himkt / allennlp-models Goto Github PK

Officially supported AllenNLP models.

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allennlp-models is available on PyPI. To install with pip, just run

pip install --pre allennlp-models

Note that the allennlp-models package is tied to the allennlp core package. Therefore when you install the models package you will get the corresponding version of allennlp (if you haven't already installed allennlp). For example,

pip install allennlp-models==1.0.0rc3
pip freeze | grep allennlp
# > allennlp==1.0.0rc3
# > allennlp-models==1.0.0rc3

If you intend to install the models package from source, then you probably also want to install allennlp from source. Once you have allennlp installed, run the following within the same Python environment:

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, it's always tested against the main branch of allennlp. Similarly, allennlp is always tested against the main branch of allennlp-models.

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 you can either use a prebuilt image from a release 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 runtime.

To build an image locally from a specific release, run

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

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:

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.