The cost of a run on the How to Train Models track. Compute, labeling, and your time are the cost. Log GPU hours. Kill runs that cannot beat the baseline. A cheaper model that is good enough is a result.
This lesson assumes you already worked through Retraining later.
The idea in practice
Estimate cost before a large search. Set a stop on spend.
A concrete check
goal = {
'track': 'How to Train Models',
'lesson': 'The cost of a run',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
A week of sweeps with no baseline on the books. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Estimate whether a linear model is worth trying before a rented GPU.
Self-check
- Say The cost of a run in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.