If you have Docker installed, you can use the Recursion Tree Visualiser without manually installing Python, Graphviz, or other dependencies. This approach provides a consistent, isolated environment across different operating systems.
Prerequisites
Before using the Docker installation method, ensure you have:
- Docker installed (download here)
- Docker Compose installed (usually included with Docker Desktop)
- Basic familiarity with command-line operations
Installation
The Docker setup uses two files: a Dockerfile for building the image and a docker-compose.yml file for running the container.
Download the Dockerfile
Download the Dockerfile from the repository:curl https://raw.githubusercontent.com/Bishalsarang/Recursion-Tree-Visualizer/master/Dockerfile --output Dockerfile
FROM python:3.6-slim
RUN apt-get update
RUN apt-get -y install graphviz
ADD . /vs
WORKDIR /vs
RUN pip install recursion-visualiser
Download the docker-compose.yml
Download the docker-compose configuration:curl https://raw.githubusercontent.com/Bishalsarang/Recursion-Tree-Visualizer/master/docker-compose.yml --output docker-compose.yml
version: "3.6"
services:
vs:
build: .
container_name: recursion-visualiser-env
stdin_open: true
tty: true
user: ${CURRENT_UID}
volumes:
- .:/vs
Start the Docker container
Build and start the container with the following command:CURRENT_UID=$(id -u):$(id -g) docker-compose up
- Sets the container user to match your current user ID (prevents permission issues)
- Builds the Docker image (first time only)
- Starts the container in the background
The first time you run docker-compose up, it will download the base Python image and install all dependencies. This may take a few minutes.
Running Python scripts
Once the container is running, you can execute Python scripts inside it using docker-compose exec.
Basic usage
docker-compose exec vs python your_script.py
Example: Running fibonacci.py
Create the script
Run the script
View the output
Create a file named fibonacci.py in your current directory:from visualiser.visualiser import Visualiser as vs
@vs(node_properties_kwargs={
"shape": "record",
"color": "#f57542",
"style": "filled",
"fillcolor": "grey"
})
def fib(n):
if n <= 1:
return n
return fib(n=n - 1) + fib(n=n - 2)
def main():
print(fib(n=6))
vs.make_animation("fibonacci.gif", delay=2)
if __name__ == "__main__":
main()
Execute the script inside the Docker container:docker-compose exec vs python fibonacci.py
Drawing for fib(1)
8
File fibonacci.png successfully written
Writing frames....
Writing gif...
Saved gif fibonacci.gif successfully
The generated files (fibonacci.gif and fibonacci.png) will be saved in your current directory on the host machine, thanks to the volume mount in docker-compose.yml.ls -l fibonacci.*
# fibonacci.gif
# fibonacci.png
Understanding the Docker setup
The Dockerfile
The Dockerfile defines the environment:
Starts from a minimal Python 3.6 image.
RUN apt-get update
RUN apt-get -y install graphviz
/vs in the container and sets it as the working directory.
RUN pip install recursion-visualiser
The docker-compose.yml
The docker-compose file simplifies container management:
Builds the image from the Dockerfile in the current directory.
Runs the container as your current user, preventing file permission issues.
Mounts your current directory to /vs in the container, allowing the container to read your scripts and write output files.
stdin_open: true
tty: true
Common Docker commands
Start the container (if stopped)
CURRENT_UID=$(id -u):$(id -g) docker-compose up -d
-d flag runs the container in detached mode (background).
Stop the container
Restart the container
View container logs
docker-compose logs -f vs
Access the container shell
For interactive debugging or exploration:
docker-compose exec vs /bin/bash
python fibonacci.py
ls -la
# etc.
exit or Ctrl+D.
Remove the container and image
To completely remove the Docker setup:
# Stop and remove containers
docker-compose down
# Remove the image
docker rmi recursion-visualiser-env_vs
File permissions
The CURRENT_UID=$(id -u):$(id -g) environment variable ensures that files created by the container are owned by your user, not root.
CURRENT_UID=$(id -u):$(id -g) docker-compose up
docker-compose exec vs python fibonacci.py
ls -l fibonacci.gif
# -rw-r--r-- 1 yourusername yourgroup 12345 ... fibonacci.gif
docker-compose up # No CURRENT_UID set
docker-compose exec vs python fibonacci.py
ls -l fibonacci.gif
# -rw-r--r-- 1 root root 12345 ... fibonacci.gif
sudo to modify or delete.Always use CURRENT_UID=$(id -u):$(id -g) when starting the container to avoid permission issues with generated files.
Working with examples
The repository includes several example scripts. Here’s how to run them using Docker:
# Clone the repository (if you haven't already)
git clone https://github.com/Bishalsarang/Recursion-Tree-Visualizer.git
cd Recursion-Tree-Visualizer
# Start the container
CURRENT_UID=$(id -u):$(id -g) docker-compose up -d
# Run different examples
docker-compose exec vs python examples/fibonacci.py
docker-compose exec vs python examples/factorial.py
docker-compose exec vs python examples/coin_change.py
docker-compose exec vs python examples/combinations.py
Advantages of Docker installation
No dependency conflicts
The Docker container provides an isolated environment, preventing conflicts with other Python packages or Graphviz versions on your system.
Consistent across platforms
The same Docker setup works on Linux, macOS, and Windows, eliminating platform-specific installation issues.
Easy cleanup
Remove the entire environment with a single command: docker-compose down && docker rmi recursion-visualiser-env_vs
Reproducible environment
The Dockerfile specifies exact versions, ensuring consistent behavior across different machines and times.
Troubleshooting
Permission denied errors
Problem: Cannot read/write files, or files are owned by root.
Solution: Always use CURRENT_UID when starting the container:
CURRENT_UID=$(id -u):$(id -g) docker-compose up
Container not starting
Problem: docker-compose up fails or exits immediately.
Solution: Check Docker daemon is running:
docker ps # Should list running containers
sudo systemctl start docker # Linux
# or start Docker Desktop on macOS/Windows
Module not found errors
Problem: ModuleNotFoundError: No module named 'visualiser'
Solution: Rebuild the container to reinstall dependencies:
docker-compose down
docker-compose build --no-cache
CURRENT_UID=$(id -u):$(id -g) docker-compose up
Port conflicts
The default setup doesn’t expose any ports, but if you modify it and encounter port conflicts:
# Check what's using the port
sudo lsof -i :PORT_NUMBER
# Stop conflicting service or change port in docker-compose.yml
Out of disk space
Problem: Docker build fails due to insufficient space.
Solution: Clean up unused Docker resources:
# Remove unused containers, networks, images
docker system prune -a
# Check disk usage
docker system df
Comparison: Docker vs. Native installation
| Aspect | Docker | Native Installation |
|---|
| Setup time | ~5-10 minutes (first time) | ~5-15 minutes (varies by OS) |
| Dependencies | Only Docker required | Python, pip, Graphviz, packages |
| Isolation | Complete isolation | Shares system Python |
| Updates | Rebuild container | Update packages individually |
| File access | Via volume mounts | Direct access |
| Performance | Slight overhead | Native performance |
| Cross-platform | Identical across platforms | Platform-specific steps |
Use Docker if you want a clean, isolated environment or work across multiple machines. Use native installation if you need maximum performance or already have Python and Graphviz installed.