Using a GPU without fooling yourself on the How to Train Models track. A GPU speeds matrix math. It does not fix leakage. Move the model and batch to the same device. Keep evaluation deterministic enough to compare runs.
This lesson assumes you already worked through Mixed precision.
The idea in practice
Check that tensors are on CUDA or MPS when you think they are. One CPU tensor can silently slow everything or error.
A concrete check
goal = {
'track': 'How to Train Models',
'lesson': 'Using a GPU without fooling yourself',
}
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
Timing a run that still computed on CPU and crediting the GPU. 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
Name the check that proves the batch and the model share a device.
Self-check
- Say Using a GPU without fooling yourself 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.