Supported Platforms

Environments

Platform	Architecture	Runtime Requirements	Supported OS Examples
Microsoft Windows	x86_64	v142	Windows 11 Windows Server 2022+
GNU/Linux	x86_64	glibc 2.35 libstdc++.so.6.0.28	Ubuntu 22.04 LTS Debian 12+
GNU/Linux	arm64-v8a	glibc 2.35 libstdc++.so.6.0.28	Ubuntu 22.04 LTS (aarch64) NVIDIA Jetson (GPU supported)
Apple macOS	Coming soon

Prerequisites

All versions:

Technology	Minimum version
Python	3.12

GPU version additional requirements:

Technology	Minimum version
NVIDIA GPU	(compute capability 3.5+)
NVIDIA driver	575.51 or newer (supports CUDA 13.0)

Note

PyTorch GPU wheels bundle the CUDA runtime. You do not need to install the CUDA toolkit separately. The NVIDIA driver is the only hard requirement.

PyTorch 2.9 with CUDA 13.0 supports all current NVIDIA GPU architectures, including Ada Lovelace (RTX 4000 series) and Blackwell (RTX 5000 series).

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.