An image pipeline can finish successfully and still leave its user with the wrong deliverable. The output may be on a temporary machine, the filename may refer to an older run, or the file may exist without enough context to reproduce it.
I work on CloudGPU. This workflow checklist applies to a single AI short-drama shot as well as an adapter-training experiment.
Define delivery before execution
Before a run, write down what counts as a usable result. For a generated shot, that might mean an image sequence or a video file, a known frame size, an expected naming convention and a manifest linking it to the approved workflow. For training, it might mean an adapter, its configuration and a small evaluation set.
The acceptance rule belongs to the task proposal. “The command returned success” is a useful execution observation, but it does not establish that the deliverable can be opened or traced to the intended inputs.
Keep a receipt with the result
A practical receipt records the workflow revision, model repository and immutable revision, selected files, input checksums, seed where applicable, command arguments, environment image and output checksums. It also records the job identifier and the destination where the exported result was verified.
Those fields answer different questions. A workflow revision explains the graph. A model revision explains which weights were requested. A checksum identifies the actual transferred file. A seed helps describe the generation request, but does not by itself make execution reproducible across different software or hardware.
Keep secrets, temporary download credentials and private customer material out of the receipt. A useful public example can describe file structure and verification without publishing the files themselves.
Distinguish execution success from delivery success
Treat result delivery as a separate check:
- The command reached a terminal state.
- The expected output names exist.
- The files are nonempty and can be decoded by the intended reader.
- Export completed to the owner-approved destination.
- The destination copy matches the recorded checksums.
- The receipt points to that destination copy, not a temporary working path.
If export fails, the task has not delivered the result even if the generation command succeeded. Preserve a clear failure report rather than marking the handoff complete.
Choose the collection path before the job
CloudGPU documents a public compute catalog, task quotes and bounded Jobs. A Job runs its command and then destroys its GPU. Its logs are a limited execution record, not a replacement for full output files. Arrange output export inside the command before it exits, using an owner-controlled destination and the approved data-handling policy.
For an interactive instance, download and verify results before destroying it. Do not assume that stopping a local client preserves unexported files. Optional persistent files and Workspace features need a separate capabilities and account-eligibility check; they do not automatically collect a Job’s outputs or restore an entire runtime.
The current public contract separates these capabilities; check the enabled features and account eligibility before choosing a collection path.
Make another run deliberate
Once the result receipt exists, rerun decisions become easier to explain. Keep the inputs and acceptance criteria stable when testing a workflow change. If a model file, graph or environment changes, record a new revision rather than silently attaching the old receipt.
A network retry is different from a new experiment. Preserve the original job identity when reconciling an uncertain request. A deliberate rerun needs a fresh proposal, current quote, appropriate authorization and its own result receipt.
A small handoff that helps the next person
Deliver the result and its receipt together. The next person should be able to tell what was requested, what actually ran, where the output was verified and what would change in a new run. That is more useful than a folder named “final” and a screenshot of a successful command.
CloudGPU’s agent workflow guide: https://cloudgpu.app/docs/agents?utm_source=devto&utm_medium=article&utm_campaign=202611_seg01_comfyui_gpu&utm_content=article1111_result_receipt
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