HPE Performance Cluster Manager provides bare-metal provisioning, image management, software updates, monitoring, and cluster health management for large HPC clusters. Scheduling is handled by tools such as Slurm, not HPCM.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a00044858enw.html

HPE AI Essentials includes Kubeflow, and Kubeflow includes Katib for hyperparameter tuning experiments. The other options incorrectly map HPO to MLflow, Ray Tune exposure, or Spark/Livy.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00ecp55hen_us&docLocale=en_US&p age=reference%2Fkubernetes%2Fusing-kubernetes%2Fgeneral-functionality%2Ftutorials%2Fkubeflow%2FTutorial_Katib_Hyperparameter_Tuning.html
https://www.hpe.com/us/en/collaterals/collateral.a50014263enw.html

Disaggregated shared storage lets capacity and performance scale independently of compute nodes and supports non-disruptive expansion and data services shared across hosts. Direct- attached NVMe can offer lower latency for a single host but ties capacity to that host.

AI pilot planning should start by mapping data locations, dependencies, access patterns, and latency/compliance constraints, then placing AI workloads close to the relevant data. Data movement, conversion, or insecure centralization are not appropriate first steps.

The HPE Private Cloud AI Developer System is the entry configuration intended for early development, experimentation, and prototyping before moving to small, medium, or large production-oriented configurations.