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🛰️ tinytracker - Simple Tracking for Your ML Experiments

💡 Overview

tinytracker is a minimal, local-only experiment tracker designed for machine learning projects. It helps you easily manage and track your experiments, allowing you to focus on what matters most—developing your models and optimizing your results.

🚀 Getting Started

To begin using tinytracker, follow the steps below. If you have any questions or need further assistance, feel free to explore our resources or reach out for help.

📥 Download tinytracker

Visit the Releases page to download the latest version of tinytracker.

✅ System Requirements

Before installing tinytracker, ensure your system meets the following requirements:

• Operating System: Windows 10 or higher, macOS (latest versions), Linux (most distributions)
• Python: Version 3.6 or higher
• SQLite: Pre-installed with Python
• Disk Space: At least 100 MB free

📦 Installation Steps

1. Go to the Releases page.
2. Find the latest release version.
3. Download the file suitable for your operating system.
4. Locate the downloaded file on your device.
5. Double-click on the file to start the installation process.
6. Follow the on-screen instructions to complete the installation.

⚙️ Running tinytracker

After installation, you can begin using tinytracker right away:

1. Open a terminal or command prompt on your device.
2. Type tinytracker and hit Enter.
3. Follow the prompts in the command line to create and manage your experiments.

🌟 Features

tinytracker comes packed with features that help streamline your ML project:

• Experiment Tracking: Save and categorize your experiments easily.
• Hyperparameter Optimization: Experiment with different parameters effortlessly.
• Local-First: Run your experiments locally without needing internet access.
• Python Integration: Seamlessly integrate with Python scripts.

🎓 Using tinytracker

To effectively use tinytracker for your projects, here are some tips:

• Create a New Experiment: Once you start tinytracker, use the command create <experiment_name> to set up a new experiment.
• Log Experiment Details: Use log <metric_name> <metric_value> to record metrics for your experiments.
• View Experiments: Use list to see all your experiments at a glance.

🌈 Community Support

Join the tinytracker community for support and updates. Share your experiences and learn from others who also use tinytracker for their machine learning projects. Check out the Community Forum linked in the repository for discussions and tips.

📚 Additional Resources

For in-depth guides on features and best practices, refer to the following resources:

• User Guide
• FAQ
• Troubleshooting Guide

These materials can help you maximize your use of tinytracker and overcome any challenges.

💬 Feedback

Your feedback is crucial to the development of tinytracker. If you encounter issues or have suggestions, please submit them through the GitHub Issues page or contact us directly.

Visit the Releases page to download the latest version of tinytracker and take charge of your machine learning experiments today!