Migrating to cloud operations can be daunting, but there are so many advantages to be had when you use HPE GreenLake cloud. Let us help. Check out our articles on mastering cloud migration, converting specs, synching users, and enhancing your environment for sustainability.
If it’s parallel compute programming you’re into, our articles on Chapel’s recent 2.1 release and high-level support for CPU-GPU data transfers and features that let you define functions that perform computations on types at compile-time are sure to please. We also continue our Determined AI series on activation memory in this month’s edition. Enjoy!
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Mastering cloud migration with the 6Rs approach
Looking to achieve the agility and efficiency afforded by cloud, but having trouble migrating? Learn how to address the hurdles, like legacy systems, data security and uninterrupted operations, with HPE GreenLake for Private Cloud Enterprise.
Programmatically monitor energy consumption
Learn how to retrieve carbon emissions, energy consumption, and cost of infrastructure data from HPE GreenLake managed environments programmatically with the HPE Sustainability Insight Center API..
Community
Converting HPE GreenLake API specifications in OAS 3.1 using OpenAPI tools
Learn how to convert the HPE GreenLake API for Data Services into a PowerShell client library using open source tools and how to use it in these examples.
HPE GreenLake Flex Solutions SCIM API integration with Okta SCIM adapter
Walk through the process of configuring the Okta SCIM adapter to sync users and user groups from Okta to HPE GreenLake Flex Solutions in this post.
Announcing Chapel 2.1!
Building off this past March’s milestone 2.0 release, 2.1 significantly expands Chapel’s installation options and improves support for AWS. There’s more, too! Get the highlights here.
Chapel’s high-level support for CPU-GPU data transfer and multi-GPU programming
Explore how Chapel’s parallelism and locality features can enable using multiple GPUs and how its high-level array operations can be used to move data between GPUs and CPUs.
Generic linear multistep method evaluator using Chapel
See how you can implement an evaluator for a whole family of numerical methods, allowing you to describe a method in only a line of code, and execute it as fast as a hand-written implementation in this detailed tutorial.
Reflections on ChapelCon ’24: A community growing together
Building off of its predecessor, CHIUW, ChapelCon is a decidedly more community-oriented event. Get the big-picture highlights and ideas for next year’s event here.
Activation Memory: A deep dive using PyTorch
Building off of the first post in this series, this article discusses where activation memory comes from, how to measure it in PyTorch, and why changing the activation function can significantly reduce memory costs.
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This newsletter originally appears at https://developer.hpe.com/newsletter/aug-2024/ on August 8, 2024. HPE Developer Community monthly is reproduced here with express permission of Hewlett Packard Enterprise