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.

RocketML Deep Neural Networks (rmldnn)RocketML Inc

RocketML Deep Neural Networks (rmldnn)

RocketML Inc

RocketML Deep Neural Networks (rmldnn)

RocketML Inc

RocketML Deep Neural Networks (rmldnn)
RocketML Inc