Make the result explainable.
An experiment should retain the inputs and environment needed to interpret its outcome. Model and dataset versions, execution parameters and evaluation methods all affect what the result means.
A project record gives the investigation a durable identity. Artifacts and tests provide the evidence underneath the conclusion.
Coordinate capacity deliberately.
A cluster may support parallel experiments or distributed workloads, but its benefits depend on the model, framework and interconnect. Validate the specific execution strategy.
Learn without losing context.
Capture both the successful intervention and the conditions in which it worked. That makes a lesson useful to the next researcher without presenting it as a universal rule.