We're excited to introduce the release of Intel® Gaudi® software version 1.15.0 - Habana Developer Blog - Intel Gaudi Developers
Bringing forth numerous enhancements and updates for an improved user experience.| Intel Gaudi Developers
Bringing forth numerous enhancements and updates for an improved GenAI development experience. The post We’re excited to introduce the release of Intel® Gaudi® software version 1.17.0 appeared first on Intel Gaudi Developers.| Intel Gaudi Developers
Bringing forth numerous enhancements and updates for an improved GenAI development experience. The post We’re excited to introduce the release of Intel® Gaudi® software version 1.16.0 appeared first on Intel Gaudi Developers.| Intel Gaudi Developers
Bringing forth numerous enhancements and updates for an improved user experience.| Intel Gaudi Developers
Learn how to execute scalable model development with Fully sharded data parallel (FSDP) training using PyTorch and Intel Gaudi Accelerators| Intel Gaudi Developers
With the Intel Gaudi SynapseAI 1.13.0 release, users can run Fine Tune the Llama2 70B model using only 8 Gaudi2 Accelerators.| Intel Gaudi Developers
Bringing forth numerous enhancements and updates for an improved user experience.| Intel Gaudi Developers
In the 1.10 release, we’ve upgraded versions of several libraries, including PyTorch 2.0.1, PyTorch Lightning 2.0.0 and TensorFlow 2.12.0. We have added support for EKS 1.25 and OpenShift 4.12| Intel Gaudi Developers
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.| Intel Gaudi Developers
In this article, you'll learn how to easily deploy multi-billion parameter language models on Habana Gaudi2 and get a view into the Hugging Face performance evaluation of Gaudi2 and A100 on BLOOMZ.| Intel Gaudi Developers
AWS and Habana collaborated to enable EFA Peer Direct support on the Gaudi-based AWS DL1 instances, offering users significant improvement in multi-instance model training performance.| Intel Gaudi Developers
In this article, you will learn how to use Habana® Gaudi®2 to accelerate model training and inference, and train bigger models with 🤗 Optimum Habana.| Intel Gaudi Developers
With Habana’s SynapseAI 1.8.0 release support of DeepSpeed Inference, users can run inference on large language models, including BLOOM 176B.| Habana Developers
We have upgraded versions of several libraries with SynapseAI 1.8.0, including PyTorch 1.13.1, PyTorch Lightning 1.8.6 and TensorFlow 2.11.0 & 2.8.4.| Intel Gaudi Developers
In this paper we’ll show how Transfer Learning is an efficient way to train an existing model on a new and unique dataset with equivalent accuracy and significantly less training time.| Intel Gaudi Developers
In this post, we show you how to run Habana’s DeepSpeed enabled BERT1.5B model from our Model-References repository.| Habana Developers
This tutorial provides example training scripts to demonstrate different DeepSpeed optimization technologies on HPU. This tutorial will focus on the memory optimization technologies, including Zero Redundancy Optimizer(ZeRO) and Activation Checkpointing.| Habana Developers
The SDSC Voyager supercomputer is an innovative AI system designed specifically for science and engineering research at scale.| Intel Gaudi Developers
In this post, we will learn how to run PyTorch stable diffusion inference on Habana Gaudi processor, expressly designed for the purpose of efficiently accelerating AI Deep Learning models.| Habana Developers
Sometimes we want to run the same model code using different type of AI accelerators. For example, this can be required if your development laptop has a GPU, but your training server is using Gaudi.| Habana Developers
The Habana team is happy to announce the release of SynapseAI® version 1.7.0. A live demo of Stable Diffusion was presented by Pat Gelsinger in Intel Innovation in September and there has been a lot of interest from our users since then.| Habana Developers
Fine tuning GPT2 with Hugging Face and Habana Gaudi. In this tutorial, we will demonstrate fine tuning a GPT2 model on Habana Gaudi AI processors using Hugging Face optimum-habana library with DeepSpeed.| Habana Developers
Optimize your deep learning with data parallel processes on Intel Gaudi using DeepSpeed. Enhance efficiency in training with our expert insights.| Intel Gaudi Developers
One of the main challenges in training Large Language Models (LLMs) is that they are often too large to fit on a single node or even if they fit, the training may be too slow. To address this issue, their training can be parallelized across multiple Gaudi accelerators (HPUs).| Habana Developers
If you want to train a large model using Megatron-DeepSpeed, but the model you want is not included in the implementation, you can port it to the Megatron-DeepSpeed package. Assuming your model is transformer-based, you can add your implementation easily, basing it on existing code.| Habana Developers
We have optimized additional Large Language Models on Hugging Face using the Optimum Habana library.| Intel Gaudi Developers
In this release, we’ve upgraded versions of several libraries, including DeepSpeed 0.9.4, PyTorch Lightning 2.0.4 and TensorFlow 2.12.1.| Intel Gaudi Developers
Announcing a new End-to-End use case showing Training of a semantic segmentation model for Autonomous Driving| Intel Gaudi Developers
In the 1.9 release, we’ve upgraded versions of several libraries, including PyTorch Lightning 1.9.4, DeepSpeed 0.7.7, fairseq 0.12.3, and Horovod v0.27.0.| Intel Gaudi Developers
