https://github.com/nzw0301/optuna-wandb
Example codes in the medium post titled "Optuna meets Weights and Biases."
https://github.com/nzw0301/optuna-wandb
hyperparameter-optimization optuna python pytoch wandb weights-and-biases
Last synced: 5 months ago
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Example codes in the medium post titled "Optuna meets Weights and Biases."
- Host: GitHub
- URL: https://github.com/nzw0301/optuna-wandb
- Owner: nzw0301
- Created: 2021-10-31T04:44:35.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-08-11T13:11:48.000Z (about 3 years ago)
- Last Synced: 2025-05-05T22:52:04.574Z (5 months ago)
- Topics: hyperparameter-optimization, optuna, python, pytoch, wandb, weights-and-biases
- Language: Python
- Homepage: https://medium.com/optuna/optuna-meets-weights-and-biases-58fc6bab893
- Size: 26.4 KB
- Stars: 22
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Optuna meets Weights and Biases
This repo provides the examples codes used in the medium post titled [Optuna meets Weights and Biases](https://medium.com/optuna/optuna-meets-weights-and-biases-58fc6bab893).
---
## Installation
## `pip`
```bash
pip install wandb optuna scikit-learn torch torchvision plotly
```
## `conda`
```bash
conda env create -f environment.yml
conda activate optuna-wandb
```
---
### Updated 🚀 [11-Aug-2022]: Add `as_multirun=True` example to make [`part-1`](./part-1/wandb_optuna.py) simpler
In forthcoming optuna v3, optuna's wandb callback provides `as_multirun` option to trace an objective function optimised by iterative way, e.g., stochastic gradient descent. Thanks to this feature, we can combine optuna and wandb more easily.