An Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.
The fastest way to get an environment to run AllenNLP is with Docker. Once you have installed Docker
docker run -it --rm allennlp/allennlp to get an environment that will run on either the cpu or gpu.
Now you can do any of the following:
- Run a model on example sentences with
- Start a web service to host our models with
- Interactively code against AllenNLP from the Python interpreter with
You can also install via the
pip package manager or by cloning this repository into a Python 3.6 virtualenv.
See below for more detailed instructions.
What is AllenNLP?
Built on PyTorch, AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP was designed with the following principles:
- Hyper-modular and lightweight. Use the parts which you like seamlessly with PyTorch.
- Extensively tested and easy to extend. Test coverage is above 90% and the example models provide a template for contributions.
- Take padding and masking seriously, making it easy to implement correct models without the pain.
- Experiment friendly. Run reproducible experiments from a json specification with comprehensive logging.
AllenNLP includes reference implementations of high quality models for Semantic Role Labelling, Question and Answering (BiDAF), Entailment (decomposable attention), and more.
AllenNLP is built and maintained by the Allen Institute for Artificial Intelligence, in close collaboration with researchers at the University of Washington and elsewhere. With a dedicated team of best-in-field researchers and software engineers, the AllenNLP project is uniquely positioned to provide state of the art models with high quality engineering.
|allennlp||an open-source NLP research library, built on PyTorch|
|allennlp.commands||functionality for a CLI and web service|
|allennlp.data||a data processing module for loading datasets and encoding strings as integers for representation in matrices|
|allennlp.models||a collection of state-of-the-art models|
|allennlp.modules||a collection of PyTorch modules for use with text|
|allennlp.nn||tensor utility functions, such as initializers and activation functions|
|allennlp.service||a web server to serve our demo and API|
|allennlp.training||functionality for training models|
Setting up a Conda development environment
Conda will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run AllenNLP.
Change your directory to your clone of AllenNLP.
Create a Conda environment with Python 3.6
conda create -n allennlp python=3.6
Now activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use AllenNLP.
source activate allennlp
Install the required dependencies.
Visit http://pytorch.org/ and install the relevant pytorch package.
You should now be able to test your installation with
pytest -v. Congratulations!
Installing via pip
AllenNLP also has a pip package if you wish to use allennlp as a library. Install with:
pip install allennlp
This installation method is still a little experimental. Please open an issue if you encounter issues after following the instructions to create a virtual environment above.
Setting up a Docker development environment
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.
Downloading a pre-built Docker image
It is easy to run a pre-built Docker development environment. AllenNLP is configured with Docker Cloud to build a new image on every update to the master branch. To download an image from Docker Hub:
docker pull allennlp/allennlp:latest
Building a Docker image
Following are instructions on creating a Docker environment that works on a CPU or GPU. The following command will take some time, as it completely builds the environment needed to run AllenNLP.
docker build --tag allennlp/allennlp .
You should now be able to see this image listed by running
docker images allennlp.
REPOSITORY TAG IMAGE ID CREATED SIZE allennlp/allennlp latest b66aee6cb593 5 minutes ago 2.38GB
Running the Docker image
You can run the image with
docker run --rm -it allennlp/allennlp. The
--rm flag cleans up the image on exit and the
-it flags make the session interactive so you can use the bash shell the Docker image starts.
The Docker environment uses Conda to install Python and automatically enters the Conda environment "allennlp".
You can test your installation by running
Setting up a Kubernetes development environment
Kubernetes will deploy your Docker images into the cloud, so you can have a reproducible development environment on AWS.
kubectlto connect to your Kubernetes cluster.
kubectl create -f /path/to/kubernetes-dev-environment.yaml. This will create a "job" on the cluster which you can later connect to using bash. Note that you will be using the last Dockerfile that would pushed, and so the source code may not match what you have locally.
Retrieve the name of the pod created with
kubectl describe job <JOBNAME> --namespace=allennlp. The pod name will be your job name followed by some additional characters.
Get a shell inside the container using
kubectl exec -it <PODNAME> bash
When you are done, don't forget to kill your job using
kubectl delete -f /path/to/kubernetes-dev-environment.yaml
AllenNLP is an open-source project backed by the Allen Institute for Artificial Intelligence (AI2). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering. To learn more about who specifically contributed to this codebase, see our contributors page.