Artifacts you must save on the How to Train Models track. Save weights, preprocessor, label map, config, and metrics. A weight file alone cannot be served if the vocabulary lived in a notebook cell.
This lesson assumes you already worked through Selecting the model to keep.
The idea in practice
Put them in one directory with a manifest. Load them in a fresh process as a test.
A concrete check
goal = {
'track': 'How to Train Models',
'lesson': 'Artifacts you must save',
}
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
Saving state_dict without the class definition or the feature list. 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
List the manifest fields for a reload test.
Self-check
- Say Artifacts you must save 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.