<div align="center" style="margin-bottom: 100px;">
<h2>Mobile first web app to monitor PyTorch & TensorFlow model training</h2>
<h3>Relax while your models are training instead of sitting in front of a computer</h3>
[](https://badge.fury.io/py/labml-app)
[](https://pepy.tech/project/labml-app)
[](http://docs.labml.ai/)
[](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)
<img src="https://github.com/labmlai/labml/blob/master/images/cover-dark.png" alt=""/>
</div>
This is an open-source library to push updates of your ML/DL model training to mobile. [Here's a sample experiment](https://app.labml.ai/run/39b03a1e454011ebbaff2b26e3148b3d)
### Notable Features
* **Mobile first design:** web version, that gives you a great mobile experience on a mobile browser.
* **Model Gradients, Activations and Parameters:** Track and compare these indicators independently. We provide a separate analysis for each of the indicator types.
* **Summary and Detail Views:** Summary views would help you to quickly scan and understand your model progress. You can use detail views for more in-depth analysis.
* **Track only what you need:** You can pick and save the indicators that you want to track in the detail view. This would give you a customised summary view where you can focus on specific model indicators.
* **Standard ouptut:** Check the terminal output from your mobile. No need to SSH.
### [📚 How to track experiments?](https://github.com/labmlai/labml)
### How to run app locally?
Install the PIP package
```sh
pip install labml-app
```
Start the server
```sh
labml app-server
```
Set the web api url to `http://localhost:5005/api/v1/track?` when you run experiments.
You can also [set this on `.labml.yaml`](https://github.com/labmlai/labml/blob/master/guides/labml_yaml_file.md).
```python
from labml import tracker, experiment
with experiment.record(name='sample', token='http://localhost:5005/api/v1/track?'):
for i in range(50):
loss, accuracy = train()
tracker.save(i, {'loss': loss, 'accuracy': accuracy})
```
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"description": "<div align=\"center\" style=\"margin-bottom: 100px;\">\n \n<h2>Mobile first web app to monitor PyTorch & TensorFlow model training</h2>\n<h3>Relax while your models are training instead of sitting in front of a computer</h3>\n\n\n[](https://badge.fury.io/py/labml-app)\n[](https://pepy.tech/project/labml-app)\n[](http://docs.labml.ai/)\n[](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)\n\n<img src=\"https://github.com/labmlai/labml/blob/master/images/cover-dark.png\" alt=\"\"/>\n</div>\n\nThis is an open-source library to push updates of your ML/DL model training to mobile. [Here's a sample experiment](https://app.labml.ai/run/39b03a1e454011ebbaff2b26e3148b3d)\n\n### Notable Features\n\n* **Mobile first design:** web version, that gives you a great mobile experience on a mobile browser.\n* **Model Gradients, Activations and Parameters:** Track and compare these indicators independently. We provide a separate analysis for each of the indicator types.\n* **Summary and Detail Views:** Summary views would help you to quickly scan and understand your model progress. You can use detail views for more in-depth analysis.\n* **Track only what you need:** You can pick and save the indicators that you want to track in the detail view. This would give you a customised summary view where you can focus on specific model indicators.\n* **Standard ouptut:** Check the terminal output from your mobile. No need to SSH.\n\n### [\ud83d\udcda How to track experiments?](https://github.com/labmlai/labml)\n\n### How to run app locally?\n\nInstall the PIP package\n\n```sh\npip install labml-app\n\n```\n\nStart the server\n\n```sh\nlabml app-server\n```\n\nSet the web api url to `http://localhost:5005/api/v1/track?` when you run experiments.\nYou can also [set this on `.labml.yaml`](https://github.com/labmlai/labml/blob/master/guides/labml_yaml_file.md).\n\n```python\nfrom labml import tracker, experiment\n\nwith experiment.record(name='sample', token='http://localhost:5005/api/v1/track?'):\n for i in range(50):\n loss, accuracy = train()\n tracker.save(i, {'loss': loss, 'accuracy': accuracy})\n```\n",
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