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

from 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

# 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/