full-guide-wsl-docker-segment-geospatial-gpu-pixienv

full-guide-wsl-docker-segment-geospatial-gpu-pixienv.sh

###### WSL2 DEBIAN SETUP #####
# wsl --install Debian
# wsluser
# password

echo cd ~ >> ~/.bashrc && source ~/.bashrc

sudo nano /etc/wsl.conf
```wsl.conf
[boot]
systemd=true
[automount]
enabled=false
[interop]
appendWindowsPath=false
[user]
default=wsluser
```

exit

# wsl --terminate Debian
# wsl --shutdown
# wsl --export Debian E:\WSL\_Backup\Debian-InitialStart.tar
# wsl --unregister Debian
# wsl --import wslname E:\WSL\wslname\ E:\WSL\_Backup\Debian-InitialStart.tar
# wsl -d wslname

mkdir -p ~/dockerstuff

sudo nano /etc/fstab
```fstab
E:\\WSL\\_dockerstuff /home/wsluser/dockerstuff drvfs rw,uid=1000,gid=1000,metadata 0 0
```

exit
# wsl --terminate wslname
# wsl -d wslname

cd ~/dockerstuff
mkdir vol
mkdir compose
mkdir images
mkdir build
cd ~

# install necessary packages for Debian WSL
sudo apt-get update -y && sudo apt-get upgrade -y && sudo apt-get install -y wget curl unzip zstd pigz gpg git iptables uidmap dbus-user-session

exit
# wsl --terminate wslname
# wsl -d wslname

systemctl --user
# Ctrl+C (Close)
##############################


##### DOCKER ROOTLESS INSTALLATION #####
curl -fsSL https://get.docker.com/rootless | sh
sudo loginctl enable-linger wsluser
echo -e '\nexport PATH=/home/wsluser/bin:$PATH\nexport DOCKER_HOST=unix:///run/user/1000//docker.sock' >> ~/.bashrc && source ~/.bashrc
mkdir -p ~/.docker/cli-plugins/
LATEST_RELEASE=$(curl -s https://api.github.com/repos/docker/compose/releases/latest | grep -oP '"tag_name": "\K(.*)(?=")')
curl -SL "https://github.com/docker/compose/releases/download/${LATEST_RELEASE}/docker-compose-linux-x86_64" -o ~/.docker/cli-plugins/docker-compose
chmod +x ~/.docker/cli-plugins/docker-compose
# if needed to map lower number ports
sudo setcap cap_net_bind_service=ep $HOME/bin/rootlesskit

docker --version
docker compose version
########################################


##### NVIDIA DOCKER CONTAINER TOOLKIT #####
## necessary for container to harnessing gpu capabilities of the host ##
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
nvidia-ctk runtime configure --runtime=docker --config=$HOME/.config/docker/daemon.json
systemctl --user restart docker
###########################################


##### CREATING DOCKERFILE FOR SEGMENT-GEOSPATIAL #####
cd ~/dockerstuff
cd build
mkdir samgeo
cd samgeo
nano Dockerfile
```Dockerfile
# change to another nvidia cuda image base if needed
# https://catalog.ngc.nvidia.com/orgs/nvidia/containers/cuda/tags
FROM nvcr.io/nvidia/cuda:12.6.3-base-ubuntu24.04
# Install system dependencies
RUN apt-get update -y && apt-get upgrade -y && \
    apt-get install -y --reinstall nano wget curl gpg git libgl1 libglib2.0-0 libsm6 libxext6 libxrender-dev sqlite3 && \
    apt-get autoclean && apt-get clean && rm -rf /var/lib/apt/lists/*

# Install pixi using the standalone installer
RUN curl -fsSL https://pixi.sh/install.sh | bash

# Ensure pixi is added to PATH
ENV PATH="/root/.pixi/bin:$PATH"

# Setup workspace and examples
WORKDIR /root
RUN git clone https://github.com/opengeos/segment-geospatial.git && \
    mkdir workspace && \
    mv /root/segment-geospatial/docs/examples /root/workspace/examples && \
    rm -fr /root/segment-geospatial

# Setup pixi environment with python 3.12.7
WORKDIR /root/workspace
RUN pixi init && pixi add "python=3.12.7"

# Pin to python 3.12.7
RUN printf '[tool.pixi]\nrequires-python = "==3.12.7"\n\n' > pyproject.toml

# Configure additional PyPI index URL for PyTorch
RUN printf '[pypi-options]\nextra-index-urls = ["https://download.pytorch.org/whl/cu124"]' >> pyproject.toml

# Add GDAL to pixi env
RUN pixi add gdal

# Add SQLite
RUN pixi add sqlite

# Add pip dependencies for installation in pixi env
RUN pixi add --pypi torch torchvision && \
    pixi add --pypi groundingdino-py segment-anything-fast jupyter jupyter-server-proxy && \
    pixi add --pypi segment-geospatial

# Cleaning up caches
RUN pixi clean cache -y && \
    find /root/.cache -mindepth 1 -delete

# Set environment variables
ARG LOCALTILESERVER_CLIENT_PREFIX='proxy/{port}'
ENV LOCALTILESERVER_CLIENT_PREFIX=$LOCALTILESERVER_CLIENT_PREFIX

# Activate jupyter extension and its jupyter server proxy config
WORKDIR /root/workspace
RUN pixi run jupyter server extension enable --sys-prefix jupyter_server_proxy

# Uncomment two lines below to immediately activate pixi env and running jupyter lab at port 8888
# EXPOSE 8888
# CMD ["pixi", "run", "jupyter", "lab", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"]
```
######################################################


##### CREATING IMAGE FROM DOCKERFILE #####
# docker pull nvcr.io/nvidia/cuda:12.6.2-base-ubuntu24.04
docker build -t segment-geospatial-cuda12 .
##########################################


##### EXPORT THE IMAGE TO TAR BACKUP #####
docker save segment-geospatial-cuda12 | gzip > ~/dockerstuff/images/seggeo-cuda12.tar.gz
##########################################


##### REMOVE THE IMAGE #####
docker rmi segment-geospatial-cuda12
############################


##### IMPORT IMAGE FROM TAR BACKUP #####
docker load -i ~/dockerstuff/images/seggeo-cuda12.tar.gz
docker images
# if the name of docker image is different
# docker tag xxx segment-geospatial-cuda1262-cudnn-rt-ubuntu2404:latest
########################################

##### RUNNING SEGMENT-GEOSPATIAL CONTAINER #####
## setup the folders ##

# cd ~
# mkdir dockerstuf
cd ~/dockerstuff
cd vol
mkdir samgeo
cd samgeo
mkdir data
mkdir cache
mkdir notebooks
mkdir jupyter

chown -R $USER:$USER ~/dockerstuff/vol/samgeo/cache ~/dockerstuff/vol/samgeo/data ~/dockerstuff/vol/samgeo/notebooks
chmod -R 775 ~/dockerstuff/vol/samgeo/cache ~/dockerstuff/vol/samgeo/data ~/dockerstuff/vol/samgeo/notebooks

cd ~/dockerstuff/vol/samgeo

## to run the container ##
docker run -it --name segment-geospatial-cuda12 --gpus all \
    -v ~/dockerstuff/vol/samgeo/jupyter:/root/.jupyter \
    -v ~/dockerstuff/vol/samgeo/cache:/root/.cache \
    -v ~/dockerstuff/vol/samgeo/data:/root/workspace/data \
    -v ~/dockerstuff/vol/samgeo/notebooks:/root/workspace/notebooks \
    -p 8888:8888 \
    --restart unless-stopped \
    segment-geospatial-cuda12 \
    /bin/bash
    
## then from bash terminal inside container use:
pixi run jupyter lab password
# (create password, hashed key will be stored in ~/dockerstuff/vol/samgeo/jupyter)
pixi run jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root
http://127.0.0.1:8888/lab
# (press Ctrl+D to exit)

## to start it again:
docker start segment-geospatial-cuda12 && docker exec -it segment-geospatial-cuda12 bash
pixi run jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root
http://127.0.0.1:8888/lab
# (press Ctrl+D to exit)

## to use bash only and activate pixi environment for running python and others:
docker start segment-geospatial-cuda12 && docker exec -it segment-geospatial-cuda12 bash
pixi shell
# (run your python command etc)
# (press Ctrl+D to exit, twice)

## to stop the container:
docker stop segment-geospatial-cuda12
################################################


##### TESTING CONTAINER FOR BASH OPTION #####
# pixi shell
python
```
import torch
torch.cuda.is_available()
torch.cuda.get_device_name(0)

import leafmap
from samgeo import SamGeo2, regularize
```
#############################################