transformers inference (for Weaviate)

This is the the inference container which is used by the Weaviate text2vec-transformers module. You can download it directly from Dockerhub using one of the pre-built images or built your own (as outlined below).

It is built in a way to support any PyTorch or Tensorflow transformers model, either from the Huggingface Model Hub or from your disk.

This makes this an easy way to deploy your Weaviate-optimized transformers NLP inference model to production using Docker or Kubernetes.

Documentation

Documentation for this module can be found here.

Choose your model

Pre-built images

You can download a selection of pre-built images directly from Dockerhub. We have chosen publically available models that in our opinion are well suited for semantic search.

The pre-built models include:

The above image names always point to the latest version of the inference container including the model. You can also make that explicit by appending -latest to the image name. Additionally, you can pin the version to one of the existing git tags of this repository. E.g. to pin distilbert-base-uncased to version 1.0.0, you can use semitechnologies/transformers-inference:distilbert-base-uncased-1.0.0.

Your favorite model is not included? Open a pull-request to include it or build a custom image as outlined below.

Custom build with any huggingface model

You can build a docker image which supports any model from the huggingface model hub with a two-line Dockerfile.

In the following example, we are going to build a custom image for the distilroberta-base model.

Create a new Dockerfile (you do not need to clone this repository, any folder on your machine is fine), we will name it distilrobert.Dockerfile. Add the following lines to it:

FROM semitechnologies/transformers-inference:custom
RUN MODEL_NAME=distilroberta-base ./download.py

Now you just need to build and tag your Dockerfile, we will tag it as distilroberta-inference:

docker build -f distilroberta.Dockerfile -t distilroberta-inference .

That's it! You can now push your image to your favorite registry or reference it locally in your Weaviate docker-compose.yaml using the docker tag distilroberta-inference.

Custom build with a private / local model

You can build a docker image which supports any model which is compatible with Huggingface's AutoModel and AutoTokenzier.

In the following example, we are going to build a custom image for a non-public model which we have locally stored at ./my-model.

Create a new Dockerfile (you do not need to clone this repository, any folder on your machine is fine), we will name it my-model.Dockerfile. Add the following lines to it:

FROM semitechnologies/transformers-inference:custom
COPY ./my-model /app/models/model

The above will make sure that your model end ups in the image at /app/models/model. This path is important, so that the application can find the model.

Now you just need to build and tag your Dockerfile, we will tag it as my-model-inference:

docker build -f my-model.Dockerfile -t my-model-inference .

That's it! You can now push your image to your favorite registry or reference it locally in your Weaviate docker-compose.yaml using the docker tag my-model-inference.