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At its re : Invent conference today , Amazon ’s AWS cloud arm announced the launching ofSageMaker HyperPod , a new purpose - work up service for preparation and very well - tuning great speech models ( LLMs ) . SageMaker HyperPod is now by and large usable .
Amazon has long bet onSageMaker , its service for building , training and deploying machine learning role model , as the lynchpin of its motorcar encyclopaedism scheme . Now , with the advent of reproductive AI , it ’s perhaps no surprisal that it is also lean on SageMaker as the core product to make it soft for its users to train and o.k. - tune LLMs .
“ SageMaker HyperPod gives you the ability to create a allot bunch with accelerated representative that ’s optimize for distributed training , ” Ankur Mehrotra , AWS ’ cosmopolitan manager for SageMaker , severalise me in an interview ahead of today ’s announcement . “ It gives you the creature to efficiently disperse models and data across your cluster — and that bucket along up your grooming process . ”
He also remark that SageMaker HyperPod permit users to frequently write checkpoints , allow them to pause , analyze and optimize the training appendage without ingest to start over . The service also includes a number of fail - safes so that when a GPUs depart down for some reason , the full breeding unconscious process does n’t miscarry , too .
“ For an ML squad , for instance , that ’s just interested in training the model — for them , it becomes like a zero - signature experience and the cluster becomes sort of a self - healing cluster in some sense , ” Mehrotra explicate . “ Overall , these capabilities can help you train founding models up to 40 % faster , which , if you cogitate about the price and the time to market , is a huge differentiator . ”
user can opt to prepare on Amazon ’s own custom Trainium ( and now Trainium 2 ) chips or Nvidia - based GPU instance , including those using the H100 processor . The company foretell that HyperPod can speed up the training process by up to 40 % .
The company already has some experience with this using SageMaker for build Master of Laws . The Falcon 180B theoretical account , for example , wastrained on SageMaker , using a clustering of thousands of A100 GPUs . Mehrotra remark that AWS was capable to take what it acquire from that and its previous experience with scale SageMaker to build HyperPod .
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Perplexity AI ’s conscientious objector - founder and CEO Aravind Srinivas narrate me that his company receive former access to the service during its individual beta . He note that his team was initially skeptical about using AWS for preparation and fine - tune its models .
“ We did not solve with AWS before , ” he said . “ There was a myth — it ’s a myth , it ’s not a fact — that AWS does not have enceinte substructure for large model grooming and obviously we did n’t have time to do due diligence , so we believe it . ” The team got connect with AWS , though , and the engineers there asked them to test the service out ( for destitute ) . He also observe that he has find it sluttish to get support from AWS — and access to enough GPUs for Perplexity ’s use case . It obviously help that the team was already familiar with doing illation on AWS .
Srinivas also stressed that the AWS HyperPod team focused strongly on accelerate up the interconnects that link Nvidia ’s graphics placard . “ They went and optimize the primitives — Nvidia ’s various primitive — that allow you to transmit these gradient and parameters across different nodes , ” he explained .
