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:

Type Safety - Utilizing Python dataclasses for configuration type checking and IDE support
Reproducibility - Automatically tracking all experiment configurations for fully reproducible experiments
Analysis Capabilities - Providing powerful APIs for easily analyzing experiment results
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)
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
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.

Metadata

Release files for hydraflow 0.23.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for hydraflow 0.23.2
File	Size	Uploaded
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Built distribution (wheel)

Table of built distributions (wheels) for hydraflow 0.23.2
File	Interpreter	ABI	Platform	Reset
hydraflow-0.23.2-py3-none-any.whl 36.8 kB Apr 29, 2026	Python 3	none	any	Details

Total release size: 66.1 kB

Release files / hydraflow-0.23.2.tar.gz

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Release files / hydraflow-0.23.2-py3-none-any.whl

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Size	36.8 kB
Tags	Python 3
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Upload date	Apr 29, 2026
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Uploaded via	`uv/0.11.8 {"installer":{"name":"uv","version":"0.11.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}`