Automatic scaling and separation of a workflow
To address scalability issues related to large and frequent file uploads, as well as high traffic on data set related pages like the summary and details pages, workflow operations within the data set are now executed in a dedicatedHorizontalPodAutoscaler(HPA) scalable pod. This setup allows the pod to automatically scale up or down based on the varying resource demands of the workflow.
By separating the workflow operations into its own scalable pod managed by HPA, the system can efficiently handle spikes in resource requirements caused by file uploads and high traffic on data set pages. This approach ensures optimal performance and responsiveness, effectively mitigating scalability challenges associated with these operations.
Edge device manager
Multiple data retention policies
Administrators now have the capability to create and manage multiple data retention policies for determining when data is automatically purged from inspection data sets. Users can select from a list of predefined policies and apply them to specific inspection types or data sets.
This enhancement enables administrators to tailor data retention rules based on varying compliance requirements or operational needs. Users benefit from the flexibility to choose and apply the most appropriate data retention policy to their inspection data sets, ensuring efficient management of data lifecycle and compliance with organizational guidelines.
Support for NVIDIA Ada and Hopper GPUs
Support for NVIDIA Ada and Hopper GPUs is now available for both training and inference tasks. To utilize these GPUs effectively, ensure your system has an NVIDIA driver capable of supporting CUDA 11.8 or higher.
Support for the Java SE Development Kit 17
Maximo Visual Inspectionin Maximo Application Suiteversion 9.0 now runs on JavaSE Development Kit 17.
TheJavaSE Development Kit 17 runs on the IBMSemeru Runtime Certified Edition Version 17 for WebSphere Liberty.
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