Concrete ML
WebsiteLibrariesProducts & ServicesDevelopersSupport
0.5
0.5
  • What is Concrete ML?
  • Getting Started
    • Installation
    • Key Concepts
    • Inference in the Cloud
  • 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
  • Advanced topics
    • Quantization
    • Pruning
    • Compilation
    • Production Deployment
    • Advanced Features
  • 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
      • concrete.ml.common.check_inputs
      • concrete.ml.common.debugging
      • concrete.ml.common.debugging.custom_assert
      • concrete.ml.common.utils
      • concrete.ml.deployment
      • concrete.ml.deployment.fhe_client_server
      • concrete.ml.onnx
      • concrete.ml.onnx.convert
      • concrete.ml.onnx.onnx_model_manipulations
      • concrete.ml.onnx.onnx_utils
      • concrete.ml.onnx.ops_impl
      • concrete.ml.quantization
      • concrete.ml.quantization.base_quantized_op
      • concrete.ml.quantization.post_training
      • concrete.ml.quantization.quantized_module
      • concrete.ml.quantization.quantized_ops
      • concrete.ml.quantization.quantizers
      • concrete.ml.sklearn
      • concrete.ml.sklearn.base
      • concrete.ml.sklearn.glm
      • concrete.ml.sklearn.linear_model
      • concrete.ml.sklearn.protocols
      • concrete.ml.sklearn.qnn
      • concrete.ml.sklearn.rf
      • concrete.ml.sklearn.svm
      • concrete.ml.sklearn.torch_module
      • concrete.ml.sklearn.tree
      • concrete.ml.sklearn.tree_to_numpy
      • concrete.ml.sklearn.xgb
      • concrete.ml.torch
      • concrete.ml.torch.compile
      • concrete.ml.torch.numpy_module
      • concrete.ml.version
Powered by GitBook

Libraries

  • TFHE-rs
  • Concrete
  • Concrete ML
  • fhEVM

Developers

  • Blog
  • Documentation
  • Github
  • FHE resources

Company

  • About
  • Introduction to FHE
  • Media
  • Careers
On this page
  • module concrete.ml.common.utils
  • Global Variables
  • function replace_invalid_arg_name_chars
  • function generate_proxy_function
  • function get_onnx_opset_version

Was this helpful?

Export as PDF
  1. Developer Guide
  2. API

concrete.ml.common.utils

Previousconcrete.ml.common.debugging.custom_assertNextconcrete.ml.deployment

Last updated 2 years ago

Was this helpful?

module concrete.ml.common.utils

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

Global Variables

  • DEFAULT_P_ERROR_PBS


function replace_invalid_arg_name_chars

replace_invalid_arg_name_chars(arg_name: str) → str

Sanitize arg_name, replacing invalid chars by _.

This does not check that the starting character of arg_name is valid.

Args:

  • arg_name (str): the arg name to sanitize.

Returns:

  • str: the sanitized arg name, with only chars in _VALID_ARG_CHARS.


function generate_proxy_function

generate_proxy_function(
    function_to_proxy: Callable,
    desired_functions_arg_names: Iterable[str]
) → Tuple[Callable, Dict[str, str]]

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

This returns a runtime compiled function with the sanitized argument names passed in desired_functions_arg_names as the arguments to the function.

Args:

  • function_to_proxy (Callable): the function defined like def f(*args) for which to return a function like f_proxy(arg_1, arg_2) for any number of arguments.

  • desired_functions_arg_names (Iterable[str]): the argument names to use, these names are sanitized and the mapping between the original argument name to the sanitized one is returned in a dictionary. Only the sanitized names will work for a call to the proxy function.

Returns:

  • Tuple[Callable, Dict[str, str]]: the proxy function and the mapping of the original arg name to the new and sanitized arg names.


function get_onnx_opset_version

get_onnx_opset_version(onnx_model: ModelProto) → int

Return the ONNX opset_version.

Args:

  • onnx_model (onnx.ModelProto): the model.

Returns:

  • int: the version of the model