hydraflow 0.21.1

HydraFlow seamlessly integrates Hydra and MLflow to streamline ML experiment management, combining Hydra's configuration management with MLflow's tracking capabilities.

HydraFlow

Overview

HydraFlow seamlessly integrates Hydra and MLflow to streamline machine learning experiment workflows. By combining Hydra's powerful configuration management with MLflow's robust experiment tracking, HydraFlow provides a comprehensive solution for defining, executing, and analyzing machine learning experiments.

Design Principles

HydraFlow is built on the following design principles:

1. Type Safety - Utilizing Python dataclasses for configuration type checking and IDE support
2. Reproducibility - Automatically tracking all experiment configurations for fully reproducible experiments
3. Analysis Capabilities - Providing powerful APIs for easily analyzing experiment results
4. Workflow Integration - Creating a cohesive workflow by integrating Hydra's configuration management with MLflow's experiment tracking

Key Features

• Type-safe Configuration Management - Define experiment parameters using Python dataclasses with full IDE support and validation
• Seamless Hydra-MLflow Integration - Automatically register configurations with Hydra and track experiments with MLflow
• Advanced Parameter Sweeps - Define complex parameter spaces using extended sweep syntax for numerical ranges, combinations, and SI prefixes
• Workflow Automation - Create reusable experiment workflows with YAML-based job definitions
• Powerful Analysis Tools - Filter, group, and analyze experiment results with type-aware APIs
• Custom Implementation Support - Extend experiment analysis with domain-specific functionality

Installation

pip install hydraflow

Requirements: Python 3.13+

Quick Example

import hydraflow
from dataclasses import dataclass
from mlflow.entities import Run

@dataclass
class Config:
    width: int = 1024
    height: int = 768

@hydraflow.main(Config, tracking_uri="sqlite:///mlflow.db")
def app(run: Run, cfg: Config) -> None:
    # Your experiment code here
    print(f"Running with width={cfg.width}, height={cfg.height}")

if __name__ == "__main__":
    app()

Execute a parameter sweep with:

python app.py -m width=800,1200 height=600,900

Core Components

HydraFlow consists of the following key components:

Configuration Management

Define type-safe configurations using Python dataclasses:

@dataclass
class Config:
    learning_rate: float = 0.001
    batch_size: int = 32
    epochs: int = 10

Main Decorator

The @hydraflow.main decorator integrates Hydra and MLflow:

@hydraflow.main(Config)
def train(run: Run, cfg: Config) -> None:
    # Your experiment code

Workflow Automation

Define reusable experiment workflows in YAML:

jobs:
  train_models:
    run: python train.py
    sets:
      - each: model=small,medium,large
        all: learning_rate=0.001,0.01,0.1

Analysis Tools

Analyze experiment results with powerful APIs:

import mlflow
from hydraflow import Run, iter_run_dirs

# Load runs
mlflow.set_tracking_uri("sqlite:///mlflow.db")
runs = Run.load(iter_run_dirs())

# Filter and analyze
best_runs = runs.filter(model_type="transformer").to_frame("learning_rate", "accuracy")

Documentation

For detailed documentation, visit our documentation site:

• Getting Started - Installation and core concepts
• Practical Tutorials - Learn through hands-on examples
• User Guide - Detailed documentation of HydraFlow's capabilities
• API Reference - Complete API documentation

License

This project is licensed under the MIT License.