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<!-- PROJECT LOGO -->
<br />
<div align="center">
<a href="https://github.com/NicolasMICAUX/autohparams">
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<h3 align="center">AutoHparams</h3>
<p align="center">
Automatically create a hparams of hyper-parameters from global variables!
<br />
<!--
<a href="https://github.com/NicolasMICAUX/autohparams"><strong>Explore the docs »</strong></a>
-->
<br />
<br />
<a href="https://github.com/NicolasMICAUX/autohparams">View Demo</a>
·
<a href="https://github.com/NicolasMICAUX/autohparams/issues">Report Bug</a>
</div>
<!-- ABOUT THE PROJECT -->
## About The Project
<!-- [Screen Shot][product-screenshot] -->
Have you ever run some machine learning training run for hours, just to realize days later that you forgot to save some hyper-parameters? So you need to pray for your memory to be good, or re-run everything?
AutoHparams is a tool that dumps every variable in the global scope of your code to a dictionary that you can save using your favorite tool. This helps to avoid 80% of the missed hyper-parameters situations.
<!-- GETTING STARTED -->
## Getting Started
Using AutoHparams is a one-liner.
Install AutoHparams with pip :
```sh
pip install autohparams
```
Import it in your code, by adding this line :
```python
from autohparams import get_auto_hparams
```
To get a hparams dictionnary, just do:
```python
hparams = get_auto_hparams(globals())
```
**Advanced tip**
By default, `get_auto_hparams` ignores variables whose name starts with an underscore `_`. This is convenient to filter the variables you want to include in the hparams.
For example:
```python
lr = 0.001 # we want to report learning rate
bs = 64 # we want to report batch size
_gpu = 0 # we don't want to report the gpu used
hparams = get_auto_hparams(globals())
```
<!-- USAGE EXAMPLES -->
## Usage
You can now use it in any framework you want, for example:
**Tensorboard**
```python
import tensorflow as tf
from tensorboard.plugins.hparams import api as hp
with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
hp.hparams(hparams)
```
**MLflow**
```python
import mlflow
with mlflow.start_run():
mlflow.log_params(hparams)
```
**Weights & Biases (wandb)**
```python
import wandb
wandb.init(hparams=hparams)
```
**Comet.ml**
```python
from comet_ml import Experiment
experiment = Experiment()
experiment.log_parameters(hparams)
```
**Neptune.ai**
```python
import neptune.new as neptune
run = neptune.init()
run['parameters'] = hparams
```
**Pytorch Lightning**
```python
import pytorch_lightning as pl
trainer = pl.Trainer(logger=...)
trainer.logger.log_hyperparams(hparams)
```
**Guild AI**
```python
import guild
guild.run(hparams=hparams)
```
**Polyaxon**
```python
import polyaxon_sdk
api_client = polyaxon_sdk.ApiClient()
api_client.create_hyper_params(run_uuid='uuid-of-your-run', body=hparams)
```
**ClearML**
```python
from clearml import Task
task = Task.init()
task.set_parameters(hparams)
```
**Kubeflow**
```python
from kubeflow.metadata import metadata
store = metadata.Store()
store.log_metadata(hparams)
```
#### Even faster import
If you like python wizardry, you can also import and use autohparams as a function:
```python
import autohparams
config = autohparams(globals())
```
One cannot do easier!
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- CONTRIBUTING -->
## Contributing
Contributions are welcome!
<!-- ROADMAP-->
### Roadmap/todo
<!-- table with columns : task, importance, difficulty, status, description -->
<!--
| Task | Importance | Difficulty | Contributor on it | Description |
|:-------------------------|------------|------------|-------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
| [Write some tests](https://github.com/NicolasMICAUX/autohparams/discussions/5) | 4/5 | 2/5 | NOBODY | Write some tests to ensure that the code is working properly. |
| [Profile code](https://github.com/NicolasMICAUX/autohparams/discussions/11) | 2/5 | 1/5 | NOBODY | Profile the code to see if we can speed it up a little. |
-->
Non-Code contribution :
| Task | Importance | Difficulty | Contributor on it | Description |
|:-------------------------|------------|------------|-------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Adding documentation](https://github.com/NicolasMICAUX/autohparams/discussions/6) | 4/5 | 1/5 | NOBODY | Write basic tutorials with real-life scenarios, write a wiki for other contributors to better understand the functioning of the library. |
_For every todo, just click on the link to find the discussion where I describe how I would do it._
See the [discussions](https://github.com/NicolasMICAUX/autohparams/discussions) for a full list of proposed features (and known issues).
<p align="right">(<a href="#readme-top">back to top</a>)</p>
### How to contribute
Contributing is an awesome way to learn, inspire, and help others. Any contributions you make are **greatly appreciated**, even if it's just about styling and best practices.
If you have a suggestion that would make this project better, please fork the repo and create a pull request.
Don't forget to give the project a star! Thanks again!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/YourAmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## Authors
This library was created by [Nicolas MICAUX](https://github.com/NicolasMICAUX).
<!-- MARKDOWN LINKS & IMAGES -->
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[contributors-shield]: https://img.shields.io/github/contributors/NicolasMICAUX/autohparams.svg?style=for-the-badge
[contributors-url]: https://github.com/NicolasMICAUX/autohparams/graphs/contributors
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[stars-url]: https://github.com/NicolasMICAUX/autohparams/stargazers
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[issues-url]: https://github.com/NicolasMICAUX/autohparams/issues
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[pypi-url]: https://pypi.org/project/searchin/
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[python-url]: https://www.python.org/downloads/
[//]: # ([license-shield]: https://img.shields.io/github/license/NicolasMICAUX/autohparams.svg?style=for-the-badge)
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So you need to pray for your memory to be good, or re-run everything?\n\nAutoHparams is a tool that dumps every variable in the global scope of your code to a dictionary that you can save using your favorite tool. This helps to avoid 80% of the missed hyper-parameters situations.\n\n<!-- GETTING STARTED -->\n## Getting Started\nUsing AutoHparams is a one-liner.\n\nInstall AutoHparams with pip :\n```sh\npip install autohparams\n```\n\nImport it in your code, by adding this line :\n```python\nfrom autohparams import get_auto_hparams\n```\n\nTo get a hparams dictionnary, just do: \n```python\nhparams = get_auto_hparams(globals())\n```\n\n**Advanced tip** \nBy default, `get_auto_hparams` ignores variables whose name starts with an underscore `_`. This is convenient to filter the variables you want to include in the hparams. \nFor example:\n```python\t\nlr = 0.001 # we want to report learning rate\nbs = 64 # we want to report batch size\n_gpu = 0 # we don't want to report the gpu used\nhparams = get_auto_hparams(globals())\n```\n\n<!-- USAGE EXAMPLES -->\n## Usage\nYou can now use it in any framework you want, for example: \n**Tensorboard**\n```python\nimport tensorflow as tf\nfrom tensorboard.plugins.hparams import api as hp\n\nwith tf.summary.create_file_writer('logs/hparam_tuning').as_default():\n hp.hparams(hparams)\n```\n\n**MLflow**\n```python\nimport mlflow\n\nwith mlflow.start_run():\n mlflow.log_params(hparams)\n```\n\n**Weights & Biases (wandb)**\n```python\nimport wandb\n\nwandb.init(hparams=hparams)\n```\n\n**Comet.ml**\n```python\nfrom comet_ml import Experiment\n\nexperiment = Experiment()\nexperiment.log_parameters(hparams)\n```\n\n**Neptune.ai**\n```python\nimport neptune.new as neptune\n\nrun = neptune.init()\n\nrun['parameters'] = hparams\n```\n\n**Pytorch Lightning**\n```python\nimport pytorch_lightning as pl\n\ntrainer = pl.Trainer(logger=...)\ntrainer.logger.log_hyperparams(hparams)\n```\n\n**Guild AI**\n```python\nimport guild\n\nguild.run(hparams=hparams)\n```\n\n**Polyaxon**\n```python\nimport polyaxon_sdk\n\napi_client = polyaxon_sdk.ApiClient()\napi_client.create_hyper_params(run_uuid='uuid-of-your-run', body=hparams)\n```\n\n**ClearML**\n```python\nfrom clearml import Task\n\ntask = Task.init()\ntask.set_parameters(hparams)\n```\n\n**Kubeflow**\n```python\nfrom kubeflow.metadata import metadata\n\nstore = metadata.Store()\nstore.log_metadata(hparams)\n```\n\n#### Even faster import\nIf you like python wizardry, you can also import and use autohparams as a function:\n```python\nimport autohparams\nconfig = autohparams(globals())\n```\nOne cannot do easier!\n\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n<!-- CONTRIBUTING -->\n## Contributing \nContributions are welcome! \n\n<!-- ROADMAP-->\n### Roadmap/todo\n<!-- table with columns : task, importance, difficulty, status, description -->\n<!-- \n| Task | Importance | Difficulty | Contributor on it | Description |\n|:-------------------------|------------|------------|-------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|\n| [Write some tests](https://github.com/NicolasMICAUX/autohparams/discussions/5) | 4/5 | 2/5 | NOBODY | Write some tests to ensure that the code is working properly. |\n| [Profile code](https://github.com/NicolasMICAUX/autohparams/discussions/11) | 2/5 | 1/5 | NOBODY | Profile the code to see if we can speed it up a little. |\n-->\n\nNon-Code contribution :\n\n| Task | Importance | Difficulty | Contributor on it | Description |\n|:-------------------------|------------|------------|-------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [Adding documentation](https://github.com/NicolasMICAUX/autohparams/discussions/6) | 4/5 | 1/5 | NOBODY | Write basic tutorials with real-life scenarios, write a wiki for other contributors to better understand the functioning of the library. |\n\n\n_For every todo, just click on the link to find the discussion where I describe how I would do it._ \nSee the [discussions](https://github.com/NicolasMICAUX/autohparams/discussions) for a full list of proposed features (and known issues).\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n### How to contribute\nContributing is an awesome way to learn, inspire, and help others. 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