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Speed up your cluster procurement time with Amazon SageMaker HyperPod training plans

AWS Machine Learning Blog

However, customizing these larger models requires access to the latest and accelerated compute resources. In this post, we demonstrate how you can address this requirement by using Amazon SageMaker HyperPod training plans , which can bring down your training cluster procurement wait time. For Target , select HyperPod cluster.

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Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

AWS Machine Learning Blog

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. We use the purpose-built geospatial container with SageMaker Processing jobs for a simplified, managed experience to create and run a cluster.

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Reduce ML training costs with Amazon SageMaker HyperPod

AWS Machine Learning Blog

As cluster sizes grow, the likelihood of failure increases due to the number of hardware components involved. Larger clusters, more failures, smaller MTBF As cluster size increases, the entropy of the system increases, resulting in a lower MTBF. It implies that if a single instance fails, it stops the entire job.

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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning Blog

The process of setting up and configuring a distributed training environment can be complex, requiring expertise in server management, cluster configuration, networking and distributed computing. Its mounted at /fsx on the head and compute nodes. Scheduler : SLURM is used as the job scheduler for the cluster.

AWS 109
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How climate tech startups are building foundation models with Amazon SageMaker HyperPod

Flipboard

SageMaker HyperPod is a purpose-built infrastructure service that automates the management of large-scale AI training clusters so developers can efficiently build and train complex models such as large language models (LLMs) by automatically handling cluster provisioning, monitoring, and fault tolerance across thousands of GPUs.

AWS 129
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Customize DeepSeek-R1 distilled models using Amazon SageMaker HyperPod recipes – Part 1

AWS Machine Learning Blog

The launcher interfaces with underlying cluster management systems such as SageMaker HyperPod (Slurm or Kubernetes) or training jobs, which handle resource allocation and scheduling. Alternatively, you can use a launcher script, which is a bash script that is preconfigured to run the chosen training or fine-tuning job on your cluster.

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Boost your forecast accuracy with time series clustering

AWS Machine Learning Blog

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. We use the Time Series Clustering using TSFresh + KMeans notebook, which is available on our GitHub repo.