Metadata-Version: 2.1
Name: labml_app
Version: 0.0.3
Summary: UNKNOWN
Home-page: https://github.com/lab-ml/app
Author: Varuna Jayasiri, Nipun, Aditya
Author-email: vpjayasiri@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://lab-ml.com/
Description: <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>
        
        
        [![PyPI - Python Version](https://badge.fury.io/py/labml-app.svg)](https://badge.fury.io/py/labml-app)
        [![PyPI Status](https://pepy.tech/badge/labml-app)](https://pepy.tech/project/labml-app)
        [![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/labforml/shared_invite/zt-egj9zvq9-Dl3hhZqobexgT7aVKnD14g/)
        [![Docs](https://img.shields.io/badge/labml-docs-blue)](http://docs.labml.ai/)
        [![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](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)
        
        You can host this on your own.
        We also have a small [AWS instance running](https://app.labml.ai). and you are welcome to use it. Please consider using your own installation if you are running lots of experiments.
        
        ### 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:5000/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:5000/api/v1/track?'):
            for i in range(50):
                loss, accuracy = train()
                tracker.save(i, {'loss': loss, 'accuracy': accuracy})
        ```
        
Keywords: machine learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
