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Experiment Tracking Pack

$29

Experiment tracking with W&B and MLflow, custom dashboards, metrics comparison, and reproducibility tooling.

📁 8 files🏷 v1.0.0
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📁 File Structure 8 files

experiment-tracking-pack/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-experiment-comparison.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

Experiment Tracking Pack

Comprehensive experiment tracking setup with Weights & Biases and MLflow integration. Includes custom dashboards, metrics comparison tools, and reproducibility patterns for ML experiments.

What's Included

  • Weights & Biases project setup and configuration
  • MLflow tracking integration as secondary backend
  • Custom experiment comparison dashboards
  • Metrics visualization and reporting templates
  • Hyperparameter sweep tracking patterns
  • Experiment reproducibility configurations
  • Team collaboration and sharing setups

Quick Start


# 1. Copy the example config
cp config.example.yaml config.yaml

# 2. Set your W&B API key
export WANDB_API_KEY=your_key_here

# 3. Initialize a new project
wandb init

# 4. Run the example tracked experiment
python examples/tracked_experiment.py

Prerequisites

  • Python 3.9+
  • Weights & Biases account (free tier available)
  • MLflow 2.x (optional, for dual tracking)

Contents


experiment-tracking-pack/
  config.example.yaml
  docs/
    overview.md
    patterns/
      pattern-01-*.md
    checklists/
      pre-deployment.md
  templates/
    config.yaml

Support

For questions or issues, contact: megafolder122122@hotmail.com

License

MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.

📄 Code Sample .yaml preview

config.example.yaml # Experiment Tracking Pack - Example Configuration # Copy this file to config.yaml and update values for your environment wandb: entity: "your-team" project: "my-ml-project" tags: ["experiment", "baseline"] log_model: true log_code: true mlflow: enabled: false # Enable for dual tracking tracking_uri: "http://localhost:5000" experiment_name: "my-ml-project/baseline" tracking: log_frequency: 10 # Log metrics every N steps log_system_metrics: true log_git_info: true save_code: true comparison: metrics: - "accuracy" - "f1_score" - "loss" primary_metric: "accuracy" higher_is_better: true logging: level: "INFO"
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