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At re:Invent 2023, AWS introduced a new capability within its AI stack called Amazon SageMaker HyperPod. The product was developed based on customer feedback and pain points around working with AI foundation models. AWS says the solution can reduce training time by up to 40%. The solution enables the following: |
在 re: Invent 2023 上，AWS 在其人工智能堆栈中引入了一项名为 Amazon SageMaker HyperPod 的新功能。该产品是根据客户反馈和有关使用人工智能基础模型的痛点开发的。AWS表示，该解决方案可以将培训时间减少多达40％。该解决方案支持以下功能： |
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Automatic cluster health check and repair. If an instance becomes defective during a training workload, SageMaker HyperPod automatically detects and swaps faulty nodes with healthy ones. To detect faulty hardware, SageMaker HyperPod regularly runs an array of health checks for graphics processing unit (GPU) and network integrity. |
自动集群运行状况检查和修复。如果实例在训练工作负载期间出现缺陷，SageMaker HyperPod 会自动检测故障节点并将其交换为运行状况良好的节点。为了检测硬件故障，SageMaker HyperPod定期对图形处理单元（GPU）和网络完整性进行一系列运行状况检查。 |
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Streamlined, distributed training for large training clusters. SageMaker HyperPod is preconfigured with Amazon SageMaker distributed training libraries, enabling users to automatically split their models and training data sets across AWS cluster instances to help them efficiently scale training workloads. |
大型训练集群的简化分布式训练。SageMaker HyperPod 预先配置了亚马逊 SageMaker 分布式训练库，使用户能够在 AWS 集群实例之间自动拆分模型和训练数据集，以帮助他们高效地扩展训练工作负载。 |
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Optimized utilization of clusters’ compute, memory, and network resources. Amazon SageMaker distributed training libraries optimize training jobs for AWS network infrastructure and cluster topology through two techniques: model parallelism and data parallelism. Model parallelism splits models too large to fit on a single GPU into smaller parts before distributing across multiple GPUs to train, and data parallelism splits large data sets to train concurrently and improve training speed. |
优化集群计算、内存和网络资源的利用。Amazon SageMaker 分布式训练库通过两种技术优化 AWS 网络基础设施和集群拓扑的训练作业：模型并行和数据并行。模型并行性将过大而无法安装在单个 GPU 上的模型拆分成较小的部分，然后再分布在多个 GPU 上进行训练，而数据并行性则将大型数据集拆分以进行并行训练并提高训练速度。 |
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Enterprise Strategy Group spoke with Shane Robbins, Senior Manager, Product Marketing – AI and Machine Learning about what AWS is learning from customer use of Amazon SageMaker HyperPod. “First, our customers are looking to get rid of some of the heavy lifting around model training,” said Robbins, “Compute workloads, in that sense, are huge, and the automatic detection and swap of faulty nodes the product offers means teams don’t have to dedicate resources to make it work. But in addition to that, many of our customers want more control, so they are building their own foundation models. The only path to such a thing is to have a cost-effective way to get there.” |
企业战略集团与人工智能和机器学习产品营销高级经理谢恩·罗宾斯进行了交谈，了解了AWS从客户使用亚马逊SageMaker HyperPod中学到了什么。罗宾斯说：“首先，我们的客户希望摆脱与模型训练有关的一些繁重工作，从这个意义上讲，计算工作负载是巨大的，而产品提供的故障节点的自动检测和交换意味着团队不必投入资源来使其正常运行。但除此之外，我们的许多客户还想要更多的控制权，因此他们正在建立自己的基础模型。实现这一目标的唯一途径是找到一种具有成本效益的方法来实现这一目标。” |
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Robbins mentioned customers that use HyperPod include AI foundation model makers like Perplexity and Stability AI, but he also mentioned enterprise plays like Thomson-Reuters, Hugging Face, IBM, and Salesforce. |
罗宾斯提到，使用HyperPod的客户包括Perplexity和Stability AI等人工智能基础模型制造商，但他也提到了汤森路透、Hugging Face、IBM和Salesforce等企业巨头。 |