Metadata-Version: 2.1
Name: gobbli
Version: 0.0.2
Summary: Uniform interface to deep learning approaches via Docker containers.
Home-page: https://github.com/RTIInternational/gobbli/
Author: RTI International
Maintainer: Jason Nance
License: LICENSE.txt
Description: <div align="center">
          <div>
            <img src="https://raw.githubusercontent.com/RTIInternational/gobbli/master/img/gobbli_lg.svg?sanitize=true" alt="gobbli logo" width="200" />
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          <div>
            <a href="https://travis-ci.com/RTIInternational/gobbli"><img src="https://travis-ci.com/RTIInternational/gobbli.svg?branch=master" alt="PyPI version"></a>
            <a href="https://badge.fury.io/py/gobbli"><img src="https://badge.fury.io/py/gobbli.svg" alt="PyPI version"></a>
            <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/gobbli" />
            <a href="https://doi.org/10.5281/zenodo.3387610"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3387610.svg" alt="DOI"></a>
          </div>
        </div>
        
        This is a library designed to provide a uniform interface to various deep learning models for text via programmatically created Docker containers.
        
        ## Usage
        
        See [the docs](https://gobbli.readthedocs.io/en/latest/) for prerequisites, a quickstart, and the API reference.  In brief, you need [Docker](https://www.docker.com/) installed with appropriate permissions for your user account to run Docker commands and Python 3.7.  Then run the following:
        
            pip install gobbli
        
        You may also want to check out the [benchmarks](./benchmark) to see some comparisons of gobbli's implementation of various models in different situations.
        
        ## Development
        
        Assuming you have all prerequisites noted above, you need to install the package and all required + optional dependencies in development mode:
        
            pip install -e ".[augment,tokenize]"
            
        Install additional dev dependencies:
        
            pip install -r requirements.txt
            
        Run linting, autoformatting, and tests:
        
            ./run_ci.sh
            
        To avoid manually fixing some of these errors, consider enabling [isort](https://github.com/timothycrosley/isort) and [black](https://github.com/python/black) support in your favorite editor.
        
        If you're running tests in an environment with less than 12GB of memory, you'll want to pass the `--low-resource` argument when running tests to avoid out of memory errors.
            
        **NOTE:** If running on a Mac, even with adequate memory available, you may encounter Out of Memory errors (exit status 137) when running the tests.  This is due to not enough memory being allocated to your Docker daemon.  Try going to Docker for Mac -> Preferences -> Advanced and raising "Memory" to 12GiB or more.
        
        If you want to run the tests GPU(s) enabled, see the `--use-gpu` and `--nvidia-visible-devices` arguments under `py.test --help`.  If your local machine doesn't have an NVIDIA GPU, but you have access to one that does via SSH, you can use the `test_remote_gpu.sh` script to run the tests with GPU enabled over SSH.
        
        ### Docs
        
        To generate the docs, install the docs requirements:
        
            pip install -r docs/requirements.txt
            
        Since doc structure is auto-generated from the library, you must have the library (and all its dependencies) installed as well.
        
        Then, run the following from the repository root:
            
            ./generate_docs.sh
            
        Then browse the generated documentation in `docs/_build/html`.
        
            
        ## Attribution
        
        gobbli wouldn't exist without the public release of several state-of-the-art models.  The library incorporates:
        
        - [BERT](https://github.com/google-research/bert), released by Google
        - [MT-DNN](https://github.com/namisan/mt-dnn), released by Microsoft
        - [Universal Sentence Encoder](https://tfhub.dev/google/universal-sentence-encoder/2), released by Google
        - [fastText](https://github.com/facebookresearch/fastText), released by Facebook
        - [pytorch_transformers](https://github.com/huggingface/pytorch-transformers), released by Hugging Face
        
        Original work on the library was funded by [RTI International](https://www.rti.org/).
        
        Logo design by [Marcia Underwood](http://marciaunderwood.com).
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: augment
Provides-Extra: tokenize
