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Not necessarily. Canceling an agent does not, by itself, prove that a GPU job it started has stopped. Check the job’s status in the GPU provider or scheduler; if it is still active, use that system’s stop, terminate, or cancel control and verify the job reaches a terminal state.
Why canceling the agent may not stop its GPU job
An agent, an orchestration workflow, and a GPU job can have separate lifecycles. Canceling one layer may change its own status without stopping work already handed to another system.
For example, AWS DevOps Agent documents that a running invocation can be canceled and marked Canceled, while work completed before cancellation is preserved and tool calls already in progress may still complete. That describes AWS DevOps Agent invocations; it does not establish that every GPU task continues. AWS DevOps Agent: Cancel an invocation
A similar separation appears in AWS Step Functions’ Bedrock AgentCore integration: stopping an execution or Task state does not stop the harness from continuing to run. AWS Step Functions: Bedrock AgentCore integration
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Check the agent and GPU job separately
- Record the identifiers. Note the agent or task ID, GPU job ID, provider, region, scheduler, and approximate cancellation time.
- Check the agent’s invocation history. Confirm its cancellation status and whether a tool call was still active when cancellation was requested. A canceled status alone does not confirm that separate work ended.
- Query the GPU provider or scheduler. Look up the job itself and inspect its current state. If there is an orchestration workflow between the agent and GPU, inspect that state as well.
- Stop the job if it is still active. Choose the provider’s operation that matches the job’s current state; queued-job cancellation and active-job termination may be different operations.
- Verify the result. Confirm the job reaches a terminal state. If continued compute charges are a concern, check resource use or billing records too.
What AWS cancellation behavior shows
Workflow cancellation can be best effort
For a Step Functions task using the synchronous “Run a Job” pattern, Step Functions makes a best-effort attempt to cancel the job. The role must have the required Cancel, Stop, Terminate, or Delete permissions. AWS warns that without the necessary permissions, the task may continue running and accrue additional charges. AWS Step Functions: Run a Job synchronously
AWS Batch distinguishes queued jobs from running jobs
If—and only if—the GPU job is managed by AWS Batch, the job’s state determines which operation applies. The AWS SDK reference says CancelJob can cancel jobs in SUBMITTED, PENDING, or RUNNABLE, but does not cancel jobs in STARTING or RUNNING; those need to be terminated. AWS Batch API: CancelJob
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Stopping an orchestration task may leave its runtime alive
AWS’s AgentCore integration documentation explicitly says that stopping the Step Functions execution or Task state does not stop the harness. This is why it is important to inspect the runtime or scheduler that owns the GPU job rather than infer its state from an upstream task’s status. AWS Step Functions: Bedrock AgentCore integration
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Do not infer external GPU status from a coding-agent control
OpenAI’s Codex Cloud documentation says cloud tasks can continue while a user’s computer is asleep and run in isolated workspaces. Its CLI help says Ctrl-C cancels the current step. Neither page establishes whether canceling a Codex task terminates a separate GPU job managed by an external provider, so check that provider directly if one is involved.
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- System Compatibility Note: 2-slot card, 271x112x39mm, single 8-pin power, 200W TDP. Verify chassis clearance and PSU capacity before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
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