Metadata-Version: 2.1
Name: allennlp-models
Version: 2.0.1
Summary: Officially supported models for the AllenNLP framework
Home-page: https://github.com/allenai/allennlp-models
Author: Allen Institute for Artificial Intelligence
Author-email: allennlp@allenai.org
License: Apache
Description: <div align="center">
            <br>
            <img src="https://raw.githubusercontent.com/allenai/allennlp/main/docs/img/allennlp-logo-dark.png" width="400"/>
            <p>
            Officially supported AllenNLP models.
            </p>
            <hr/>
        </div>
        <p align="center">
            <a href="https://github.com/allenai/allennlp-models/actions">
                <img alt="Build" src="https://github.com/allenai/allennlp-models/workflows/CI/badge.svg?event=push&branch=main">
            </a>
            <a href="https://pypi.org/project/allennlp-models/">
                <img alt="PyPI" src="https://img.shields.io/pypi/v/allennlp-models">
            </a>
            <a href="https://github.com/allenai/allennlp-models/blob/main/LICENSE">
                <img alt="License" src="https://img.shields.io/github/license/allenai/allennlp-models.svg?color=blue&cachedrop">
            </a>
            <a href="https://codecov.io/gh/allenai/allennlp">
                <img alt="Codecov" src="https://codecov.io/gh/allenai/allennlp/branch/main/graph/badge.svg">
            </a>
        </p>
        <br/>
        
        <div align="center">
        ❗️ To file an issue, please open a ticket on <a href="https://github.com/allenai/allennlp/issues/new/choose">allenai/allennlp</a> and tag it with "Models". ❗️
        </div>
        
        ## Installing
        
        ### From PyPI
        
        `allennlp-models` is available on PyPI. To install with `pip`, just run
        
        ```bash
        pip install --pre allennlp-models
        ```
        
        Note that the `allennlp-models` package is tied to the [`allennlp` core package](https://pypi.org/projects/allennlp-models). Therefore when you install the models package you will get the corresponding version of `allennlp` (if you haven't already installed `allennlp`). For example,
        
        ```bash
        pip install allennlp-models==1.0.0rc3
        pip freeze | grep allennlp
        # > allennlp==1.0.0rc3
        # > allennlp-models==1.0.0rc3
        ```
        
        ### From source
        
        If you intend to install the models package from source, then you probably also want to [install `allennlp` from source](https://github.com/allenai/allennlp#installing-from-source).
        Once you have `allennlp` installed, run the following within the same Python environment:
        
        ```bash
        git clone https://github.com/allenai/allennlp-models.git
        cd allennlp-models
        ALLENNLP_VERSION_OVERRIDE='allennlp' pip install -e .
        pip install -r dev-requirements.txt
        ```
        
        The `ALLENNLP_VERSION_OVERRIDE` environment variable ensures that the `allennlp` dependency is unpinned so that your local install of `allennlp` will be sufficient. If, however, you haven't installed `allennlp` yet and don't want to manage a local install, just omit this environment variable and `allennlp` will be installed from the main branch on GitHub.
        
        Both `allennlp` and `allennlp-models` are developed and tested side-by-side, so they should be kept up-to-date with each other. If you look at the GitHub Actions [workflow for `allennlp-models`](https://github.com/allenai/allennlp-models/actions), it's always tested against the main branch of `allennlp`. Similarly, `allennlp` is always tested against the main branch of `allennlp-models`.
        
        ### Using Docker
        
        Docker provides a virtual machine with everything set up to run AllenNLP--
        whether you will leverage a GPU or just run on a CPU.  Docker provides more
        isolation and consistency, and also makes it easy to distribute your
        environment to a compute cluster.
        
        Once you have [installed Docker](https://docs.docker.com/engine/installation/) you can either use a [prebuilt image from a release](https://hub.docker.com/r/allennlp/models) or build an image locally with any version of `allennlp` and `allennlp-models`.
        
        If you have GPUs available, you also need to install the [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) runtime.
        
        To build an image locally from a specific release, run
        
        ```bash
        docker build \
            --build-arg RELEASE=1.2.2 \
            --build-arg CUDA=10.2 \
            -t allennlp/models - < Dockerfile.release
        ```
        
        Just replace the `RELEASE` and `CUDA` build args what you need. Currently only CUDA 10.2 and 11.0 are officially supported.
        
        Alternatively, you can build against specific commits of `allennlp` and `allennlp-models` with
        
        ```bash
        docker build \
            --build-arg ALLENNLP_COMMIT=d823a2591e94912a6315e429d0fe0ee2efb4b3ee \
            --build-arg ALLENNLP_MODELS_COMMIT=01bc777e0d89387f03037d398cd967390716daf1 \
            --build-arg CUDA=10.2 \
            -t allennlp/models - < Dockerfile.commit
        ```
        
        Just change the `ALLENNLP_COMMIT` / `ALLENNLP_MODELS_COMMIT` and `CUDA` build args to the desired commit SHAs and CUDA versions, respectively.
        
        Once you've built your image, you can run it like this:
        
        ```bash
        mkdir -p $HOME/.allennlp/
        docker run --rm --gpus all -v $HOME/.allennlp:/root/.allennlp allennlp/models
        ```
        
        > Note: the `--gpus all` is only valid if you've installed the nvidia-docker runtime.
        
Keywords: allennlp NLP deep learning machine reading semantic parsing parsers
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6.1
Description-Content-Type: text/markdown
