Model distillation
Generate teacher targets and train a smaller student with measured quality and latency.
Import this workflow from AkôFlow Hub
Open the public README and measured execution results. The Hub includes the execution capture, activity metrics, logs and version history. Reading public results does not require an account.
In Desktop, choose Create workflow → Import from Hub and paste this version-specific URI:
akoflow://hub/publications/a153be08-6e72-468e-8e14-7702c81aa640/versions/1
Download the exact workflow YAML (v1). Configure the runtime prerequisites below before planning and executing it.
Run this example locally
Generate soft labels with a local sigmoid teacher, fit a cheaper affine student, evaluate it against held-out teacher predictions, and measure actual CPU prediction latency in a parallel branch.
Install Go and Python 3.12 or newer.
git clone https://github.com/UFFeScience/akoflow-examples.git &&
cd akoflow-examples &&
git checkout --detach d890c3171e3661890305b763b8fc3ede48bb61b4 &&
cd machine-learning/model-distillation &&
./prepare.sh &&
./run.sh
Run the block from the directory where you keep your repositories. If you already cloned the repository, enter this example's directory and run ./prepare.sh && ./run.sh.
Preparation downloads the Go module and any declared Python packages once. The workload then runs locally with offline input data. Every meaningful step is a separate process; independent activities run in parallel after their dependencies complete.
Read outputs/manifest.json for the generated artifacts, outputs/dag-validation.json for dependency waves, and outputs/stage-<activity>.json for each activity's observed result. A failed stage stops the run and prevents a successful final manifest.
Expected outputs: student.pkl, distillation-report.json.
Run the same activities in Desktop
Start your local AkôFlow daemon with Docker available and configure AKOFLOW_API_TOKEN in your shell. From this example's directory, run:
go run ../../tools/daemon-local -root ../.. -example machine-learning/model-distillation
This command validates the daemon connection, builds the declared image, registers the local environment and workflow, and submits a real run. The daemon owns execution and file transfers. Open the printed run ID in Desktop to inspect each activity.
Activity responsibilities
| Activity | Responsibility |
|---|---|
load-teacher | Execute the load-teacher stage and publish its declared outputs for downstream activities. |
soft-labels | Execute the soft-labels stage and publish its declared outputs for downstream activities. |
train-student | Execute the train-student stage and publish its declared outputs for downstream activities. |
quality | Execute the quality stage and publish its declared outputs for downstream activities. |
latency | Execute the latency stage and publish its declared outputs for downstream activities. |
publish | Execute the publish stage and publish its declared outputs for downstream activities. |
Inputs
- Teacher model
- Examples
- Student architecture
Outputs
- student.pkl
- distillation-report.json
Provenance to preserve
- Teacher revision
- Generated targets
- Benchmark hardware
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 ↗Execution evidence
Verified on local Docker: 6/6 activities completed. These captures belong to run showcase-model-distillation-local-89ee6b6444-run-20261020t191140.471480000.
Read the daemon execution record · Local output manifest
Each stage also ran through the standalone local command. Read the dependency and artifact validation.
load-teacher
After: workflow start.
soft-labels
After: load-teacher.
train-student
After: soft-labels.
quality
After: train-student.
latency
After: train-student.
publish
After: quality, latency.
Execution considerations
Cache generated labels and run latency tests on the intended deployment class rather than the training host.
AkôFlow boundary: the engine schedules, deploys, executes, transfers data, and records evidence. The ML or agent framework remains an implementation choice inside each activity.