In training workloads, there may occur some scenarios in which graph re-compilations occur. This can create system latency and slow down the overall training process with multiple iterations of graph compilation. This blog focuses on detecting these graph re-compilations.
debugging, developer, Gaudi, performance, pytorch
In this tutorial we will learn how to write code that automatically detects what type of AI accelerator is installed on the machine (Gaudi, GPU or CPU), and make the needed changes to run the code smoothly.
We have upgrade versions of several libraries with SynapseAI 1.7.0, including DeepSpeed 0.7.0, PyTorch Lightning 1.7.7, TensorFlow 2.10.0 & 2.8.3, horovod 0.25.0, libfabric 1.16.1, EKS 1.23, and Open Shift 4.11.
In this tutorial, we will demonstrate fine tuning a GPT2 model on Habana Gaudi AI processors using Hugging Face optimum-habana library with DeepSpeed.
DeepSpeed, developer, Fine Tuning, Gaudi, GPT, GPT2, Hugging Face
Gaudi and Gaudi2-based Servers Deliver Flexibility with Industry-standard Interoperability State-of-the-art deep learning applications require multiple ...
One of the key challenges in Large Language Model (LLM) training is reducing the memory requirements needed for training without sacrificing compute/communication efficiency and model accuracy.
DeepSpeed, developer, Gaudi, Large Language Models
Habana Collaborates with Red Hat to Make AI/Deep Learning More Accessible to Enterprise Customers through OpenShift Data Science
AI is transforming enterprises with valuable business insights, increased operational efficiencies and enhanced user experiences ...
SynapseAI 1.5 brings many improvements, both in usability and in Habana ecosystem support. For PyTorch ...