aiverify-test-engine 2.2.1

AI Verify Test Engine provides core interfaces, converters, data, model and plugin managers to facilitate the development of tests for AI systems. It is used as a base library for all AI Verify official stock-plugins and can be used to develop custom plugins.

AI Verify Test Engine

Description

AI Verify Test Engine provides core interfaces, converters, data, model and plugin managers to facilitate the development of tests for AI systems. It is used as a base library for all AI Verify official stock-plugins and can be used to develop custom plugins.

Installation

Install aiverify-test-engine via pip. The following table list the available install options and the optional dependencies along with the additional functionality that is supported.

Installation Command	Description
pip install aiverify-test-engine	Installs only the core functionalites. Supports tabular data formats like CSV, as well as Pandas pickle and Joblib files, and Scikit-learn models.
pip install aiverify-test-engine[dev]	Includes additional dependencies for development. Intended for developers who want to contribute to the project.
pip install aiverify-test-engine[tensorflow]	Installs optional Tensorflow and Keras dependencies.
pip install aiverify-test-engine[pytorch]	Installs optional PyTorch dependencies.
pip install aiverify-test-engine[gbm]	Installs XGBoost and LightGBM packages. Supports serializing models in these formats.
pip install aiverify-test-engine[all]	Installs the core package along with all additional non development dependencies.

Developer Guide

Local Installation

To contribute changes to the test engine code, clone the repository, navigate to the aiverify-test-engine folder, and install the dev version of the library:

pip install '.[dev]'

Here's an overview of the project folder structure and a brief description of each:

aiverify-test-engine/
├── aiverify_test_engine/   # Core library code
│   ├── interfaces/         # Core interfaces (algorithm, converter, data, model, pipeline, serializer, plugin)
│   ├── io/                 # Data and model IO related logic
│   ├── plugins/            # Manage the loading and execution of algorithm, data, model, pipeline and plugins
│   ├── utils/              # Utility functions and validators
├── tests/                  # Test cases
├── pyproject.toml          # Project configuration file

Running Tests

python -m pytest tests

If Tensorflow has trouble with GPU, use following environment variable to force Tensorflow to use CPU:

CUDA_VISIBLE_DEVICES="" TF_CPP_MIN_LOG_LEVEL=2 TF_NUM_INTEROP_THREADS=1 TF_NUM_INTRAOP_THREADS=1 XLA_FLAGS="--xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found" python -m pytest tests

Building the Package

hatch build

License

• Licensed under Apache Software License 2.0

Developers:

• AI Verify