Metadata-Version: 2.1
Name: labml-app
Version: 0.0.0
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;">
            
        <img src="https://raw.githubusercontent.com/lab-ml/lab/master/images/lab_logo.png" width="200px" alt="">
        
        <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.svg)](https://badge.fury.io/py/labml)
        [![PyPI Status](https://pepy.tech/badge/labml)](https://pepy.tech/project/labml)
        [![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://lab-ml.com/)
        [![Twitter](https://img.shields.io/twitter/url.svg?label=Follow%20%40LabML&style=social&url=https%3A%2F%2Ftwitter.com%2FLabML)](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)
        
        <img src="https://github.com/lab-ml/app/blob/master/images/cover.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](https://github.com/lab-ml/app/blob/master/docs/installation.rst).
        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. Thanks.
        
        ### 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 use it ?
        1. Install the [labml client library](https://github.com/lab-ml/labml).
        
        ```
        pip install labml
        ```
        
        2. Start pushing updates to the app  [with two lines of code](http://lab-ml.com/guide/tracker.html). Refer to the examples below.
        3. Click on the link printed in the terminal to open the app. [![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/9e7f39e047e811ebbaff2b26e3148b3d)
        
        ### Examples
        
        1. Pytorch [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Ldu5tr0oYN_XcYQORgOkIY_Ohsi152fz?usp=sharing) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/hnipun/monitoring-ml-model-training-on-your-mobile-phone)
        
        ```python
        from labml import tracker, experiment
        
        with experiment.record(name='sample', exp_conf=conf):
            for i in range(50):
                loss, accuracy = train()
                tracker.save(i, {'loss': loss, 'accuracy': accuracy})
        ```
        
        2. PyTorch Lightning [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/15aSPDwbKihDu_c3aFHNPGG5POjVlM2KO?usp=sharing) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/hnipun/pytorch-lightning)
        
        ```python
        from labml import experiment
        from labml.utils.lightening import LabMLLighteningLogger
        
        trainer = pl.Trainer(gpus=1, max_epochs=5, progress_bar_refresh_rate=20, logger=LabMLLighteningLogger())
        
        with experiment.record(name='sample', exp_conf=conf, disable_screen=True):
                trainer.fit(model, data_loader)
        
        ```
        
        3. TensorFlow 2.0 Keras [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lx1dUG3MGaIDnq47HVFlzJ2lytjSa9Zy?usp=sharing) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/hnipun/monitor-keras-model-training-on-your-mobile-phone)
        
        ```python
        from labml import experiment
        from labml.utils.keras import LabMLKerasCallback
        
        with experiment.record(name='sample', exp_conf=conf):
            for i in range(50):
                model.fit(x_train, y_train, epochs=conf['epochs'], validation_data=(x_test, y_test),
                          callbacks=[LabMLKerasCallback()], verbose=None)
        ```
        
        
        ### Citing LabML
        
        If you use LabML for academic research, please cite the library using the following BibTeX entry.
        
        ```bibtex
        @misc{labml,
         author = {Varuna Jayasiri, Nipun Wijerathne},
         title = {LabML: A library to organize machine learning experiments},
         year = {2020},
         url = {https://lab-ml.com/},
        }
        ```
        
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
