Installation#

Quick install via pip#

pip install kaleidocell

For GPU acceleration (Linux/HPC), install PyTorch with the appropriate CUDA wheel before installing kaleidocell:

# Replace cu124 with your CUDA version (run nvidia-smi to check)
pip install torch --index-url https://download.pytorch.org/whl/cu124
pip install kaleidocell

Prerequisites#

Miniconda or Anaconda is required for Options A–C. Docker is required for Options D–E.

Option A — Linux / HPC server (GPU, CUDA 12.4)#

# Initialize conda (only needed if not already in your shell)
source "$(conda info --base)/etc/profile.d/conda.sh"

# Define paths (edit these)
PROJECT_DIR=/path/to/kaleidocell
ENV_DIR=$PROJECT_DIR/.environments/kaleidocell_env

conda create -p "$ENV_DIR" python=3.11 -y
conda activate "$ENV_DIR"

# Install GPU-enabled PyTorch — replace cu124 with your CUDA version
# (run nvidia-smi to check: cu121 = 12.1, cu118 = 11.8, …)
pip install torch --index-url https://download.pytorch.org/whl/cu124

cd "$PROJECT_DIR"
pip install -e .
pip install ipykernel
python -m ipykernel install --user --name=kaleidocell_env --display-name "kaleidocell_env"

Option B — Linux / HPC via environment.yml#

source "$(conda info --base)/etc/profile.d/conda.sh"

PROJECT_DIR=/path/to/kaleidocell
ENV_DIR=$PROJECT_DIR/.environments/kaleidocell_env

cd "$PROJECT_DIR"
conda env create -p "$ENV_DIR" -f environment.yml
conda activate "$ENV_DIR"

python -m ipykernel install --user --name=kaleidocell_env --display-name "kaleidocell_env"

To reactivate later:

source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate "$ENV_DIR"

Option C — Mac (Apple Silicon M1/M2/M3)#

PyTorch ships with MPS (Metal Performance Shaders) support out of the box — no separate CUDA wheel is needed.

source "$(conda info --base)/etc/profile.d/conda.sh"

PROJECT_DIR=/path/to/kaleidocell
ENV_DIR=$PROJECT_DIR/.environments/kaleidocell_env

conda create -p "$ENV_DIR" python=3.11 -y
conda activate "$ENV_DIR"

pip install torch   # MPS support included by default

cd "$PROJECT_DIR"
pip install -e .
pip install ipykernel
python -m ipykernel install --user --name=kaleidocell_env --display-name "kaleidocell_env"

To reactivate later:

source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate "$ENV_DIR"

Option D — Docker (terminal)#

A pre-built image with all dependencies (Python 3.11, PyTorch CUDA 12.4, kaleidocell) is available on Docker Hub as hdsu/kaleidocell_env:latest.

# Pull the image
docker pull hdsu/kaleidocell_env:latest

# Run interactively — mount your data directory into /workspace/data
docker run --gpus all -it --rm \
    -v /path/to/your/data:/workspace/data \
    hdsu/kaleidocell_env:latest

# Run Jupyter Notebook (open http://localhost:8888 in your browser)
docker run --gpus all -it --rm \
    -v /path/to/your/data:/workspace/data \
    -p 8888:8888 \
    hdsu/kaleidocell_env:latest \
    jupyter notebook --ip=0.0.0.0 --no-browser --allow-root \
        --notebook-dir=/workspace/data

Note

GPU support requires the NVIDIA Container Toolkit. Omit --gpus all to run on CPU only.

Option E — Docker (VS Code Dev Container)#

Dev Containers open the project inside the Docker image directly from VS Code, with full IntelliSense, debugging, and Jupyter support.

Setup

  1. Install the Dev Containers extension in VS Code.

  2. Create a .devcontainer/kaleidocell/ folder at the root of your project and place the following devcontainer.json inside it (a copy is provided in docker/devcontainer.json):

{
    "name": "kaleidocell",
    "image": "hdsu/kaleidocell_env:latest",
    "runArgs": [
        "--name", "kaleidocell",
        "--gpus", "all"
    ],
    "workspaceMount": "source=${localWorkspaceFolder},target=/workspace,type=bind",
    "workspaceFolder": "/workspace",
    "customizations": {
        "vscode": {
            "extensions": [
                "ms-python.python",
                "ms-python.vscode-pylance",
                "ms-toolsai.jupyter",
                "ms-python.black-formatter"
            ]
        }
    }
}

  1. Open VS Code, press F1 → Dev Containers: Reopen in Container.

Verify GPU acceleration#

import torch
print("CUDA:", torch.cuda.is_available())
print("MPS: ", torch.backends.mps.is_available())