Prepare the job
Choose the code, inputs, and compute resources needed for an analysis.
Run code and keep the result with the work.
Compute. Execute analytical jobs with defined inputs and resources. Inspect logs and save outputs alongside the task that needs them.
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Compute. Inputs, execution, and outputs in view.
Inspect execution
Follow progress and logs, and investigate failed runs.
Keep the outputs
Save the result and supporting artifacts with the input and run information needed to understand it.
Follow the answer back to the job.
The recovery Task links to its optimizer job. Inspect the runtime, resources, and logs behind the result.
The recovery Task retains the optimizer result. Follow its link to the completed job rather than reconstructing the computation elsewhere.
Review the Python runtime, machine resources, timing, and completion status associated with this run.
The saved logs describe the input and candidate capacity. The team can use that evidence in a routing decision; computation itself does not change a carrier booking.
In practice
A carrier recovery needs a capacity calculation. The team opens the optimizer job from its Task, inspects the execution details, and reads the saved logs. The calculation supports the recovery decision, and the operator remains responsible for approving any carrier change.
Explore the solutionDiscuss your compute workflow.
Describe the calculation, its inputs, and how the team will use the output. We’ll discuss execution and resource requirements.