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Description:

hydraflow 0.2.15

Hydraflow

Overview
Hydraflow is a library designed to seamlessly integrate
Hydra and MLflow, making it easier to
manage and track machine learning experiments. By combining the flexibility of
Hydra's configuration management with the robust experiment tracking capabilities
of MLflow, Hydraflow provides a comprehensive solution for managing complex
machine learning workflows.
Key Features

Configuration Management: Utilize Hydra's advanced configuration management
to handle complex parameter sweeps and experiment setups.
Experiment Tracking: Leverage MLflow's tracking capabilities to log parameters,
metrics, and artifacts for each run.
Artifact Management: Automatically log and manage artifacts, such as model
checkpoints and configuration files, with MLflow.
Seamless Integration: Easily integrate Hydra and MLflow in your machine learning
projects with minimal setup.

Installation
You can install Hydraflow via pip:
pip install hydraflow

Getting Started
Here is a simple example to get you started with Hydraflow:
import hydra
import hydraflow
import mlflow
from dataclasses import dataclass
from hydra.core.config_store import ConfigStore
from pathlib import Path

@dataclass
class MySQLConfig:
host: str = "localhost"
port: int = 3306

cs = ConfigStore.instance()
cs.store(name="config", node=MySQLConfig)

@hydra.main(version_base=None, config_name="config")
def my_app(cfg: MySQLConfig) -> None:
# Set experiment by Hydra job name.
hydraflow.set_experiment()

# Automatically log Hydra config as params.
with hydraflow.start_run():
# Your app code below.

with hydraflow.watch(callback):
# Watch files in the MLflow artifact directory.
# You can update metrics or log other artifacts
# according to the watched files in your callback
# function.
pass

# Your callback function here.
def callback(file: Path) -> None:
pass

if __name__ == "__main__":
my_app()