Metadata-Version: 2.1
Name: labml
Version: 0.4.129
Summary: Organize Machine Learning Experiments
Home-page: https://github.com/labml.ai/labml
Author: Varuna Jayasiri, Nipun Wijerathne
Author-email: vpjayasiri@gmail.com, hnipun@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://docs.labml.ai/
Description: <div align="center" style="margin-bottom: 100px;">
        
        <h1>Monitor deep learning model training and hardware usage from mobile.</h1>
        
        [![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)
        [![Docs](https://img.shields.io/badge/labml-docs-blue)](https://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>
        
        ### 🔥 Features
        
        * Monitor running experiments from [mobile phone](https://github.com/labmlai/labml/tree/master/app) (or laptop)
        [![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/9e7f39e047e811ebbaff2b26e3148b3d)
        * Monitor [hardware usage on any computer](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md) with a single command
        * 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
        * 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/labmlai/labml/blob/master/samples/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
        * [Change hyper-parameters while the model is training](https://github.com/labmlai/labml/blob/master/guides/dynamic_hyperparameters.md)
        * 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
        
        [![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})
        ```
        
        ### PyTorch Lightning example
        
        
        [![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)
        
        ```
        
        
        ### TensorFlow 2.X Keras example
        
        [![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)
        ```
        
        ### 📚 Documentation
        
        * [Python API Reference](https://docs.labml.ai)
        * [Samples](https://github.com/labmlai/labml/tree/master/samples)
        
        ##### Guides
        
        * [API to create experiments](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/experiment.ipynb)
        * [Track training metrics](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/tracker.ipynb)
        * [Monitored training loop and other iterators](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/monitor.ipynb)
        * [API for custom visualizations](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/analytics.ipynb)
        * [Configurations management API](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/configs.ipynb)
        * [Logger for stylized logging](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/logger.ipynb)
        
        ### 🖥 Screenshots
        
        #### 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" alt="Analytics"/>
        </div>
        
        ## Tools
        
        ### [Hosting your own experiments server](https://github.com/labmlai/labml/tree/master/app)
        
        ```sh
        # Install the package
        pip install labml-app
        
        # Start the server
        
        labml app-server
        ```
        
        
        ### [Training models on cloud](https://github.com/labmlai/labml/tree/master/remote)
        
        ```bash
        # Install the package
        pip install labml_remote
        
        # Initialize the project
        labml_remote init
        
        # Add cloud server(s) to .remote/configs.yaml
        
        # Prepare the remote server(s)
        labml_remote prepare
        
        # Start a PyTorch distributed training job
        labml_remote helper-torch-launch --cmd 'train.py' --nproc-per-node 2 --env GLOO_SOCKET_IFNAME enp1s0
        ```
        
        ### [Monitoring hardware usage](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md)
        
        ```sh
        # Install packages and dependencies
        pip install labml psutil py3nvml
        
        # Start monitoring
        labml monitor
        ```
        
        ## Other Guides
        
        #### [Setting up a local Ubuntu workstation for deep learning](https://github.com/labmlai/labml/blob/master/guides/local-ubuntu.md)
        
        #### [Setting up a local cloud computer for deep learning](https://github.com/labmlai/labml/blob/master/guides/remote-python.md)
        
        ## Citing
        
        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.ai: A library to organize machine learning experiments},
         year = {2020},
         url = {https://labml.ai/},
        }
        ```
        
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
