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.

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

• 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

pip install hydraflow

Requirements: Python 3.13+

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

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

@hydraflow.main(Config)
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

HydraFlow consists of the following key components:

Define type-safe configurations using Python dataclasses:

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

The @hydraflow.main decorator integrates Hydra and MLflow:

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

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

Analyze experiment results with powerful APIs:

from hydraflow import Run, iter_run_dirs

# Load runs
runs = Run.load(iter_run_dirs("mlruns"))

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

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

This project is licensed under the MIT License.