rohit.vision

Experiment Tracking & Configuration Tools

WIP mlops experiment-tracking hydra mlflow mlops optuna wandb 2 min read

Hydra, Weights & Biases (W&B), MLflow, and Optuna

Configuration Management

Hydra

A framework by Meta for elegantly configuring complex machine learning applications.

  • Mechanism: Instead of using massive argparse blocks or flat JSON/YAML files, Hydra allows you to dynamically build hierarchical configurations using multiple YAML files.
  • Key Feature: Composition. You can override specific deeply nested configuration values directly from the command line without changing the source code.
  • Example: python train.py model=resnet50 dataset=imagenet optimizer.lr=0.01

Experiment Tracking Platforms

Weights & Biases (W&B)

The industry standard SaaS platform for tracking machine learning experiments, particularly popular in deep learning and LLM fine-tuning.

  • Features:
    • Live dashboards for loss curves, accuracy, and system metrics (GPU utilization).
    • Artifact tracking (saving models and datasets).
    • Sweeps (hyperparameter tuning).
  • Pros: Incredibly easy to integrate (wandb.init()), excellent UI, seamless integration with Hugging Face and PyTorch Lightning.

MLflow

An open-source platform developed by Databricks for managing the end-to-end machine learning lifecycle.

  • Core Components:
    • Tracking: Logging parameters, metrics, and artifacts (similar to W&B but self-hosted or managed via Databricks).
    • Models: A standard format for packaging machine learning models.
    • Registry: A centralized model store, set of APIs, and UI to collaboratively manage the full lifecycle of an MLflow Model (Staging -> Production).
  • Pros: Enterprise-ready, deeply integrated into the Databricks ecosystem, strong focus on the deployment handoff (Model Registry).

Hyperparameter Optimization

Optuna

An open-source hyperparameter optimization framework designed for machine learning.

  • Mechanism: Automates the trial-and-error process of finding the best hyperparameters using intelligent search algorithms (like Tree-structured Parzen Estimator - TPE) rather than brute-force Grid Search.
  • Key Features:
    • Define-by-Run: You can construct the search space dynamically during execution (useful for complex architectures like neural networks where layer count might be a variable).
    • Pruning: Automatically detects and kills unpromising trials early to save compute time.
    • Integrates smoothly with W&B and MLflow for logging the trials.