Batch inference
Partition a large input, infer concurrently, and merge traceable predictions.
Activity responsibilities
| Activity | Responsibility |
|---|---|
load-model | Execute the load model stage and publish its declared outputs for downstream activities. |
partition-input | Execute the partition input stage and publish its declared outputs for downstream activities. |
infer-shard-a | Execute the infer shard A stage and publish its declared outputs for downstream activities. |
infer-shard-b | Execute the infer shard B stage and publish its declared outputs for downstream activities. |
infer-shard-n | Execute the infer shard N stage and publish its declared outputs for downstream activities. |
merge-predictions | Execute the merge predictions stage and publish its declared outputs for downstream activities. |
publish | Execute the publish stage and publish its declared outputs for downstream activities. |
Inputs
- Approved model
- Input dataset
- Inference configuration
Outputs
- Prediction shards
- Merged predictions
- Completeness report
Provenance to preserve
- Shard boundaries
- Model digest
- Per-shard logs
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
Place shards according to memory, accelerator availability, and data locality. The merge stage must detect missing shards.
AkôFlow boundary: the engine schedules, deploys, executes, transfers data, and records evidence. The ML or agent framework remains an implementation choice inside each activity.