How to edit mounted files in a devcontainer? - docker

Question

I'm struggling to setup a dev container for my project. Initially, the setup was fine. However, when I edited files from inside the container, I could no longer modify them outside of the container because the user inside the container was a root user.
So the, I modified my Dockerfile to build my project inside of a user directory. The issue is now, I can't edit the mounted source code because I lack the necessary permissions inside the container.
Here is my (new) Dockerfile:
# We build everything from the perspective of the user
# This lets us edit mounted files from inside the container using
# the VsCode Remote Container extension w/o setting their permissions to root
FROM nvidia/cuda:11.6.1-devel-ubuntu20.04
# Prevents apt from giving prompts
# Set as ARG so it does not persist after build
# https://serverfault.com/questions/618994/when-building-from-dockerfile-debian-ubuntu-package-install-debconf-noninteract
ARG DEBIAN_FRONTEND=noninteractive
# Docker docs: https://docs.docker.com/develop/develop-images/dockerfile_best-practices/
# In addition, when you clean up the apt cache by removing /var/lib/apt/lists it reduces the image size, since the apt cache is not stored in a layer.
# Since the RUN statement starts with apt-get update, the package cache is always refreshed prior to apt-get install.
# Install all apt dependencies before we switch to non-root
RUN apt update && apt install curl \
# for bulding torchvision
git ninja-build -y && rm -rf /var/lib/apt/lists/*
# These commands are for the VsCode Docker extension
RUN adduser --system --group user
USER user
WORKDIR /home/user
# Install miniconda.sh
ENV PATH="/home/user/miniconda3/bin:${PATH}"
RUN curl \
https://repo.anaconda.com/miniconda/Miniconda3-py38_4.12.0-Linux-x86_64.sh -o miniconda.sh \
&& mkdir /home/user/.conda \
&& bash miniconda.sh -b \
&& rm miniconda.sh
COPY ./environment.yml ./environment.yml
RUN conda env create -f environment.yml && rm ./environment.yml
# pip throws a warning "don't run pip as root", but it's not actually running as root -- so ignore it
# (you can check by attaching to the container & running `pip list`)
COPY ./requirements.txt ./requirements.txt
RUN conda run --no-capture-output -n video-rec pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu116 && rm ./requirements.txt
# This is a hack, will figure it out later
# RUN conda run --no-capture-output -n video-rec pip install 'mosiacml[all]'
# TODO: Pillow SIMD for fast image augmentations
# Uncomment + remove FFMPEG from environment.yml if using GPU decoding
# Compile nv-codec
# RUN git clone --depth 1 --branch n11.1.5.1 https://git.videolan.org/git/ffmpeg/nv-codec-headers.git && \
# cd nv-codec-headers && \
# make install -j 100
# Build FFMPEG with Nvidia, torch requires FFMPEG 4.2 (I think)
# RUN git clone --depth 1 --branch n4.2.7 https://git.ffmpeg.org/ffmpeg.git ffmpeg/ \
# && cd ffmpeg && \
# ./configure --enable-nonfree --enable-shared --enable-cuda-nvcc --enable-libnpp --extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 \
# # I need this, I believe this generates code that works for A100s
# # See: https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/
# # SM80 or SM_80, compute_80 => NVIDIA A100
# # SM76 => Tesla GA10x cards, RTX Ampere – RTX 3080, GA102 – RTX 3090, RTX A2000, A3000, RTX A4000, A5000, A6000, NVIDIA A40, GA106 – RTX 3060, GA104 – RTX 3070, GA107 – RTX 3050, RTX A10, RTX A16, RTX A40, A2 Tensor Core GPU
# --nvccflags="-gencode arch=compute_80,code=sm_80 -O2" \
# && make -j 100 \
# && make install
# torchvision (FFMPEG dependency is installed through conda)
RUN git clone --depth 1 --branch v0.13.1 https://github.com/pytorch/vision.git vision \
&& cd vision \
# remove the existing torchvision
&& conda run --no-capture-output -n video-rec pip uninstall --yes torchvision \
&& conda run --no-capture-output -n video-rec python3 setup.py install
# Set a default shell for the Dev container extension
ENV SHELL /bin/bash
and here is my devcontainer.json:
{
"dockerComposeFile": "../commands/develop/docker-compose.yml",
"service": "develop",
"workspaceFolder": "/home/user/app",
"shutdownAction": "stopCompose",
"updateRemoteUserUID": true
}
Lastly, here is my compose file:
version: "3.8"
services:
develop:
build: ../..
stdin_open: true
volumes:
- ../..:/home/user/app
- ${DATA_DIR}:/data
environment:
WANDB_API_KEY: ${WANDB_API_KEY}
user: user
# This removes a certain memory restriction ??
# w/o the container crashes
ipc: host
deploy:
resources:
reservations:
devices:
- capabilities: [gpu]
What can I do to let me edit files on the host machine from inside my dev container without ruining their permissions?