# google-research/tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

Repository: https://github.com/google-research/tuning_playbook
Canonical: https://ross.abutalabs.com/products/tuning_playbook
License: NOASSERTION
License Family: other
Last push: 2024-06-18T00:25:53+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 94
- inputs: {"age_days": 1323, "days_push": 807, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 30297, forks 2419 (observed 2026-08-28T04:11:54.369949+00:00)

## What it is
A comprehensive guide from Google Research engineers on systematically maximizing deep learning model performance through disciplined hyperparameter tuning. It covers choosing architectures, optimizers, batch sizes, experiment design, and training pipeline best practices.

## Use cases
- how to tune hyperparameters for deep learning models
- guide for choosing batch size and learning rate
- how to systematically improve model accuracy
- best practices for running ML experiments
- deciding how long to train a neural network
- scientific approach to deep learning tuning
- setting up experiment tracking for model training

## When to choose
- you are training or fine-tuning deep learning models and want a rigorous tuning methodology
- you need guidance on experiment design and hyperparameter search strategy
- you are an ML engineer or researcher wanting to maximize model performance

## When to avoid
- you need runnable code, a library, or a tool rather than a written guide
- you are a beginner seeking introductory ML tutorials
- you need domain-specific advice outside neural network training

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, deep-learning, llm-training, developer-tools
- domain: deep-learning, machine-learning, tutorials, artificial-intelligence
- platform: python, cloud
- tags: hyperparameter-tuning, playbook, guide, best-practices, model-training, google-research, documentation, gpu

## Member repositories
- google-research/tuning_playbook (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:54.369949+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:52:46.947788+00:00, confidence not recorded.
  - readme: https://github.com/google-research/tuning_playbook (fetched 2026-08-28T04:11:54.369949+00:00, sha 77b0af092bf9)
- Data as of 2026-08-30T08:39:29.467469+00:00.