The best recommendation for an ML engineer running experiments on HPE Machine Learning Development Environment to keep more of the checkpoints is to adjust the experiment config ' s checkpoint storage settings to save more of the latest and best checkpoints. This can be done by monitoring ongoing trials in the WebUI and clicking checkpoint flags to auto-save the desired checkpoints. Additionally, the engineer should double-check that the checkpoint storage location is operating under 90% of total capacity to ensure that enough capacity is available to store the checkpoints. Finally, they can adjust the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage if desired.

Fair-share scheduling allocates resources to experiments based on the weight value of the resource pool. Increasing the weight value of a resource pool will result in more resource slots being allocated to it.

Implementing hyperparameter optimization (HPO) manually can be time-consuming and demand a great deal of expertise. HPO is not a joint ML and IT Ops effort and it can be implemented on TensorFlow models, so these are not the primary challenges faced by ML teams. Additionally, ML teams often have access to large enough data sets to make HPO feasible and worthwhile.

Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots. This is because priority scheduling is used in the HPE Machine Learning Development Environment resource pool, which means higher priority tasks will be given priority over lower priority tasks. As such, Trial 3 with priority 1 will be given priority over Trial 2 with priority 50.

Adaptive ASHA is an enhanced version of ASHA that uses a reinforcement learning approach to select hyperparameter configurations. This allows Adaptive ASHA to select higher-performing configs and clone those configurations, allowing for better performance than ASHA.