Feature/docker

.dockerignore

@@ -0,0 +1,26 @@
+# Ignore the following files and directories when building the Docker image
+*.pyc
+__pycache__/
+*.ipynb_checkpoints
+*.log
+*.csv
+*.tsv
+*.h5
+*.pth
+*.pt
+*.zip
+*.tar.gz
+*.egg-info/
+dist/
+build/
+.env
+venv/
+.env.local
+*.DS_Store
+*.egg
+*.whl
+*.pkl
+*.json
+*.yaml
+*.yml
+submodules/

Dockerfile

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+FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
+
+# Set the working directory
+WORKDIR /EDGS
+
+# Install system dependencies first, including git, build-essential, and cmake
+RUN apt-get update && apt-get install -y \
+  git \
+  wget \
+  build-essential \
+  cmake \
+  ninja-build \
+  libgl1-mesa-glx \
+  libglib2.0-0 \
+  && rm -rf /var/lib/apt/lists/*
+
+# Copy only essential files for cloning submodules first (e.g., .gitmodules)
+# Or, if submodules are public, you might not need to copy anything specific for this step
+# For simplicity, we'll copy everything, but this could be optimized
+COPY . .
+
+# Initialize and update submodules
+RUN git submodule init && git submodule update --recursive
+
+# Install Miniconda
+RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda.sh && \
+  bash /tmp/miniconda.sh -b -p /opt/conda && \
+  rm /tmp/miniconda.sh
+ENV PATH="/opt/conda/bin:${PATH}"
+
+# Create the conda environment and install dependencies
+RUN conda create -y -n edgs python=3.10 pip && \
+  conda clean -afy && \
+  echo "source activate edgs" > ~/.bashrc
+
+# Set CUDA architectures to compile for
+ENV TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;8.9;9.0+PTX"
+
+# Activate the environment and install Python dependencies
+RUN /bin/bash -c "source activate edgs && \
+  pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 && \
+  pip install -e ./submodules/gaussian-splatting/submodules/diff-gaussian-rasterization && \
+  pip install -e ./submodules/gaussian-splatting/submodules/simple-knn && \
+  pip install pycolmap wandb hydra-core tqdm torchmetrics lpips matplotlib rich plyfile imageio imageio-ffmpeg && \
+  pip install -e ./submodules/RoMa && \
+  pip install gradio plotly scikit-learn moviepy==2.1.1 ffmpeg open3d"
+
+# Expose the port for Gradio
+EXPOSE 7862
+
+# Command to run the Gradio demo
+CMD ["bash", "-c", "source activate edgs && python gradio_demo.py --port 7862"]

README.md

@@ -69,45 +69,14 @@ Alternatively, check our [Colab notebook](https://colab.research.google.com/gith
 <a id="sec-install"></a>
 ## 🛠️ Installation
 
-You can either run `install.sh` or manually install using the following:
+You can install it just:
 
 ```bash
-git clone git@github.com:CompVis/EDGS.git --recursive
-cd EDGS
-git submodule update --init --recursive 
-
-conda create -y -n edgs python=3.10 pip
-conda activate edgs
-
-# Set up path to your CUDA. In our experience similar versions like 12.2 also work well 
-export CUDA_HOME=/usr/local/cuda-12.1
-export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
-export PATH=$CUDA_HOME/bin:$PATH
-
-conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia -y
-conda install nvidia/label/cuda-12.1.0::cuda-toolkit -y
-
-pip install -e submodules/gaussian-splatting/submodules/diff-gaussian-rasterization
-pip install -e submodules/gaussian-splatting/submodules/simple-knn
-
-# For COLMAP and pycolmap
-# Optionally install original colmap but probably pycolmap suffices
-# conda install conda-forge/label/colmap_dev::colmap
-pip install pycolmap
-
-
-pip install wandb hydra-core tqdm torchmetrics lpips matplotlib rich plyfile imageio imageio-ffmpeg
-conda install numpy=1.26.4 -y -c conda-forge --override-channels
-
-pip install -e submodules/RoMa
-conda install anaconda::jupyter --yes
-
-# Stuff necessary for gradio and visualizations
-pip install gradio 
-pip install plotly scikit-learn moviepy==2.1.1 ffmpeg
-pip install open3d 
+docker compose up
 ```
 
+or you can install with running `install.sh`.
+
 <a id="sec-data"></a>
 ## 📦 Data
 

docker-compose.yml

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+services:
+  edgs-app:
+    build: . # Instructs Docker Compose to build using the Dockerfile in the current directory
+    image: edgs-app # This is the name of the image you built
+    ports:
+      - "7862:7862" # Map port 7862 on the host to port 7862 in the container
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all # Use all available GPUs
+              capabilities: [gpu] # Request GPU capabilities
+    volumes:
+      - ./data:/EDGS/data # Example: map a local 'data' folder to '/EDGS/data' in the container
+      - ./output:/EDGS/output # Example: map a local 'output' folder