Infrastructure selection for training
Compare plans for local GPU, HPC, and cloud before executing the selected candidate.
Activity responsibilities
| Activity | Responsibility |
|---|---|
profile-workload | Execute the profile workload stage and publish its declared outputs for downstream activities. |
plan-local-gpu | Execute the plan local GPU stage and publish its declared outputs for downstream activities. |
plan-hpc | Execute the plan HPC stage and publish its declared outputs for downstream activities. |
plan-cloud | Execute the plan cloud stage and publish its declared outputs for downstream activities. |
compare-plans | Execute the compare plans stage and publish its declared outputs for downstream activities. |
select-scope | Execute the select scope stage and publish its declared outputs for downstream activities. |
train | Execute the train stage and publish its declared outputs for downstream activities. |
Inputs
- Workflow profile
- Execution scopes
- Budget and deadline
Outputs
- Candidate plans
- Selection record
- Observed run
Provenance to preserve
- Planner version
- Predictions
- Plan-versus-observed error
Source bundle
The executable workflow and its implementation files are maintained in the separate AkôFlow examples repository. The documentation intentionally keeps code, container recipes, generators, and validation scripts out of the narrative page.
Open the complete source bundle ↗Verified local execution
These captures and the output manifest were produced by the fixture's local Docker run and validator. They are published with the same bundle as the runnable files.
Execution considerations
Evaluate candidates with frozen inputs and one common model. Queue planning experiments so concurrent load does not distort evidence.
AkôFlow boundary: the engine schedules, deploys, executes, transfers data, and records evidence. The ML or agent framework remains an implementation choice inside each activity.