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name: domino-flows description: 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.

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

Related Documentation

• FLOW-BASICS.md - DAG concepts, task definitions
• EXAMPLES.md - Common flow patterns

Quick Start

Basic Flow

Copyfrom flytekit import task, workflow

@task
def preprocess_data(input_path: str) -> str:
    """Task 1: Data preprocessing"""
    # Processing logic
    output_path = "/mnt/data/processed.parquet"
    return output_path

@task
def train_model(data_path: str) -> str:
    """Task 2: Model training"""
    # Training logic
    model_path = "/mnt/artifacts/model.pkl"
    return model_path

@workflow
def training_pipeline(input_path: str) -> str:
    """Workflow connecting tasks"""
    processed = preprocess_data(input_path=input_path)
    model = train_model(data_path=processed)
    return model

Running the Flow

Copy# Local execution
result = training_pipeline(input_path="/data/raw.csv")

# Submit to Domino
# Use Domino UI or CLI to trigger the flow

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/