Supported Platforms

Environments

Platform	Minimum version	Architectures	Build Requirements
Microsoft Windows	10	x86_64	Miniconda - CUDA Python
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

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 api_ref/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.