Unveiling Major Performance Improvements and New Features
We are excited to announce the latest release of Deep Netts, bringing a series of significant upgrades and new features designed to enhance performance, simplify model development, and improve overall user experience.
Performance Boosts: Over 50% Improvement for Java Models on CPU
In this release, we have achieved substantial performance gains across our platform. Based on our internal benchmarks, pure Java models now run more than 50% faster on CPU for both inference and training tasks, when compared to previous versions of Deep Netts. This optimization makes it easier to develop and deploy high-performance models in Java environments without the need for specialized hardware.
GPU Support: Up to 1000x Faster Inference
The introduction of GPU support is a game-changer for deep learning applications, especially in areas like computer vision. Running large convolutional networks such as VGG Net on GPU provides up to 1000x faster inference compared to the traditional CPU-based approach.
Our benchmarks show that inference time for a single image has been slashed to just 19 milliseconds on a GPU, compared to 13,000 milliseconds on a 5-core CPU running version 3.1.1, or 19,000 milliseconds on the same CPU using version 3.0.0. This remarkable speedup is made possible by integrating JCuda for NVIDIA GPUs, supporting CUDA version 11.2 and higher.
In addition, the release continues to support TensorFlow-based models, allowing you to train models in TensorFlow and then seamlessly import them into Deep Netts for Java-based deployment.
Algorithmic Improvements for Faster and More Accurate Training
The latest release also brings improved algorithms that enhance model accuracy while reducing training time. These upgrades result in faster training sessions, both in terms of time and the number of epochs required to reach convergence, making it easier to fine-tune models and deploy them more quickly.
AutoML: Simplifying Hyperparameter Tuning
To further simplify the development process, we are introducing AutoML features for automatic hyperparameter search. With this tool, you can effortlessly determine the best architecture for your specific problem, compare different models, and optimize your workflows. The intuitive interface makes it accessible even to those with minimal experience in machine learning.
New Visualization and Debugging Tools
We’ve enhanced the debugging experience with new visualization tools that allow you to analyze weight statistics and explore network architectures in 3D space using JavaFX. These tools provide deeper insights into model behavior, helping you diagnose issues and optimize performance more effectively.
This release is packed with features that empower developers to create, train, and deploy high-performance machine learning models with ease. We are excited to see how these new capabilities will help accelerate your AI projects! Stay tuned for more updates and enhancements.
Ready to experience the power of these new features? Download the latest version of Deep Netts now and take your machine learning models to the next level with improved performance, GPU support, AutoML, and powerful visualization tools.
