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# We need this to use GPUs inside the container
FROM nvidia/cuda:11.2.2-base
# Using a multi-stage build simplifies the s3prl installation
# TODO: Find a slimmer base image that also "just works"
FROM tiangolo/uvicorn-gunicorn:python3.8
RUN apt-get update --fix-missing && apt-get install -y wget \
libsndfile1 \
sox \
git \
git-lfs
RUN python -m pip install --upgrade pip
RUN python -m pip --no-cache-dir install fairseq@git+https://github.com//pytorch/fairseq.git@f2146bdc7abf293186de9449bfa2272775e39e1d#egg=fairseq
RUN python -m pip --no-cache-dir install git+https://github.com/s3prl/s3prl.git#egg=s3prl
COPY s3prl/ /app/s3prl
COPY src/ /app/src
# Setup filesystem
RUN mkdir /app/data
# Configure Git
# TODO: Create a dedicated SUPERB account for the project?
RUN git config --global user.email "[email protected]"
RUN git config --global user.name "SUPERB Admin"
# Default args for fine-tuning
ENV upstream_model osanseviero/hubert_base
ENV downstream_task asr
ENV hub huggingface
ENV hf_hub_org None
ENV push_to_hf_hub True
ENV override None
WORKDIR /app/s3prl
# Each task's config.yaml is used to set all the training parameters, but can be overridden with the `override` argument
# The results of each training run are stored in /app/s3prl/result/downstream/{downstream_task}
# and pushed to the Hugging Face Hub with name:
# Default behaviour - {hf_hub_username}/superb-s3prl-{upstream_model}-{downstream_task}-uuid
# With hf_hub_org set - {hf_hub_org}/superb-s3prl-{upstream_model}-{downstream_task}-uuid
CMD python run_downstream.py -n ${downstream_task} -m train -u ${upstream_model} -d ${downstream_task} --hub ${hub} --hf_hub_org ${hf_hub_org} --push_to_hf_hub ${push_to_hf_hub} --override ${override} |