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# NOTE: This is a sample dockerfile for creating docker images for deploying
# a haystack pipeline. Follow the comments and make suitable changes for your use-case.
#
# Use-case showcased here:
# Dockerfile for a <blah> pipeline
#
#
# We also show how to cache HuggingFace models; both public and private. More details in the comments.
# CAUTION: Do not use `huggingface-cli login` inside the docker as it store the access token locally
# Here we prefer passing access token as an `ARG` because,
# we only need to use access token to cache required model.
# Also, Do not create an ENV variable containing access token,
# as ENV variable remains active inside docker for its entire lifecycle.
# To know futher: https://huggingface.co/docs/hub/security-tokens#best-practices
# Choose appropriate Haystack base image (i.e. v1.13.2)
ARG HAYSTACK_BASE_IMAGE=deepset/haystack:cpu-v1.13.2
FROM $HAYSTACK_BASE_IMAGE
#ARG hf_model_names="['deepset/minilm-uncased-squad2']"
# `hf_model_names` should be a list of string containing model names from HuggingFace hub
# i.e., "['hf/model1']" or "['hf/model1', 'hf/model2', 'hf/model3']"
#ARG hf_token=''
# To cache HuggingFace public models
#RUN python3 -c "from haystack.utils.docker import cache_models;cache_models($hf_model_names)"
# To cache HuggingFace private models
#RUN python3 -c "from haystack.utils.docker import cache_models;cache_models($hf_model_names, $hf_token)"
# To copy pipeline yml into the docker
ARG repo_pipeline_path=retriever_reader.yml
ARG container_pipeline_path=/opt/pipelines/pipeline.yml
COPY $repo_pipeline_path $container_pipeline_path
# Exporting Pipeline path as an env variable
# Haystack reads this env variable to load the appropriate pipeline
ENV PIPELINE_YAML_PATH=$container_pipeline_path
RUN chmod 700 /opt/file-upload
# cmd for starting Haystack API server
CMD ["gunicorn", "rest_api.application:app", "-b", "0.0.0.0:7860", "-k", "uvicorn.workers.UvicornWorker", "--workers", "1", "--timeout", "180"]