A Regression Support Vector Machine (SVM).

: Implements the sklearn DecisionTreeClassifier.

: Implements the sklearn DecisionTreeClassifier.

: Implements the XGBoost classifier.

: Implements the XGBoost regressor.

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

: sklearn.utils.check_X_y with an assert.

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

: sklearn.utils.check_array with an assert.

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

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

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

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

: Dump any Concrete ML object in a file.

: Dump any object as a string.

: Dump the value into a custom dict format.

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

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

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

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

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

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

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

: Compute the number of bits required to represent x.

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

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

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

: Return the ONNX opset_version.

: Check if a model is a Brevitas type.

: Indicate if the model class represents a classifier.

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

: Indicate if the input container is a Pandas DataFrame.

: Indicate if the input container is a Pandas Series.

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

: Indicate if the model class represents a regressor.

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

: Sanitize arg_name, replacing invalid chars by _.

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

: Create a EC2 instance.

: Terminate a AWS EC2 instance.

: Deploy a model to a EC2 AWS instance.

: Deploy a model.

: Terminate a AWS EC2 instance.

: Wait for AWS EC2 instance termination.

: Build server Docker image.

: Delete a Docker image.

: Deploy function.

: Kill all containers that use a given image.

: Filter logs based on previous logs.

: Check if ssh connection is available.

: Wait for connection to be available.

: Get the numpy equivalent forward of the provided ONNX model.

: Get the numpy equivalent forward of the provided torch Module.

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

: Compute any additional padding needed to compute pooling layers.

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

: Compute the average pooling normalization constant.

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

: Clean the graph of the onnx model by removing nodes at the given node type.

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

: Remove identity nodes from a model.

: Remove unnecessary nodes from the ONNX graph.

: Remove unused Constant nodes in the provided onnx model.

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

: Execute the provided ONNX graph on the given inputs.

: Get the attribute from an ONNX AttributeProto.

: Construct the qualified type name of the ONNX operator.

: Remove initializers from model inputs.

: Cast values to floating points.

: Compute abs in numpy according to ONNX spec.

: Compute acos in numpy according to ONNX spec.

: Compute acosh in numpy according to ONNX spec.

: Compute add in numpy according to ONNX spec.

: Compute asin in numpy according to ONNX spec.

: Compute sinh in numpy according to ONNX spec.

: Compute atan in numpy according to ONNX spec.

: Compute atanh in numpy according to ONNX spec.

: Compute Average Pooling using Torch.

: Compute the batch normalization of the input tensor.

: Execute ONNX cast in Numpy.

: Compute celu in numpy according to ONNX spec.

: Apply concatenate in numpy according to ONNX spec.

: Return the constant passed as a kwarg.

: Compute cos in numpy according to ONNX spec.

: Compute cosh in numpy according to ONNX spec.

: Compute div in numpy according to ONNX spec.

: Compute elu in numpy according to ONNX spec.

: Compute equal in numpy according to ONNX spec.

: Compute erf in numpy according to ONNX spec.

: Compute exponential in numpy according to ONNX spec.

: Flatten a tensor into a 2d array.

: Compute Floor in numpy according to ONNX spec.

: Compute greater in numpy according to ONNX spec.

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

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

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

: Compute hardsigmoid in numpy according to ONNX spec.

: Compute hardswish in numpy according to ONNX spec.

: Compute identity in numpy according to ONNX spec.

: Compute leakyrelu in numpy according to ONNX spec.

: Compute less in numpy according to ONNX spec.

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

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

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

: Compute log in numpy according to ONNX spec.

: Compute matmul in numpy according to ONNX spec.

: Compute Max in numpy according to ONNX spec.

: Compute Max Pooling using Torch.

: Compute Min in numpy according to ONNX spec.

: Compute mul in numpy according to ONNX spec.

: Compute Negative in numpy according to ONNX spec.

: Compute not in numpy according to ONNX spec.

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

: Compute or in numpy according to ONNX spec.

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

: Compute pow in numpy according to ONNX spec.

: Compute relu in numpy according to ONNX spec.

: Compute round in numpy according to ONNX spec.

: Compute selu in numpy according to ONNX spec.

: Compute sigmoid in numpy according to ONNX spec.

: Compute Sign in numpy according to ONNX spec.

: Compute sin in numpy according to ONNX spec.

: Compute sinh in numpy according to ONNX spec.

: Compute softmax in numpy according to ONNX spec.

: Compute softplus in numpy according to ONNX spec.

: Compute sub in numpy according to ONNX spec.

: Compute tan in numpy according to ONNX spec.

: Compute tanh in numpy according to ONNX spec.

: Compute thresholdedrelu in numpy according to ONNX spec.

: Transpose in numpy according to ONNX spec.

: Compute the equivalent of numpy.where.

: Compute the equivalent of numpy.where.

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

: Check that the given object can properly be serialized.

: Reduce size of the given data-set.

: Return a random sublist of sklearn_models_and_datasets.

: Get train or testing data-set.

: Instantiate any Concrete ML model type.

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

: Indicate if two values are equal.

: Convert the n_bits parameter into a proper dictionary.

: Fill a parameter set structure from kwargs parameters.

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

: Return the list of available linear models in Concrete ML.

: Return the list of available models in Concrete ML.

: Return the list of available neural net models in Concrete ML.

: Return the list of available tree models in Concrete ML.

: Add transpose after last node.

: Create ONNX model with Hummingbird convert method.

: Apply post-processing from the graph.

: Apply pre-processing onto the ONNX graph.

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

: Pre-process tree values.

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

: Build a quantized module from a Torch or ONNX model.

: Compile a Brevitas Quantization Aware Training model.

: Compile a torch module into an FHE equivalent.

: Compile a torch module into an FHE equivalent.

: Convert a torch tensor or a numpy array to a numpy array.

check_inputs.check_X_y_and_assert
check_inputs.check_X_y_and_assert_multi_output
check_inputs.check_array_and_assert
dumpers.dump
dumpers.dumps
concrete.ml.common.check_inputs
concrete.ml.common.serialization
concrete.ml.common.serialization.dumpers
concrete.ml.common
deploy_to_docker.build_docker_image
deploy_to_docker.delete_image
deploy_to_docker.main
deploy_to_docker.stop_container
fhe_client_server.FHEModelClient
fhe_client_server.FHEModelDev
fhe_client_server.FHEModelServer
loaders.load
loaders.loads
concrete.ml.deployment.deploy_to_docker
concrete.ml.deployment.fhe_client_server
concrete.ml.common.serialization.loaders
utils.filter_logs
utils.is_connection_available
utils.wait_for_connection_to_be_available
linear_model.ElasticNet
linear_model.Lasso
linear_model.LinearRegression
linear_model.LogisticRegression
linear_model.Ridge
onnx_impl_utils.compute_conv_output_dims
onnx_impl_utils.compute_onnx_pool_padding
onnx_impl_utils.numpy_onnx_pad
onnx_impl_utils.onnx_avgpool_compute_norm_const
onnx_utils.execute_onnx_with_numpy
onnx_utils.get_attribute
onnx_utils.get_op_type
onnx_utils.remove_initializer_from_input
ops_impl.ONNXMixedFunction
ops_impl.RawOpOutput
ops_impl.cast_to_float
ops_impl.numpy_abs
ops_impl.numpy_acos
ops_impl.numpy_acosh
ops_impl.numpy_add
ops_impl.numpy_asin
ops_impl.numpy_asinh
ops_impl.numpy_atan
ops_impl.numpy_atanh
ops_impl.numpy_avgpool
ops_impl.numpy_batchnorm
ops_impl.numpy_cast
ops_impl.numpy_celu
ops_impl.numpy_concatenate
ops_impl.numpy_constant
ops_impl.numpy_cos
ops_impl.numpy_cosh
ops_impl.numpy_div
ops_impl.numpy_elu
ops_impl.numpy_equal
ops_impl.numpy_erf
ops_impl.numpy_exp
ops_impl.numpy_flatten
ops_impl.numpy_floor
ops_impl.numpy_greater
ops_impl.numpy_greater_float
ops_impl.numpy_greater_or_equal
ops_impl.numpy_greater_or_equal_float
ops_impl.numpy_hardsigmoid
ops_impl.numpy_hardswish
ops_impl.numpy_identity
ops_impl.numpy_leakyrelu
ops_impl.numpy_less
ops_impl.numpy_less_float
ops_impl.numpy_less_or_equal
ops_impl.numpy_less_or_equal_float
ops_impl.numpy_log
ops_impl.numpy_matmul
ops_impl.numpy_max
ops_impl.numpy_maxpool
ops_impl.numpy_min
ops_impl.numpy_mul
ops_impl.numpy_neg
ops_impl.numpy_not
ops_impl.numpy_not_float
ops_impl.numpy_or
ops_impl.numpy_or_float
ops_impl.numpy_pow
ops_impl.numpy_relu
ops_impl.numpy_round
ops_impl.numpy_selu
ops_impl.numpy_sigmoid
ops_impl.numpy_sign
ops_impl.numpy_sin
ops_impl.numpy_sinh
ops_impl.numpy_softmax
ops_impl.numpy_softplus
ops_impl.numpy_sub
ops_impl.numpy_tan
ops_impl.numpy_tanh
ops_impl.numpy_thresholdedrelu
ops_impl.numpy_transpose
ops_impl.numpy_where
ops_impl.numpy_where_body
ops_impl.onnx_func_raw_args
quantized_ops.ONNXConstantOfShape
quantized_ops.ONNXGather
quantized_ops.ONNXShape
quantized_ops.ONNXSlice
quantized_ops.QuantizedAbs
quantized_ops.QuantizedAdd
quantized_ops.QuantizedAvgPool
quantized_ops.QuantizedBatchNormalization
quantized_ops.QuantizedBrevitasQuant
quantized_ops.QuantizedCast
quantized_ops.QuantizedCelu
quantized_ops.QuantizedClip
quantized_ops.QuantizedConcat
quantized_ops.QuantizedConv
quantized_ops.QuantizedDiv
quantized_ops.QuantizedElu
quantized_ops.QuantizedErf
quantized_ops.QuantizedExp
quantized_ops.QuantizedFlatten
quantized_ops.QuantizedFloor
quantized_ops.QuantizedGemm
quantized_ops.QuantizedGreater
quantized_ops.QuantizedGreaterOrEqual
quantized_ops.QuantizedHardSigmoid
quantized_ops.QuantizedHardSwish
quantized_ops.QuantizedIdentity
quantized_ops.QuantizedLeakyRelu
quantized_ops.QuantizedLess
quantized_ops.QuantizedLessOrEqual
quantized_ops.QuantizedLog
quantized_ops.QuantizedMatMul
quantized_ops.QuantizedMax
quantized_ops.QuantizedMaxPool
quantized_ops.QuantizedMin
quantized_ops.QuantizedMul
quantized_ops.QuantizedNeg
quantized_ops.QuantizedNot
quantized_ops.QuantizedOr
quantized_ops.QuantizedPRelu
quantized_ops.QuantizedPad
quantized_ops.QuantizedPow
quantized_ops.QuantizedReduceSum
quantized_ops.QuantizedRelu
quantized_ops.QuantizedReshape
quantized_ops.QuantizedRound
quantized_ops.QuantizedSelu
quantized_ops.QuantizedSigmoid
quantized_ops.QuantizedSign
quantized_ops.QuantizedSoftplus
quantized_ops.QuantizedSqueeze
quantized_ops.QuantizedSub
quantized_ops.QuantizedTanh
quantized_ops.QuantizedTranspose
quantized_ops.QuantizedUnsqueeze
quantized_ops.QuantizedWhere
quantized_module.QuantizedModule
utils.check_serialization
utils.data_calibration_processing
utils.get_random_extract_of_sklearn_models_and_datasets
utils.get_torchvision_dataset
utils.instantiate_model_generic
utils.load_torch_model
utils.values_are_equal
quantizers.MinMaxQuantizationStats
quantizers.QuantizationOptions
quantizers.QuantizedArray
quantizers.UniformQuantizationParameters
quantizers.UniformQuantizer
quantizers.fill_from_kwargs
p_error_search.BinarySearch
p_error_search.compile_and_simulated_fhe_inference
concrete.ml.deployment.utils
concrete.ml.sklearn.linear_model
concrete.ml.onnx.onnx_impl_utils
concrete.ml.onnx.onnx_utils
concrete.ml.onnx.ops_impl
concrete.ml.quantization
concrete.ml.quantization.quantized_ops
concrete.ml.quantization.quantized_module
concrete.ml.pytest.utils
concrete.ml.quantization.quantizers
concrete.ml.search_parameters
concrete.ml.search_parameters.p_error_search
qnn.NeuralNetClassifier
qnn.NeuralNetRegressor
rf.RandomForestClassifier
rf.RandomForestRegressor
svm.LinearSVC
svm.LinearSVR
tree.DecisionTreeClassifier
tree.DecisionTreeRegressor
decoder.ConcreteDecoder
decoder.object_hook
custom_assert.assert_false
custom_assert.assert_not_reached
custom_assert.assert_true
deploy_to_aws.AWSInstance
deploy_to_aws.create_instance
deploy_to_aws.delete_security_group
deploy_to_aws.deploy_to_aws
deploy_to_aws.main
deploy_to_aws.terminate_instance
deploy_to_aws.wait_instance_termination
utils.FheMode
utils.all_values_are_floats
utils.all_values_are_integers
utils.all_values_are_of_dtype
utils.check_dtype_and_cast
utils.check_there_is_no_p_error_options_in_configuration
utils.compute_bits_precision
utils.generate_proxy_function
utils.get_model_class
utils.get_model_name
utils.get_onnx_opset_version
utils.is_brevitas_model
utils.is_classifier_or_partial_classifier
utils.is_model_class_in_a_list
utils.is_pandas_dataframe
utils.is_pandas_series
utils.is_pandas_type
utils.is_regressor_or_partial_regressor
utils.manage_parameters_for_pbs_errors
utils.replace_invalid_arg_name_chars
utils.to_tuple
numpy_module.NumpyModule
xgb.XGBClassifier
xgb.XGBRegressor
glm.GammaRegressor
glm.PoissonRegressor
glm.TweedieRegressor
encoder.ConcreteEncoder
encoder.dump_name_and_value
onnx_model_manipulations.clean_graph_after_node_op_type
onnx_model_manipulations.clean_graph_at_node_op_type
onnx_model_manipulations.keep_following_outputs_discard_others
onnx_model_manipulations.remove_identity_nodes
onnx_model_manipulations.remove_node_types
onnx_model_manipulations.remove_unused_constant_nodes
onnx_model_manipulations.simplify_onnx_model
concrete.ml.sklearn.qnn
concrete.ml.sklearn.rf
concrete.ml.sklearn.svm
concrete.ml.sklearn.tree
concrete.ml.common.debugging
concrete.ml.deployment
concrete.ml.common.serialization.decoder
concrete.ml.common.debugging.custom_assert
concrete.ml.deployment.deploy_to_aws
concrete.ml.common.utils
concrete.ml.version
concrete.ml.torch.numpy_module
concrete.ml.sklearn.xgb
concrete.ml.sklearn.glm
concrete.ml.deployment.server
concrete.ml.onnx
concrete.ml.common.serialization.encoder
concrete.ml.onnx.onnx_model_manipulations
base_quantized_op.QuantizedMixingOp
base_quantized_op.QuantizedOp
base_quantized_op.QuantizedOpUnivariateOfEncrypted
concrete.ml.quantization.base_quantized_op
torch_models.BranchingGemmModule
torch_models.BranchingModule
torch_models.CNN
torch_models.CNNGrouped
torch_models.CNNInvalid
torch_models.CNNMaxPool
torch_models.CNNOther
torch_models.ConcatFancyIndexing
torch_models.DoubleQuantQATMixNet
torch_models.FC
torch_models.FCSeq
torch_models.FCSeqAddBiasVec
torch_models.FCSmall
torch_models.MultiInputNN
torch_models.MultiInputNNConfigurable
torch_models.MultiInputNNDifferentSize
torch_models.MultiOpOnSingleInputConvNN
torch_models.NetWithConcatUnsqueeze
torch_models.NetWithConstantsFoldedBeforeOps
torch_models.NetWithLoops
torch_models.PaddingNet
torch_models.QATTestModule
torch_models.QNNFashionMNIST
torch_models.QuantCustomModel
torch_models.ShapeOperationsNet
torch_models.SimpleNet
torch_models.SimpleQAT
torch_models.SingleMixNet
torch_models.StepActivationModule
torch_models.TinyCNN
torch_models.TinyQATCNN
torch_models.TorchCustomModel
torch_models.TorchSum
torch_models.TorchSumMod
torch_models.UnivariateModule
convert.get_equivalent_numpy_forward
convert.get_equivalent_numpy_forward_and_onnx_model
qnn_module.SparseQuantNeuralNetwork
post_training.ONNXConverter
post_training.PostTrainingAffineQuantization
post_training.PostTrainingQATImporter
post_training.get_n_bits_dict
concrete.ml.pytest.torch_models
concrete.ml.pytest
concrete.ml.onnx.convert
concrete.ml.sklearn.qnn_module
concrete.ml.quantization.post_training
tree_to_numpy.add_transpose_after_last_node
tree_to_numpy.get_onnx_model
tree_to_numpy.preprocess_tree_predictions
tree_to_numpy.tree_onnx_graph_preprocessing
tree_to_numpy.tree_to_numpy
tree_to_numpy.tree_values_preprocessing
tree_to_numpy.workaround_squeeze_node_xgboost
concrete.ml.sklearn.tree_to_numpy
base.BaseClassifier
base.BaseEstimator
base.BaseTreeClassifierMixin
base.BaseTreeEstimatorMixin
base.BaseTreeRegressorMixin
base.QuantizedTorchEstimatorMixin
base.SklearnLinearClassifierMixin
base.SklearnLinearModelMixin
base.SklearnLinearRegressorMixin
sklearn.get_sklearn_linear_models
sklearn.get_sklearn_models
sklearn.get_sklearn_neural_net_models
sklearn.get_sklearn_tree_models
concrete.ml.sklearn.base
concrete.ml.torch
concrete.ml.sklearn
concrete.ml.torch.compile
compile.build_quantized_module
compile.compile_brevitas_qat_model
compile.compile_onnx_model
compile.compile_torch_model
compile.convert_torch_tensor_or_numpy_array_to_numpy_array