AWS Machine Learning Blog

APRIL 22, 2024

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. In this solution, HyperPod cluster instances use the LDAPS protocol to connect to the AWS Managed Microsoft AD via an NLB.

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Clustering AWS Machine Learning Machine Learning 124

AWS Machine Learning Blog

SEPTEMBER 3, 2024

Cost optimization – The serverless nature of the integration means you only pay for the compute resources you use, rather than having to provision and maintain a persistent cluster. This same interface is also used for provisioning EMR clusters. The following diagram illustrates this solution.

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AWS Machine Learning Blog

MARCH 15, 2023

The term legacy code refers to code that was developed to be manually run on a local desktop, and is not built with cloud-ready SDKs such as the AWS SDK for Python (Boto3) or Amazon SageMaker Python SDK. The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK.

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Flipboard

AUGUST 17, 2023

If you are prompted to choose a kernel, choose Data Science as the image and Python 3 as the kernel, then choose Select. as the image and Glue Python [PySpark and Ray] as the kernel, then choose Select. Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster.

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AWS Machine Learning Blog

MAY 10, 2023

In addition to the IAM user and assumed role session scheduling the job, you also need to provide a role for the notebook job instance to assume for access to your data in Amazon Simple Storage Service (Amazon S3) or to connect to Amazon EMR clusters as needed. Prerequisites For this post, we assume a locally hosted JupyterLab environment.

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AWS Machine Learning Blog

AUGUST 22, 2023

Usually, if the dataset or model is too large to be trained on a single instance, distributed training allows for multiple instances within a cluster to be used and distribute either data or model partitions across those instances during the training process. Each account or Region has its own training instances.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. in 2012 is now widely referred to as ML’s “Cambrian Explosion.” PBAs, such as graphics processing units (GPUs), have an important role to play in both these phases. Work by Hinton et al.

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