domino-flows

Orchestrate multi-step ML workflows using Domino Flows (built on Flyte). Define DAGs with typed inputs/outputs, heterogeneous environments, automatic lineage, and reproducibility. Use when building data pipelines, multi-stage training workflows, or processes requiring orchestration and monitoring.

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Domino Flows Skill

This skill provides comprehensive knowledge for orchestrating ML workflows using Domino Flows, built on the Flyte platform.

Key Concepts

What are Domino Flows?

Domino Flows enable:

DAG-based orchestration: Define workflows as directed acyclic graphs
Typed interfaces: Strong typing for inputs and outputs
Heterogeneous environments: Different environments per task
Automatic lineage: Track data and model provenance
Reproducibility: Version-controlled workflows
Scalability: Distributed execution across compute resources

Core Components

Component	Description
Task	Single unit of work (runs as a Domino Job)
Workflow	DAG connecting tasks
Artifact	Typed input/output passed between tasks
Launch Plan	Configured workflow execution

Quick Start

⚠️ Critical: Domino Flows does NOT support native Flyte @task decorators. Tasks must use DominoJobTask + DominoJobConfig. Only @workflow is unchanged.

Basic Flow

Each task runs as a Domino Job. Stage scripts read from /workflow/inputs/<name> and write to /workflow/outputs/o0. Pass PYTHONPATH=/mnt/code in the command.

from flytekit import workflow
from flytekitplugins.domino.task import DominoJobConfig, DominoJobTask

preprocess_task = DominoJobTask(
    name="Preprocess Data",
    domino_job_config=DominoJobConfig(
        Command="bash -c 'PYTHONPATH=/mnt/code python /mnt/code/stages/preprocess.py'",
    ),
    inputs={"input_path": str},
    outputs={"o0": str},
    use_latest=True,
)

train_task = DominoJobTask(
    name="Train Model",
    domino_job_config=DominoJobConfig(
        Command="bash -c 'PYTHONPATH=/mnt/code python /mnt/code/stages/train.py'",
    ),
    inputs={"preprocess_output": str},
    outputs={"o0": str},
    use_latest=True,
)

@workflow
def training_pipeline(input_path: str = "/mnt/data/raw.csv") -> str:
    preprocess_output = preprocess_task(input_path=input_path)
    result = train_task(preprocess_output=preprocess_output)
    return result

Stage Script Pattern

# stages/preprocess.py
import json, os

INPUTS, OUTPUTS = "/workflow/inputs", "/workflow/outputs"

def main():
    input_path = open(f"{INPUTS}/input_path").read().strip()
    # ... do work ...
    os.makedirs(OUTPUTS, exist_ok=True)
    with open(f"{OUTPUTS}/o0", "w") as f:
        f.write(json.dumps({"output_path": "/mnt/artifacts/processed.parquet"}))

if __name__ == "__main__":
    main()

Running the Flow

# Always commit and push first — jobs run against remote repo state
git add -A && git commit -m "..." && git push

# Trigger remotely
PYTHONPATH=/mnt/code pyflyte run --remote \
    my_flow.py training_pipeline \
    --input_path "/mnt/data/raw.csv"

When to Use Flows

Good Use Cases

Data processing → Model training pipelines
ETL with ML steps
Multi-stage training with different environments
Processes requiring reproducibility and lineage
Scheduled/triggered workflows

Not Ideal For

Single dataset with many small computations
Tasks that write to mutable shared state
Simple single-step processes
Real-time inference (use Model APIs instead)

Documentation Links

Domino Flows: https://docs.dominodatalab.com/en/latest/user_guide/78acf5/orchestrate-with-flows/
Flyte Documentation: https://docs.flyte.org/

Repository: dominodatalab/domino-claude-plugin
Path: skills/flows/SKILL.md
Commit: d86698d

Last updated: 14 hours ago
First committed: 6 months ago

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