PyTorch

Learn about our approach to open source, including where we focus our contributions in projects and communities, how we push innovation upstream, and what you should consider when embracing open source in your enterprise.

Learn about the basics of PyTorch and it's support for the training, evaluation, and inferencing phases of the machine learning workflow.

Create a specialised LLM using the Low-Rank Adaption (LoRA) transformer technique and Optuna library for a question answering task

Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use.

A Python package that provides tensor computation and deep neural networks.

Your guide to the different presentations from IBMers and co-presenters at the main conference and co-located events at Open Source Summit North America 2024

Create a Ray cluster using the CodeFlare SDK and interact with Ray within the CodeFlare Stack to run distributed ML workloads

Learn the fundamentals of how you can build neural networks without the help of the frameworks that might make it easier to use.

Six real-world scenarios that demonstrate the ease of use and benefits of using Power10 MMA for AI inferencing

Learn about Distributed AI APIs, a set of RESTful web services with data and AI algorithms to support AI applications across hybrid cloud, distributed, and edge environments.