Did canceling the agent stop the GPU job?

The ML operations agent halted a training run when instructed to do so. However, this action did not guarantee that the job submitted to the external GPU scheduler would also be stopped. After the operator requested the cancellation, the agent stopped its orchestration process, indicating that the training run was canceled. Despite the agent's immediate response, the training job proceeded to consume reserved compute resources once the scheduler accepted the request.

The cancellation outcome was merely a final status from the external scheduler. When the agent conveys "canceled" before receiving confirmation from the scheduler, it presents an inaccurate result to the operator. This inconsistency occurs because the early cancellation response does not match the actual state of the job, which the scheduler confirms at a later time.

Consequently, the agent may provide the operator with a false sense of security, leading to unexpected compute consumption by a job that is no longer intended to run.

To resolve this issue, the agent should maintain a pending cancellation status until it receives confirmation from the scheduler regarding the submitted job's final state. If the scheduler proceeds with the job after the operator's cancellation instruction, the agent must capture the job identifier, request cancellation through the scheduler, verify the resulting state, and report any compute that has already been utilized.

The orchestration run can be immediately halted; however, the external job must still be accounted for, ensuring accurate reporting and preventing resource waste.