Build or migrate Gaudi AI training models with these guides, videos and tools.
The Gaudi® platform architecture is purpose-designed for Deep Learning training workloads in data centers. It comprises a fully programmable Tensor Processing Core (TPC) cluster with supporting development tools and libraries. With Gaudi’s programmable architecture, we give the data scientist flexibility and ease of use to migrate or build models with Habana’s SynapseAI® software stack, reference models, extensive kernel libraries and documentation.
Here are resources to help Data Scientists bring up models to run on Gaudi. The assumption is the environment has been configured and ready for model training. If additional system configuration is required, refer to the Setup and Install GitHub repository to get started.
A first step guide to enable minimum changes needed to get your model running on Gaudi.
Detailed description of how to run your model on TensorFlow.
Detailed description of how to run your model on PyTorch.
Learn how to scale your system with Horovod APIs support and Gaudi NIC or Host NIC for system scaling.
Qualify the usage and integration of Gaudi hardware platforms in your server design with qualification tool (hl_qual) for Gaudi.
Detailed instructions for installation of the Habana Gaudi driver set if you need to setup your environment.
Learn how to setup a generic Kubernetes solution for an on-premise setup or as a baseline in a larger cloud configuration.
Getting started with Gaudi: Model Migration
A how-to tutorial to make minimum changes needed to get your model to run on Gaudi.
Gaudi: Training with TensorFlow
A how-to tutorial to help you run your model on TensorFlow.
Gaudi: Training with PyTorch
A how-to tutorial to help you run your model on PyTorch
This tutorial shows how to classify images of flowers. It creates an image classifier using a
keras.Sequential model, and loads data using