Supported Platforms

Environments

Platform (Minimum version)	Architecture [#arm64]_	Runtime Environment	Build Requirements
Microsoft Windows 10	x86_64	v142	Visual Studio 2019 Minimum SDK version: v10.0.18362.0
Linux	coming soon…	coming soon…	coming soon…
macOS	coming soon…	coming soon…	coming soon…

Prerequisites

Technology	(Minimum version)
Miniconda x86_64	Latest
CUDA for GPU support	11.7
Python	3.9

Microsoft Visual Studio Build and Runtime Requirements

Starting from HOOPS 2023, HOOPS Exchange migrated its compiler to Visual Studio 2019 (previously 2015). Following Microsoft’s Documentation on binary compatibility, this change implies an update to the required MSVC redistributables.

HOOPS Exchange being an explicitly shared library, the impact occurs at runtime. Any application running HOOPS Exchange such as HOOPS AI must have at least Visual Studio 2019 redistributable.

Package Description

Here is a quick description of the package :

hoops_ai
├───cadaccess
│   ├─── HOOPSLoader
│   ├─── HOOPSModel
│   ├─── HOOPSBrep
│   └─── HOOPSTools
├───cadencoder
│   └─── BrepEncoder
├───dataset
│   ├─── DatasetLoader
│   └─── DatasetExplorer
├───flowmanager
│   ├─── @flowtask Decorators
│   ├─── ParallelTask
│   ├─── SequentialTask
│   └─── ParallelExecutor
├───insights
│   ├─── DatasetViewer
│   ├─── CADViewer
│   └─── ColorPalette
├───ml
│   └───EXPERIMENTAL
│       ├─── FlowModel
│       ├─── FlowTrainer
│       └─── FlowInference
└───storage
     ├─── OptStorage
     ├─── MemoryStorage
     ├─── JsonStorageHandler
     ├─── SchemaBuilder
     └─── DatasetMerger

Module Documentation

The hoops_ai package is organized into specialized modules that work together to transform CAD files into machine learning-ready datasets. For detailed documentation on each module, see the Programming Guide:

Data Flow Management:

• CAD Data Access - CAD file loading with HOOPSLoader, HOOPSModel, HOOPSBrep, and HOOPSTools

• CAD Data Encoding - Feature extraction with BrepEncoder

• Datasets - ML-Ready Inputs - Schema definition with SchemaBuilder

• Data Storage - Data persistence with OptStorage, MemoryStorage, JsonStorageHandler, and DatasetMerger

• Data Flow Customisation - Pipeline orchestration with @flowtask decorators, ParallelTask, SequentialTask, and ParallelExecutor

Machine Learning:

• Dataset Exploration and Mining - Dataset exploration with DatasetExplorer and DatasetLoader

• Develop Your own ML Model - Model training with FlowModel, FlowTrainer, and FlowInference (EXPERIMENTAL)

• Parts Classification Model - Part classification examples

• CAD Feature Recognition Model - Feature recognition examples

Visualization:

• Data Visualization Experience - Interactive visualization with DatasetViewer, CADViewer, and ColorPalette

Next Steps

If you are already familiar with the Supported Formats page, you are now ready to discover how to Get Started with HOOPS AI.