Welcome to Habana’s developer site.
Here you will find the content, guidance, tools and support needed to easily and flexibly build new or migrate existing AI models and optimize their performance to meet your AI requirements. You can also access the latest Gaudi software to build or update your infrastructure.
Get Started
Get access to Habana’s programmable Tensor Processor Core and SynapseAI® software stack with support for TensorFlow and PyTorch frameworks, along with our model garden, libraries, containers and tools that enable you to build popular AI models. Now supporting the new Gaudi®2 Processor!
Sign up for the latest Habana developer news, events, training, and updates.
How to Videos
Just getting started with Gaudi? Check out these “how to” videos for guidance and tips.
Gaudi2 on 176 Billion Parameter BLOOMZ
BLOOMZ on Intel Developer Cloud
Gaudi: Training with PyTorch
A how-to tutorial to help you run your model on PyTorch.
Latest Webinar Recordings
View our latest webinar recordings on demand
Deep Learning and Inference on Gaudi
In this live Webinar you will participate in a virtual activity where to see the Habana Gaudi AI processor running on the AWS DL1 instance.
Deep Learning on Gaudi
In this live Workshop you will participate in a hands-on virtual activity where you will get access to the Habana Gaudi AI processor running on the AWS DL1 instance.
Recent Posts and Tutorials
Check out these latest posts from our blog.
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We’re excited to participate in this year’s ISC High Performance Compute 2023 event in Hamburg Germany. This year our team will demonstrate the capabilities of our Habana Gaudi2® processors, which deliver high-performance, high-efficiency deep learning training and inference.
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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.
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Announcing a new End-to-End use case showing Training of a semantic segmentation model for Autonomous Driving