Scheduling GPUs on the Build Your Own AI Platform track. GPUs are scarce. The scheduler queues jobs, enforces fairness, and preempts or waits. Interactive notebooks should not silently block production training.
This lesson assumes you already worked through Cost controls.
The idea in practice
Separate queues for experiments and production retrains. Set priorities.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Scheduling GPUs',
}
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
One shared box where the loudest notebook wins. 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
Define two queues and which one may be preempted.
Self-check
- Say Scheduling GPUs 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.