Mixed precision on the How to Train Models track. Mixed precision uses smaller floats for speed and memory, with care so updates do not underflow. It is an optimization, not an accuracy trick.
This lesson assumes you already worked through Vanishing and exploding gradients.
The idea in practice
Use the framework's autocast and loss scaling. Confirm the validation metric did not drop.
A concrete check
goal = {
'track': 'How to Train Models',
'lesson': 'Mixed precision',
}
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
Casting everything to a tiny float and ignoring NaNs. 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
State why you would turn mixed precision on, in one sentence.
Self-check
- Say Mixed precision 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.