SOsonabinu·Aug 22, 2025Submission
1 points · Open discussiongoogle-research
tuning_playbook
AI & Machine Learning2023–2025
google-research / tuning_playbook
tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
12Distinct people
12Mentions
0Explicit recommenders
30.4KGitHub stars
What to know before exploring
What it is
A playbook for systematically maximizing the performance of deep learning models.
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
No recommendation phrase was found in the indexed comments.
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Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month12 dated mentions
Observed mentions: 2023-01-19 03:42:33 UTC to 2025-08-22 14:10:06 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jan 2023 | 2023-01-01 00:00:00 UTC | 2023-02-01 00:00:00 UTC | 3 | 3 | 0 |
| Feb 2023 | 2023-02-01 00:00:00 UTC | 2023-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2023 | 2023-03-01 00:00:00 UTC | 2023-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2023 | 2023-04-01 00:00:00 UTC | 2023-05-01 00:00:00 UTC | 1 | 0 | 1 |
| May 2023 | 2023-05-01 00:00:00 UTC | 2023-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2023 | 2023-06-01 00:00:00 UTC | 2023-07-01 00:00:00 UTC | 1 | 0 | 1 |
| Jul 2023 | 2023-07-01 00:00:00 UTC | 2023-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2023 | 2023-08-01 00:00:00 UTC | 2023-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2023 | 2023-09-01 00:00:00 UTC | 2023-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2023 | 2023-10-01 00:00:00 UTC | 2023-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2023 | 2023-11-01 00:00:00 UTC | 2023-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2023 | 2023-12-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2024 | 2024-01-01 00:00:00 UTC | 2024-02-01 00:00:00 UTC | 1 | 0 | 1 |
| Feb 2024 | 2024-02-01 00:00:00 UTC | 2024-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2024 | 2024-03-01 00:00:00 UTC | 2024-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2024 | 2024-04-01 00:00:00 UTC | 2024-05-01 00:00:00 UTC | 1 | 0 | 1 |
| May 2024 | 2024-05-01 00:00:00 UTC | 2024-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2024 | 2024-06-01 00:00:00 UTC | 2024-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2024 | 2024-07-01 00:00:00 UTC | 2024-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2024 | 2024-08-01 00:00:00 UTC | 2024-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2024 | 2024-09-01 00:00:00 UTC | 2024-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2024 | 2024-10-01 00:00:00 UTC | 2024-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2024 | 2024-11-01 00:00:00 UTC | 2024-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2024 | 2024-12-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2025 | 2025-01-01 00:00:00 UTC | 2025-02-01 00:00:00 UTC | 1 | 1 | 0 |
| Feb 2025 | 2025-02-01 00:00:00 UTC | 2025-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2025 | 2025-03-01 00:00:00 UTC | 2025-04-01 00:00:00 UTC | 3 | 1 | 2 |
| Apr 2025 | 2025-04-01 00:00:00 UTC | 2025-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2025 | 2025-05-01 00:00:00 UTC | 2025-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2025 | 2025-06-01 00:00:00 UTC | 2025-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2025 | 2025-07-01 00:00:00 UTC | 2025-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2025 | 2025-08-01 00:00:00 UTC | 2025-09-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
12 sourcesYou might find this helpful for prioritizing which knobs to turn first
https://github.com/google-research/tuning_playbook
In Optimizing ML training with metagradient descentPlaybook for maximizing the performance of deep learning models
2 points · Open discussionMaybe this will help:
https://github.com/google-research/tuning_playbook
In Pen and Paper Exercises in Machine Learning (2022)I think you might want to read the Tuning Playbook if you haven't already. https://github.com/google-research/tuning_playbook
In Ask HN: Are deep learning theorist employable in the industryThese are also called quasirandom numbers. Despite games, another use case is for hyperparameter search for neural networks.
https://github.com/google-research/tuning_playbook?tab=readm...
In When random numbers are too random: Low discrepancy sequences (2017)For a general overview, this could be a good starting point [1]. As for deep learning, you may wanna start from here [2], but I personally had good results with Hyperband [3] for DL.
[1] https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm....
[2] https://github.com/google-research/tuning_playbook
[3] https://jmlr.csail.mit.edu/papers/v18/16-558.html
In Hyperparameter Optimization for LLMs via Scaling LawsBatch size can be used for regularisation, but using it for that will limit training performance. From the Google Research Tuning Playbook:
> The batch size governs the training speed and shouldn't be used to directly tune the validation set performance. Often, the ideal batch size will be the largest batch size supported by the available hardware.
> […]
> As long as all hyperparameters are well-tuned (especially the learning rate and regularization hyperparameters) and the number of training steps is sufficient, the same final performance should be attainable using any batch size (see Shallue et al. 2018).
https://github.com/google-research/tuning_playbook#choosing-...
The ideal case is full-batch with tuneable regularisation, just the hardware gets expensive.
In Beyond automatic differentiationGoogle Research – Deep Learning Tuning Playbook
3 points · Open discussion