experiment-tracking

Record machine learning experiment metadata with Git and WandB integration.

Updated Jul 12, 2024

Search Tags:#git #mlops #reporting #metadata #experiment tracking #wandb

Ready to run

npx skills add https://github.com/ksopyla/MrCogito --skill experiment-tracking-ksopyla

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-tracking
Source: https://github.com/ksopyla/MrCogito/tree/main/.cursor/skills/experiment-tracking
Command: npx skills add https://github.com/ksopyla/MrCogito --skill experiment-tracking-ksopyla

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of recording, monitoring, and reporting on machine learning experiments, ensuring all critical metadata and results are captured systematically.

Core Features & Use Cases

Experiment Logging: Records training runs, evaluation sweeps, and key metrics.
Metadata Management: Links experiments to Git commits, tags, and WandB runs.
Reporting: Facilitates the creation of run reports and updates to experiment registries.
Use Case: When starting a new training run for a concept encoder model, use this skill to create a Git tag, log the hypothesis and hyperparameters in the master experiment log, and ensure WandB is configured to capture Git information.

Quick Start

Use the experiment-tracking skill to record the details of a new training run before starting it.

Frequently Asked Questions about experiment-tracking

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I track machine learning experiment metadata with Git and WandB?▼

Recording experiment metadata involves logging run configurations, hyperparameters, and benchmark results while linking them to Git commits and WandB runs. This ensures systematic tracking of training and evaluation outcomes for reproducible machine learning development.

What is the best way to log ML training runs for reproducible research?▼

The best way to log ML training runs is by systematically recording run configurations, hypotheses, and evaluation metrics. Linking these runs to Git tags and WandB integration ensures all experimental outcomes are comprehensively documented for decision-making.

How do I record hyperparameters and Git commits before starting a training run?▼

Before starting a training run, you can record hyperparameters and Git commits by creating a Git tag and logging the hypothesis in a master experiment log. This captures the exact codebase state and configuration for reproducibility.

Does this experiment tracking approach work with evaluation sweeps and benchmark results?▼

Yes, this experiment tracking approach works with evaluation sweeps and benchmark results. It manages and records metadata for both training and evaluation experiments, facilitating the creation of run reports and updates to experiment registries.

Can I use WandB integration to capture Git information for my ML experiments?▼

Yes, you can use WandB integration to capture Git information for your ML experiments. The tracking process ensures WandB is configured to capture Git details, linking your run configurations and metadata directly to your repository state.