Physical CloudBender™ regions now support running a centralized storage controller similar to cloud regions.

Integration with Azure Blob Storage and Azure Container Registry is now available natively in trainML.

CloudBender™ now allows you deploy applications as endpoints to your local region, so they are only accessible from inside your infrastructure.

Customers can now disable the automatic archiving of job outputs prior to upload.

Wasabi cloud storage has been added as an available storage integration. Wasabi can save you up to 80% on persistent storage compared to AWS and has no additional egress/API fees, making it a great option for trainML integration.

trainML Models can now be copied between projects.

Save even more money with CPU-Only job instances.

trainML now provides unparalleled job flexibility by allowing jobs to specify multiple GPU types that can satisfy a job request. The trainML job scheduler will automatically select the most affordable GPU type available.

CloudBender™ now allows you directly connect to regional datastores without having to copy that data into or out of trainML persistent storage.

New job environments based on Python 3.9 are now available for all frameworks.