Neuralhub vs. Ray vs. Strong Analytics vs. VisionPro Deep Learning Comparison Chart

Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.

VisionPro Deep Learning is the best-in-class deep learning-based image analysis software designed for factory automation. Its field-tested algorithms are optimized specifically for machine vision, with a graphical user interface that simplifies neural network training without compromising performance. VisionPro Deep Learning solves complex applications that are too challenging for traditional machine vision alone, while providing a consistency and speed that aren’t possible with human inspection. When combined with VisionPro’s rule-based vision libraries, automation engineers can easily choose the best the tool for the task at hand. VisionPro Deep Learning combines a comprehensive machine vision tool library with advanced deep learning tools inside a common development and deployment framework. It simplifies the development of highly variable vision applications.