rmldnn was built from the start with two main design principles in mind:

- Ease-of-use: Simple, code-free, configuration-based command-line interface

- Uncompromising performance: Blazing-fast speeds and HPC-grade scalability on GPU/CPU clusters

Benefits:

- Does not require any knowledge of deep-learning frameworks (Pytorch, Keras, etc) or Python.

- Is designed with scalability & performance in mind from the get-go.

- Is agnostic to machine and processor architectures, running seamlessly on both CPU and GPU systems.

- Runs on anything from one to hundreds of GPUs or CPUs without any required knowledge of distributed computing, automatically managing data partitioning and communication among nodes.

- Allows building models for different computer vision use cases like image classification, object detection, image segmentation, autoencoders, and generative networks.

Audience:

- Researchers who are solving image classification, object detection, or image segmentation problems in their respective fields using deep learning.

- Data scientists with experience in scikit-learn and venturing into deep learning.

- Data scientists who need to scale their single-process deep-learning solutions to multiple GPUs or CPUs.

- Data scientists who want to train deep-learning models without writing boilerplate code in Python/Pytorch/Tensorflow.

- Newcomers to the field of machine learning who want to experiment with deep learning without writing any code.