Metadata-Version: 2.1
Name: labml
Version: 0.4.65
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: .. image:: https://badge.fury.io/py/labml.svg
            :target: https://badge.fury.io/py/labml
        .. image:: https://pepy.tech/badge/labml
            :target: https://pepy.tech/project/labml
        
        LabML
        =====
        
        LabML lets you monitor AI model training on mobile phones.
        
        .. image:: https://raw.githubusercontent.com/vpj/lab/master/images/mobile.png
           :width: 50%
           :alt: Mobile view 
        
        You can install this package using PIP.
        
        .. code-block:: console
        
            pip install labml
        
        
        To push to mobile website, you need obtain a token from `web.lab-ml.com <https://web.lab-ml.com>`_
        (Github `lab-ml/app <https://github.com/lab-ml/app/>`_), and save statistics with ``tracker.save``.
        
        PyTorch example
        ^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            from labml import tracker, experiment
          
            with experiment.record(name='sample', exp_conf=conf, token: 'TOKEN from web.lab-ml.com'):
                for i in range(50):
                    loss, accuracy = train()
                    tracker.save(i, {'loss': loss, 'accuracy': accuracy})
        
        TensorFlow 2.X Keras example
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            from labml import experiment
            from labml.utils.keras import LabMLKerasCallback
          
            with experiment.record(name='sample', exp_conf=conf, token: 'TOKEN from web.lab-ml.com'):
                for i in range(50):
                    model.fit(x_train, y_train, epochs=conf['epochs'], validation_data=(x_test, y_test),
                              callbacks=[LabMLKerasCallback()], verbose=None)
        
        You can read the guides about creating an  `experiment <http://lab-ml.com/guide/experiment.html>`_,
        and saving statistics with `tracker <http://lab-ml.com/guide/tracker.html>`_ for details.
        
        It automatically pushes data to Tensorboard, and you can keep your old experiments organized with the 
        `LabML Dashboard <https://github.com/lab-ml/dashboard/>`_
        
        .. image:: https://raw.githubusercontent.com/lab-ml/dashboard/master/images/screenshots/dashboard_table.png
           :width: 100%
           :alt: Dashboard Screenshot
        
        All these software is 100% open source,
        and your logs will be stored locally for Tensorboard and `LabML Dashboard <https://github.com/lab-ml/dashboard/>`_.
        You will only be sending data away for `web.lab-ml.com <https://web.lab-ml.com>`_ if you include a token url.
        This can also be `locally installed <https://github.com/lab-ml/app/>`_.
        
        LabML can also keep track of git commits,
        handle `configurations, hyper-parameters <http://lab-ml.com/guide/configs.html>`_,
        save and load `checkpoints <http://lab-ml.com/guide/experiment.html>`_,
        and providing pretty logs.
        
        .. image:: https://raw.githubusercontent.com/vpj/lab/master/images/logger_sample.png
           :width: 50%
           :alt: Logger output
        
        We also have an `API <https://lab-ml.com/guide/analytics.html>`_
        to create `custom <https://github.com/lab-ml/samples/blob/master/labml_samples/pytorch/stocks/analysis.ipynb>`_
        `visualizations <https://github.com/vpj/poker/blob/master/kuhn_cfr/kuhn_cfr.ipynb>`_
        from artifacts and logs on Jupyter notebooks.
        
        .. image:: https://raw.githubusercontent.com/vpj/lab/master/images/analytics.png
           :width: 50%
           :alt: Analytics
        
        Links
        -----
        
        `💬 Slack workspace for discussions <https://join.slack.com/t/labforml/shared_invite/zt-egj9zvq9-Dl3hhZqobexgT7aVKnD14g/>`_
        
        `📗 Documentation <http://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.
        
        .. code-block:: 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/x-rst
