# syntax=docker/dockerfile:1.7

# warning: this image is 21 GB
FROM nvcr.io/nvidia/pytorch:24.09-py3

# relevant links:
# https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html#running-a-workload-with-cdi
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-09.html#rel-24-09
#
# run the container with with --device nvidia.com/gpu=all
# e.g. TODO

ENV http_proxy=http://host.containers.internal:3128
ENV https_proxy=http://host.containers.internal:3128

COPY ../environment.yml /

# install conda
RUN mkdir -p /opt/conda
ADD https://repo.anaconda.com/miniconda/Miniconda3-py38_4.12.0-Linux-x86_64.sh /opt/conda/miniconda.sh
RUN bash /opt/conda/miniconda.sh -b -p /opt/miniconda
RUN /opt/miniconda/bin/conda init bash

RUN /opt/miniconda/bin/conda env create -f /environment.yml

RUN echo "conda activate minimum-k-cut" >> /root/.bashrc

WORKDIR /minimum-k-cut/src

# /minimum-k-cut/ (the entire git repo) will be mounted from docker compose
