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docker

github.com/companion-incCloud & DevOps10.0k122 installsUpdated today
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About docker

A skill for running research code in Docker containers while Feynman remains on the host. It supports isolated experiments with mounted project files and results synced back to the host.

Key features

  1. 01Run research commands in containers
  2. 02Mount project files and sync results
  3. 03Choose images for different languages
  4. 04Support GPU workloads
  5. 05Reuse containers across iterations

Use cases

  1. 01Run code from a paper repository
  2. 02Install packages without changing the host
  3. 03Execute iterative experiments
  4. 04Isolate workloads from the network

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/companion-inc/feynman --skill docker

After installing, type/dockerin Claude Code to confirm it loaded.

Details

From the project README

Docker Sandbox

Run research code inside Docker containers while Feynman stays on the host. The container gets the project files, runs the commands, and results sync back.

When to use

  • User selects "Docker Sandbox" as the execution environment in /replicate or /autoresearch
  • Running untrusted code from a paper's repository
  • Experiments that install packages or modify system state
  • Any time the user asks to run research code safely or isolated for a Feynman workflow

How it works

  1. Build or pull an appropriate base image for the research code
  2. Mount the project directory into the container
  3. Run experiment commands inside the container
  4. Results write back to the mounted directory

Running commands in a container

For Python research code (most common):

docker run --rm -v "$(pwd)":/workspace -w /workspace python:3.11 bash -c "
  pip install -r requirements.txt &&
  python train.py
"

For projects with a Dockerfile:

docker build -t feynman-experiment .
docker run --rm -v "$(pwd)/results":/workspace/results feynman-experiment

For GPU workloads:

docker run --rm --gpus all -v "$(pwd)":/workspace -w /workspace pytorch/pytorch:latest bash -c "
  pip install -r requirements.txt &&
  python train.py
"

Choosing the base image

Research typeBase image
Python ML/DLpytorch/pytorch:latest or tensorflow/tensorflow:latest-gpu
Python generalpython:3.11
Node.jsnode:20
R / statisticsrocker/r-ver:4
Juliajulia:1.10
Multi-languageubuntu:24.04 with manual installs

Persistent containers

For iterative experiments (like /autoresearch), create a named container instead of --rm. Choose a descriptive name based on the experiment:

docker create --name <name> -v "$(pwd)":/workspace -w /workspace python:3.11 tail -f /dev/null
docker start <name>
docker exec <name> bash -c "pip install -r requirements.txt"
docker exec <name> bash -c "python train.py"

This preserves installed packages across iterations. Clean up with:

docker stop <name> && docker rm <name>

Notes

  • The mounted workspace syncs results back to the host automatically
  • Containers are network-enabled by default — add --network none for full isolation
  • For GPU access, Docker must be configured with the NVIDIA Container Toolkit

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