Tracking runs on the How to Train Models track. Each run stores code version, data version, hyperparameters, curves, and the checkpoint path. If it is not stored, it did not happen.
This lesson assumes you already worked through Searching hyperparameters.
The idea in practice
Use a tracker or a disciplined table. Name the run before it starts.
A concrete check
goal = {
'track': 'How to Train Models',
'lesson': 'Tracking runs',
}
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 folder of untitled notebooks. 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
Write the column headers for your run table.
Self-check
- Say Tracking runs 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.