Historical Cluster Usage Data¶
Our goal is to give users insights on how their HPE Machine Learning Development Environment cluster is used. Historical cluster usage is measured in the number of compute hours allocated by HPE Machine Learning Development Environment. Note that this is not based on resource utilization, so if a user gets 1 GPU allocated but only utilizes 20% of the GPU, we would still report one GPU hour.
The total used compute hours reported by HPE Machine Learning Development Environment may be less than the hours reported by the cloud because we do not include the time that the slots are idle (e.g., time waiting for a GPU to spin up, or when a GPU is not scheduled with any jobs) in that.
Our data is aggregated by HPE Machine Learning Development Environment metadata (e.g., label, user). This aggregation is performed nightly, so any data visualized on the web UI or downloaded via the endpoint is fresh as of the last night. It will not reflect changes to the metadata of a previously run experiment (e.g., labels) until the next nightly aggregation.
We build WebUI visualizations for a quick snapshot of the historical cluster usage:
Alternatively, you can use the CLI or the API endpoints to download the resource allocation data for analysis:
det resources raw <start time> <end time>: get raw allocation information, where the times are full times in the format yyyy-mm-ddThh:mm:ssZ.
det resources aggregated <start date> <end date>: get aggregated allocation information, where the dates are in the format yyyy-mm-dd.