Add segmentation container and publish workflow

.github/workflows/publish-segmentation-image.yaml

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+name: Create and publish segmentation image
+
+on:
+  push:
+    branches: [ 'master' ]
+    paths:
+      - "segmentation/**"
+      - ".github/workflows/publish-segmentation-image.yaml"
+  workflow_dispatch:
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: als-computing/microct-segmentation
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+
+    - name: Log in to the Container registry
+      uses: docker/login-action@v3
+      with:
+        registry: ${{ env.REGISTRY }}
+        username: ${{ github.actor }}
+        password: ${{ secrets.GITHUB_TOKEN }}
+
+    - name: Extract metadata (tags, labels) for Docker
+      id: meta
+      uses: docker/metadata-action@v5
+      with:
+        images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+        tags: |
+          type=sha
+          type=raw,value=latest
+
+    - name: Build and push Docker image
+      uses: docker/build-push-action@v6
+      with:
+        context: ./segmentation
+        file: ./segmentation/Dockerfile
+        push: true
+        tags: ${{ steps.meta.outputs.tags }}
+        labels: ${{ steps.meta.outputs.labels }}

.gitignore

@@ -3,4 +3,4 @@ env/
 .env/
 .venv/
 ENV/
-.ENV/
+.ENV/.DS_Store

segmentation/.dockerignore

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+# Git
+.git
+.gitignore
+
+# Notebook checkpoints
+**/.ipynb_checkpoints/
+
+# Python environments
+env/
+.env/
+.venv/
+ENV/
+.ENV/
+
+# Model weights / checkpoints
+*.pth
+*.pt
+*.ckpt
+*.onnx
+*.h5
+
+# Local data and outputs
+data/
+models/
+outputs/
+runs/

segmentation/.gitignore

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+# model weights / checkpoints
+*.pth
+*.pt
+*.ckpt
+*.onnx
+*.h5
+
+# notebook checkpoints
+.ipynb_checkpoints/
+
+# local data locations (optional)
+data/
+models/
+outputs/
+runs/
+.DS_Store
+**/.DS_Store

segmentation/Dockerfile

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+# Most of this is from: https://gitlab.com/NERSC/nersc-official-images/-/blob/main/nersc/python/3.9-anaconda-2021.11/Dockerfile
+FROM docker.io/library/ubuntu:latest
+WORKDIR /opt
+
+RUN \
+    apt-get update && apt-get install --yes \
+    build-essential \
+    gfortran \
+    git \
+    wget \
+    libgl1 libegl1 libxext6 libsm6 libxrender1 && \
+    apt-get clean all && rm -rf /var/lib/apt/lists/*
+
+#miniforge
+# Download and install the latest Miniforge (comes with Mamba and conda-forge)
+RUN wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -O /tmp/miniforge.sh && \
+    bash /tmp/miniforge.sh -b -p /opt/miniconda && \
+    rm /tmp/miniforge.sh
+
+# Update PATH environment variable to include Miniforge
+ENV PATH="/opt/miniconda/bin:$PATH"
+
+
+# Install base packages from microct + dependencies for vtk_trial.ipynb
+# Added: opencv, matplotlib, vtk, pytorch stack
+RUN mamba install --yes -c conda-forge -c astra-toolbox -c simpleitk -c pytorch -c nvidia \
+    python=3.10 \
+    jupyterlab astropy cartopy cython cfitsio "dask[distributed]" scipy scikit-learn scikit-image numba h5py joblib pandas statsmodels _libgcc_mutex \
+    astra-toolbox tifffile tomopy dxchange svmbir itk simpleitk pyfftw natsort olefile tqdm zarr \
+    opencv matplotlib vtk pytorch torchvision torchaudio pytorch-cuda=12.4 \
+    && mamba clean --all -f -y
+
+# Install Detectron2 (requires pytorch)
+RUN python -m pip install --no-build-isolation 'git+https://github.com/facebookresearch/detectron2.git'
+
+# # install SYRIS
+# COPY syris syris
+# # RUN git clone git@github.com:ufo-kit/syris.git
+# RUN conda install -c conda-forge -y cmake pybind11 -- the NERSC docs say something about not using cmake
+# RUN cd syris && pip install -r requirements.txt && pip install .
+# RUN rm -fR syris
+
+
+# for NERSC jupyterhub environment
+RUN pip install batchspawner
+ENV NERSC_JUPYTER_IMAGE=YES
+
+# VTK/EGL Environment Configuration for Headless Rendering
+ENV VTK_DEFAULT_RENDER_WINDOW_OFFSCREEN=1
+ENV VTK_OPENGL_HAS_EGL=1
+ENV VTK_USE_OFFSCREEN_EGL=1
+ENV LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}
+ENV __GLX_VENDOR_LIBRARY_NAME=nvidia
+ENV __EGL_VENDOR_LIBRARY_FILENAMES=/usr/share/glvnd/egl_vendor.d/50_nvidia.json
+ENV VTK_DEFAULT_EGL_DEVICE=0
+
+
+
+# expected by entrypoint script
+RUN mkdir -p /alsuser /alsdata
+
+WORKDIR /alsuser

segmentation/docker-compose.yml

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+services:
+  segmentation:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    platform: linux/amd64
+    container_name: segmentation
+    volumes:
+      - .:/alsuser
+    ports:
+      - "8000:8000"
+    command: jupyter lab --ip=0.0.0.0 --port=8000 --no-browser --allow-root --NotebookApp.token=''
+    restart: unless-stopped