pegasus-dockerfile

Pegasus Dockerfile Generator

You are a Pegasus container image generator. The user has invoked /pegasus-dockerfile to create a Dockerfile for their workflow.

Step 1: Read Reference Materials

1. Read references/PEGASUS.md from the repository root — especially the "Docker Container" and "Micromamba Containers" sections.
2. Read assets/templates/Dockerfile_template for the three base image patterns.

Step 2: Gather Requirements

Ask the user (skip questions they've already answered):

1. What tools are needed? List all command-line tools and Python libraries used by the wrapper scripts.
2. Are there version conflicts? Do any tools require different Python versions or conflicting libraries?
   • If yes → micromamba/conda (resolves conflicts)
   • If no → pip-based (simpler, smaller image)
3. Are wrapper scripts embedded in the container? (i.e., is_stageable=False in the transformation catalog)
   • If yes → need COPY bin/*.sh /usr/local/bin/ and chmod +x
4. Do any tools need headless/display support? (FastQC, QUAST, matplotlib without display)
   • If yes → need xvfb, libgl1-mesa-glx, libfontconfig1
5. Preferred base image?
   • python:3.8-slim — lightweight, pip-only
   • mambaorg/micromamba:1.5-jammy — conda solver for complex bioinformatics
   • ubuntu:22.04 — apt + pip + manual installs

Step 3: Select Reference Dockerfile

Based on user answers, read the closest existing example:

Pattern	Reference
Simple Python/data science (pip)	assets/examples/Dockerfile_pip_example
Complex bioinformatics (micromamba)	assets/examples/Dockerfile_micromamba_example

Read the selected reference before generating.

Step 4: Generate the Dockerfile

Start from assets/templates/Dockerfile_template and customize:

For pip-based (Option A or C):

FROM python:3.8-slim  # or ubuntu:22.04

# System dependencies
RUN apt-get update && \
    apt-get install -y --no-install-recommends \
        [packages] \
    && rm -rf /var/lib/apt/lists/*

# Python dependencies
RUN pip install --no-cache-dir \
    [packages with pinned versions]

ENV PYTHONUNBUFFERED=1

For micromamba-based (Option B):

FROM mambaorg/micromamba:1.5-jammy

USER root
RUN apt-get update && apt-get install -y --no-install-recommends \
    [system packages, xvfb if needed] \
    && rm -rf /var/lib/apt/lists/*

USER $MAMBA_USER
RUN micromamba install -y -n base -c conda-forge -c bioconda \
    python=3.8 \
    [all tools in ONE install command for solver] \
    && micromamba clean --all --yes

Key Rules

1. All tools in one container: Pegasus shares a single container across all jobs. Every tool from every wrapper must be installed.
2. Pin versions: Use tool==1.2.3 (pip) or tool=1.2.3 (conda) for reproducibility.
3. PYTHONUNBUFFERED=1: Always set this so Pegasus captures logs in real time.
4. --no-cache-dir / clean --all: Keep image size down.
5. Headless support: If any tool uses Java GUI or matplotlib, add xvfb, libgl1-mesa-glx, libfontconfig1.
6. Embedded scripts: If is_stageable=False is used, COPY and chmod +x the wrapper scripts.

Step 5: Show Build and Test Commands

After generating, show the user:

# Build
docker build -t username/image:latest -f Docker/My_Dockerfile .

# Test (interactive shell)
docker run --rm -it username/image:latest bash

# Verify tools are installed
docker run --rm username/image:latest which tool1 tool2 tool3

# Push to Docker Hub
docker push username/image:latest

Also remind the user to update the container image string in workflow_generator.py:

container = Container(
    "my_container",
    container_type=Container.SINGULARITY,
    image="docker://username/image:latest",
    image_site="docker_hub",
    # Do NOT add mounts=[] for caches/databases — use CondorIO transfer_input_files instead
)

Important: If the workflow needs external data directories (caches, model weights, databases), do NOT use container mounts=[]. Instead, use CondorIO transfer_input_files on the Transformation. See Pegasus.md "Transferring Data Directories via CondorIO".