Metadata-Version: 2.1
Name: labml
Version: 0.4.106
Summary: Organize Machine Learning Experiments
Home-page: https://github.com/lab-ml/labml
Author: Varuna Jayasiri, Nipun Wijerathne
Author-email: vpjayasiri@gmail.com, hnipun@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://lab-ml.com/
Description: <div align="center" style="margin-bottom: 100px;">
        <h1>LabML</h1>
        <h2>Organize machine learning experiments and monitor training progress from mobile.</h2>
        
        <img src="https://raw.githubusercontent.com/lab-ml/lab/master/images/lab_logo.png" width="150" alt="">
        
        [![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)
        [![Join 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)](https://lab-ml.com/)
        [![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)
        
        <img src="https://github.com/lab-ml/lab/blob/master/images/cover.png" alt=""/>
        </div>
        
        
        ### 🔥 Features
        
        * Monitor running experiments from [mobile phone](https://github.com/lab-ml/app)
        [![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/9e7f39e047e811ebbaff2b26e3148b3d)
        * Integrate with just 2 lines of code (see examples below)
        * Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
        * Keep Tensorboard logs organized
        * [Dashboard](https://github.com/lab-ml/dashboard/) to locally browse and manage experiment runs
        * Save and load checkpoints
        * API for custom visualizations
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lab-ml/samples/blob/master/labml_samples/pytorch/stocks/analysis.ipynb)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vpj/poker/blob/master/kuhn_cfr/kuhn_cfr.ipynb)
        * Pretty logs of training progress
        * Open source! we also have a small hosted server for the mobile web app
        
        
        ### Installation
        
        You can install this package using PIP.
        
        ```bash
        pip install labml
        ```
        
        ### PyTorch example
        
        ```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})
        ```
        
        ### PyTorch Lightning example
        
        ```python
        from labml import experiment
        from labml.utils.lightning import LabMLLightningLogger
        
        trainer = pl.Trainer(gpus=1, max_epochs=5, progress_bar_refresh_rate=20, logger=LabMLLightningLogger())
        
        with experiment.record(name='sample', exp_conf=conf, disable_screen=True):
            trainer.fit(model, data_loader)
        ```
        
        ### TensorFlow 2.X Keras example
        
        ```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)
        ```
        
        
        ### 📚 Documentation
        
        * [API to create experiments](https://lab-ml.com/guide/experiment.html)
        * [Track training metrics](https://lab-ml.com/guide/tracker.html)
        * [Monitored training loop and other iterators](https://lab-ml.com/guide/monit.html)
        * [API for custom visualizations](https://lab-ml.com/guide/analytics.html)
        * [Configurations management API](https://lab-ml.com/guide/configs.html)
        * [Logger for stylized logging](https://lab-ml.com/guide/logger.html)
        
        ### 🖥 Screenshots
        
        #### Dashboard
        
        <div align="center">
            <img src="https://raw.githubusercontent.com/lab-ml/dashboard/master/images/screenshots/dashboard_table.png" alt="Dashboard Screenshot"/>
        </div>
        
        #### Formatted training loop output
        
        <div align="center">
            <img src="https://raw.githubusercontent.com/vpj/lab/master/images/logger_sample.png" alt="Sample Logs"/>
        </div>
        
        #### Custom visualizations based on Tensorboard logs
        
        <div align="center">
            <img src="https://raw.githubusercontent.com/vpj/lab/master/images/analytics.png" width="500" alt="Analytics"/>
        </div>
        
        ## Links
        
        [💬 Slack workspace for discussions](https://join.slack.com/t/labforml/shared_invite/zt-egj9zvq9-Dl3hhZqobexgT7aVKnD14g/)
        
        [📗 Documentation](https://lab-ml.com/)
        
        [👨‍🏫 Samples](https://github.com/lab-ml/samples)
        
        
        ## Citing LabML
        
        If you use LabML for academic research, please cite the library using the following BibTeX entry.
        
        
        ```bibtext
        @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
