AkôFlow examples

Local runnable examples for AkôFlow, with the same activity DAGs used in Desktop and in the project documentation.

The catalog contains 27 small CPU workloads and the full 58-activity Montage 050d scientific workflow. Each example includes its input data (or a pinned image carrying that data), a preparation command, a local runner, a validator, and local daemon infrastructure. Current Desktop captures are in each bundle's screenshots/current/, with their exact run and image identified in its manifest.

Clone once and run a local example

Install Go and Python 3.12+. Blender and Montage also require Docker; Montage requires linux/amd64 support. Preparation downloads the Go YAML module and the pinned packages required by that example. Workloads then run on local input data. The small examples perform real computation; their teaching profiles do not require cloud credentials, hosted LLMs, or GPU hardware.

git clone https://github.com/UFFeScience/akoflow-examples.git
cd akoflow-examples

Each command below starts from the repository root and keeps your shell in that directory:

Example	Local command (from the repository root)
Montage 050 locally	(cd scientific-workflows/montage-050 && ./prepare.sh && ./run.sh)
AI Assisted Analysis	(cd scientific-ai/ai-assisted-analysis && ./prepare.sh && ./run.sh)
Experiment Feedback Loop	(cd scientific-ai/experiment-feedback-loop && ./prepare.sh && ./run.sh)
Surrogate Model	(cd scientific-ai/surrogate-model && ./prepare.sh && ./run.sh)
Anomaly Detection	(cd machine-learning/anomaly-detection && ./prepare.sh && ./run.sh)
Batch Inference	(cd machine-learning/batch-inference && ./prepare.sh && ./run.sh)
Computer Vision	(cd machine-learning/computer-vision && ./prepare.sh && ./run.sh)
Hyperparameter Search	(cd machine-learning/hyperparameter-search && ./prepare.sh && ./run.sh)
Inference Benchmark	(cd machine-learning/inference-benchmark && ./prepare.sh && ./run.sh)
Infrastructure Selection	(cd machine-learning/infrastructure-selection && ./prepare.sh && ./run.sh)
LoRA Fine Tuning	(cd machine-learning/lora-fine-tuning && ./prepare.sh && ./run.sh)
Model Comparison	(cd machine-learning/model-comparison && ./prepare.sh && ./run.sh)
Model Conversion	(cd machine-learning/model-conversion && ./prepare.sh && ./run.sh)
Model Distillation	(cd machine-learning/model-distillation && ./prepare.sh && ./run.sh)
Model Ensemble	(cd machine-learning/model-ensemble && ./prepare.sh && ./run.sh)
Training with MLflow	(cd machine-learning/training-with-mlflow && ./prepare.sh && ./run.sh)
Audio Transcription	(cd generative-ai/audio-transcription && ./prepare.sh && ./run.sh)
Document Extraction	(cd generative-ai/document-extraction && ./prepare.sh && ./run.sh)
Local RAG Factory	(cd generative-ai/local-rag && ./prepare.sh && ./run.sh)
RAG Indexing	(cd generative-ai/rag-indexing && ./prepare.sh && ./run.sh)
Synthetic Dataset	(cd generative-ai/synthetic-dataset && ./prepare.sh && ./run.sh)
AI Video Studio	(cd generative-ai/video-studio && ./prepare.sh && ./run.sh)
Bounded Agent	(cd agentic-workflows/bounded-agent && ./prepare.sh && ./run.sh)
Multi-Agent Review	(cd agentic-workflows/multi-agent-review && ./prepare.sh && ./run.sh)
Supervised Tool Loop	(cd agentic-workflows/supervised-tool-loop && ./prepare.sh && ./run.sh)
Document Intelligence	(cd document-ai/document-intelligence && ./prepare.sh && ./run.sh)
Video Intelligence	(cd media/video-intelligence && ./prepare.sh && ./run.sh)
Blender Render Farm	(cd rendering/blender-render-farm && ./prepare.sh && ./run.sh)

The standalone command writes outputs/manifest.json, outputs/dag-validation.json, and outputs/stage-<activity>.json. Independent activities become ready after their own dependencies complete; the local runner enforces a bounded number of concurrent processes. A failed stage prevents a successful final manifest.

Run the same DAG through the daemon

Start a local AkôFlow daemon with Docker available and configure AKOFLOW_API_TOKEN in your shell. For example:

go run ./tools/daemon-local -example machine-learning/training-with-mlflow

This validates Docker through the daemon API, builds the declared OCI image, registers local-only infrastructure and the workflow, imports a plan, and submits a real run. The daemon performs execution and artifact transfers; the renderer never runs connectivity checks, shell commands, or Docker. The command records the exact projection in evidence/daemon-run.json and prints the run ID for inspection in Desktop.

For the full long-running scientific example, submit and leave the daemon in charge:

go run ./tools/daemon-local -example scientific-workflows/montage-050 -wait=false

Workload scope

The local profiles are explicit about their inputs and algorithms. They include numerical integration and regression, a tiny rank-one adapter, geometric image classification, real ONNX and TorchScript export, schema-based generation, a tone-vocabulary audio recognizer, pixel-template OCR, and bounded deterministic tool agents. Infrastructure comparisons separate CPU measurements from declared placement assumptions. The MLflow example uses the actual SDK and a local SQLite tracking store.

Montage preserves all 58 original native commands and 138 named file handoffs from the pinned 050d image. It is not a timing simulation and can take substantially longer than the small profiles. Its CI preparation check verifies the source and image contract without claiming full mosaic completion.

Repository layout

• scientific-workflows/ and scientific-ai/: astronomical mosaics, numerical experiments, and learned scientific surrogates.
• machine-learning/, generative-ai/, document-ai/, media/, rendering/, and agentic-workflows/: local domain examples.
• workloads/: canonical implementations for the 22 small pattern workloads; go run ./tools/materialize generates their standalone source bundles.
• tools/: Go runners, catalog discovery, infrastructure generation, documentation export, and validators.
• simulation/, local/, kind/, slurm/, real/, connections/, onboarding/, server-instance/, and experiments/: additional infrastructure and experiment bundles.

The showcase standard defines offline workload execution, real computation, observable activities, and artifact validation. Product documentation is published at akoflow.com.

Continuous integration

The example files live in this repository, while the reusable CI implementation is maintained in UFFeScience/akoflow/.github/workflows/examples-ci.yml. The small workflow in this repository passes the exact pull-request or main commit to that central workflow. Runnable showcases are discovered from their docker/Dockerfile and executable run.sh; adding a showcase does not require editing a duplicated matrix.

After a push to main, CI publishes validated showcase images to GHCR and notifies the AkôFlow documentation workflow. Configure the repository secret AKOFLOW_DOCS_DISPATCH_TOKEN with access to dispatch workflows in UFFeScience/akoflow. If the secret is temporarily unavailable, the daily documentation reconciliation still checks out this repository and validates all referenced example files.