name	experiment-tracking
description	Reproducibility checklist, seed management, hardware logging, results schema design. Use when setting up experiment tracking or managing complex run structures.

Experiment Tracking

Follow these guidelines for rigorous tracking and reproducibility of scientific / system benchmark experiments.

1. Directory Structure

A clean structure is essential for running multiple experiments without cross-contamination.

results/
├── {experiment_name}_{timestamp}/
│   ├── metadata.json       # Hardware, OS, Git commit hash, raw arguments
│   ├── config/             # The exact generated/tested configuration
│   ├── logs/               # Standard out/error logs
│   └── data/               # Raw output metrics (.csv, .json)

2. Seed Management

Set seeds explicitly for all sources of randomness: numpy.random, random, torch, hash functions.
For robust multi-seed campaigns, loop over an explicit list of prime/distinct seeds (e.g., [42, 123, 456, 789, 1024]).
Log the specific seed used inside the experiment metadata.

3. Environment & Hardware Provenance

Every run should programmatically capture and save:

Git commit hash of the current code.
Whether there are uncommitted changes (git status).
OS details, Kernel version.
Hardware info: CPU model, Total RAM, Disk type.
This takes 1 second to gather via sys, platform, psutil, and subprocess but saves hours of confusion months later.

4. Results Schema Design

Write end-result summaries as clear JSON or CSV.
Ensure the schema captures both:
1. The input configuration vector (what was tested).
2. The output metric vector (the results: throughput, latency, variance).
Include validation/error states. If a run failed, track {"status": "failed", "error": "timeout"} instead of dropping the row silently.

5. Artifact Archival

Treat the results/ folder as append-only.
If an experiment had a bug, mark it as invalid in a README or database instead of deleting the directory, or move it to a deprecated/ folder. This preserves history.
Consistently bundle or tarball successful experiment groupings for backup.

ML/System Reproducibility Checklist

Ensure these are addressed:

Dependencies documented with exact versions (e.g., requirements.txt with locked versions).
Evaluation protocol heavily scripted minimizing human manual steps.
Hyperparameters to both the model and the optimization algorithm explicitly recorded.

Experiment Tracking

Follow these guidelines for rigorous tracking and reproducibility of scientific / system benchmark experiments.

1. Directory Structure

A clean structure is essential for running multiple experiments without cross-contamination.

results/ ├── {experiment_name}_{timestamp}/ │ ├── metadata.json # Hardware, OS, Git commit hash, raw arguments │ ├── config/ # The exact generated/tested configuration │ ├── logs/ # Standard out/error logs │ └── data/ # Raw output metrics (.csv, .json)

2. Seed Management

Set seeds explicitly for all sources of randomness: numpy.random, random, torch, hash functions.

For robust multi-seed campaigns, loop over an explicit list of prime/distinct seeds (e.g., [42, 123, 456, 789, 1024]).

Log the specific seed used inside the experiment metadata.

3. Environment & Hardware Provenance

Every run should programmatically capture and save:

Git commit hash of the current code.

Whether there are uncommitted changes (git status).

OS details, Kernel version.

Hardware info: CPU model, Total RAM, Disk type.

This takes 1 second to gather via sys, platform, psutil, and subprocess but saves hours of confusion months later.

4. Results Schema Design

Write end-result summaries as clear JSON or CSV.

Ensure the schema captures both:

The input configuration vector (what was tested).
The output metric vector (the results: throughput, latency, variance).

Include validation/error states. If a run failed, track {"status": "failed", "error": "timeout"} instead of dropping the row silently.

5. Artifact Archival

Treat the results/ folder as append-only.

If an experiment had a bug, mark it as invalid in a README or database instead of deleting the directory, or move it to a deprecated/ folder. This preserves history.

Consistently bundle or tarball successful experiment groupings for backup.

ML/System Reproducibility Checklist

Ensure these are addressed:

Dependencies documented with exact versions (e.g., requirements.txt with locked versions).

Evaluation protocol heavily scripted minimizing human manual steps.

Hyperparameters to both the model and the optimization algorithm explicitly recorded.

experiment-tracking