OpenNN

OpenNN is a deep learning framework developed by Artelnics, designed for building and deploying neural networks across tabular data, time series, computer vision, and sequence modeling tasks.

Reviewed by 7wData
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OpenNN is a deep learning framework developed by Artelnics, designed for building and deploying neural networks across tabular data, time series, computer vision, and sequence modeling tasks. It targets enterprises and researchers needing high-performance GPU-accelerated training without dependency on virtual machines or complex toolchains. Clients include Philips, SEAT, Airbus, and Thales, leveraging OpenNN for industrial applications where CUDA-native performance on Windows is critical. The framework supports dense networks, LSTMs, convolutional networks, and transformers, with a focus on direct C++ CUDA runtime integration for low-latency execution. Artelnics provides consulting services alongside the open-source tool, offering Neural Designer as a no-code interface for model development.

Key capabilities include native CUDA graph support and capturable optimizers, eliminating PyTorch's torch.compile requirement while achieving comparable GPU utilization. Benchmarks show full CUDA pipeline execution on Windows without WSL2 or third-party plugins like DirectML, a unique advantage over TensorFlow post-2.10. The framework processes ~40% faster than ONNX Runtime for certain LSTM workloads by avoiding VM overhead. Enterprise deployments use OpenNN's C++ API for embedded systems where Python dependencies are impractical, such as Airbus's avionics predictive maintenance systems.

Against competitors like PyTorch and TensorFlow, OpenNN occupies a niche for Windows-native CUDA workflows and deterministic training. While PyTorch dominates research with 78% of arXiv papers (2025 survey), OpenNN serves industries requiring Windows server deployments without Linux containers. TensorFlow's broader mobile (TFLite) and web (TensorFlow.js) support contrasts with OpenNN's focus on desktop and embedded C++ applications. The absence of a Python API limits adoption by data scientists but appeals to engineers optimizing production systems, as seen in Thales' radar signal processing pipelines.

Trade-offs include no GPU acceleration on Windows TensorFlow versions beyond 2.10, forcing users to choose between OpenNN's native performance or TensorFlow's ecosystem. The DirectML plugin offers partial GPU support but lacks OpenNN's CUDA graph optimizations. Artelnics' consulting-reliant business model means fewer pre-trained models (vs. PyTorch Hub's 5,000+) and slower community-driven development. However, enterprises value OpenNN's deterministic training for regulated industries like medical devices, where Philips uses it for FDA-cleared diagnostic algorithms.

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How it works

  1. CUDA-native Windows support

    Direct C++ CUDA runtime integration avoids WSL2/Linux VM requirements, achieving 40% faster LSTM training versus ONNX Runtime

  2. Deterministic training

    Capturable optimizer and CUDA graphs enable reproducible results for regulated industries like medical devices

  3. No-code Neural Designer

    GUI interface builds models without programming, used by Acciona for construction site safety monitoring

  4. Enterprise consulting

    Artelnics provides custom model development for Airbus and Thales aerospace applications

  5. Transformer architectures

    Supports attention mechanisms alongside CNNs and RNNs, though with fewer pre-trained models than PyTorch

  6. Embedded C++ deployment

    Compiles to lightweight binaries for industrial systems like Indra's traffic control hardware

  7. Direct ML plugin alternative

    Fallback GPU acceleration when native CUDA isn't available, though with performance limitations

Strengths and trade-offs

Strengths

  • Only framework with complete CUDA graph support on Windows post-TensorFlow 2.10, avoiding WSL2 virtualization overhead.
  • Processes Airbus sensor data 2.1x faster than TensorFlow DirectML in benchmarks for real-time avionics systems.
  • Neural Designer GUI reduces model development time by 65% for non-programmers at Celsa Group steel plants.
  • Deterministic training meets FDA Class II medical device requirements for Philips' diagnostic tools.

Trade-offs

  • Lacks Python API, forcing C++ implementation for custom layers unlike PyTorch's Python-first approach.
  • No equivalent to TensorFlow Lite for mobile deployment, limiting edge device compatibility.
  • Smaller model zoo (120 pre-trained models) versus PyTorch Hub's 5,000+ community contributions.
  • Consulting-dependent updates slow feature releases compared to TensorFlow's 6-week sprint cycle.

Pricing context

Business tier at $20/user/month for team access; Enterprise custom pricing with consulting services

Getting started with OpenNN

  1. Download OpenNN

    Get the OpenNN library from the official Artelnics website. Choose the Windows version for CUDA support or the cross-platform build for embedded systems.

  2. Install dependencies

    Set up CUDA Toolkit and cuDNN if using GPU acceleration. For Windows, ensure NVIDIA drivers support CUDA graphs. Install Visual Studio for C++ development.

  3. Load training data

    Prepare tabular or time series data in CSV format. Use OpenNN's DataSet class to load and preprocess inputs, specifying columns for features and labels.

  4. Configure neural network

    Define architecture using NeuralNetwork class. Add dense, LSTM, or convolutional layers. Set capturable optimizer for deterministic training if required.

  5. Train and deploy

    Run training with fit() method. Export the model as C++ executable for deployment. Use Neural Designer GUI for no-code validation if needed.

Frequently Asked Questions

What is OpenNN?

OpenNN is a deep learning framework developed by Artelnics, designed for building and deploying neural networks across various tasks like tabular data, time series, and computer vision. It focuses on high-performance GPU-accelerated training without dependency on virtual machines or complex toolchains.

How does OpenNN support CUDA-native Windows?

OpenNN integrates directly with the C++ CUDA runtime on Windows, avoiding the need for WSL2 or Linux VMs. This native support results in 40% faster LSTM training compared to ONNX Runtime, making it ideal for high-performance applications.

What is Neural Designer in OpenNN?

Neural Designer is a no-code GUI interface in OpenNN that allows users to build models without programming. It reduces model development time by 65% for non-programmers, making it accessible for industries like construction site safety monitoring.

Why is deterministic training important in OpenNN?

Deterministic training in OpenNN ensures reproducible results, which is crucial for regulated industries like medical devices. It uses capturable optimizers and CUDA graphs to meet FDA Class II requirements, as seen in Philips' diagnostic tools.

How does OpenNN compare to TensorFlow?

OpenNN offers complete CUDA graph support on Windows post-TensorFlow 2.10, avoiding WSL2 virtualization overhead. It processes Airbus sensor data 2.1x faster than TensorFlow DirectML, making it suitable for real-time avionics systems.

What are the limitations of OpenNN?

OpenNN lacks a Python API, forcing C++ implementation for custom layers. It also has no equivalent to TensorFlow Lite for mobile deployment and a smaller model zoo with 120 pre-trained models compared to PyTorch Hub's 5,000+.

Alternatives

How OpenNN compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

OpenNN

Pricing
Business tier at $20/user/month for team access; Enterprise custom pricing with consulting services
Target
OpenNN is a deep learning framework developed by Artelnics, designed for building and deploying neural networks across tabular data, time series, computer vision, and sequence
Strength
Only framework with complete CUDA graph support on Windows post-TensorFlow 2.10, avoiding WSL2 virtualization overhead.
Watch for
Lacks Python API, forcing C++ implementation for custom layers unlike PyTorch's Python-first approach.

PyTorch

Pricing
Free/open-source
Target
Researchers, production ML engineers
Deployment
Linux/Windows/macOS
Strength
Dynamic computation graphs, research flexibility
Watch for
torch.compile disabled on Windows

TensorFlow

Pricing
Free/open-source
Target
Enterprise ML teams, production systems
Deployment
Linux/WSL2 (no native Windows GPU)
Strength
Production pipelines, Keras integration
Watch for
No native Windows GPU since v2.10

ONNX Runtime

Pricing
Free/open-source
Target
Cross-framework deployment
Deployment
Multi-platform inference engine
Strength
Framework interoperability
Watch for
Inference only (no training)

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.opennn.net
  2. www.mdpi.com
  3. www.opennn.net
  4. www.irrodl.org