Jumper Wrapper Kernel

A Jupyter kernel that wraps other kernels while providing jumper-extension performance monitoring capabilities.

Features

• Wrap any Jupyter kernel: Use %wrap_kernel to wrap any installed Jupyter kernel (Python, R, Julia, etc.)
• Performance monitoring: All jumper-extension magic commands are handled locally, allowing you to monitor performance of any wrapped kernel
• Seamless forwarding: All non-magic code is forwarded to the wrapped kernel transparently

Requirements

• Python >= 3.8
• ipykernel >= 6.0
• jupyter_client >= 7.0
• jumper-extension >= 0.3.0 (required dependency)

Installation

# Install the package
pip install jumper_wrapper_kernel

# Install the kernel spec
python -m jumper_wrapper_kernel.install install

# Or install to sys.prefix (for virtualenv/conda)
python -m jumper_wrapper_kernel.install install --sys-prefix

Development Installation

git clone https://github.com/ScaDS/jumper_wrapper_kernel.git
cd jumper_wrapper_kernel
pip install -e .
python -m jumper_wrapper_kernel.install install

Usage

1. Start Jupyter Notebook or JupyterLab
2. Select "Jumper Wrapper Kernel" as your kernel
3. Use the magic commands:

Available Magic Commands

%list_kernels

List all available Jupyter kernels that can be wrapped.

%list_kernels

Output:

Available Jupyter Kernels:
--------------------------------------------------
  python3: Python 3 (ipykernel) (python)
  ir: R (r)
  julia-1.9: Julia 1.9 (julia)
--------------------------------------------------

%wrap_kernel <kernel_name>

Wrap an existing Jupyter kernel. All subsequent code will be forwarded to this kernel.

%wrap_kernel python3

Output:

Successfully wrapped kernel: python3

Jumper Extension Commands

All jumper-extension magic commands are available and executed locally:

• %perfmonitor_start [interval] - Start performance monitoring
• %perfmonitor_stop - Stop performance monitoring
• %perfmonitor_perfreport - View performance report
• %perfmonitor_plot - Plot performance data
• %cell_history - View cell execution history
• And more...

See the jumper-extension documentation for full details.

Example Workflow

# List available kernels
%list_kernels

# Wrap a Python kernel
%wrap_kernel python3

# Start performance monitoring (handled locally)
%perfmonitor_start

# Execute code on the wrapped kernel
import numpy as np
x = np.random.rand(1000, 1000)
y = np.dot(x, x.T)

# View performance report (handled locally)
%perfmonitor_perfreport

How It Works

The Jumper Wrapper Kernel acts as a proxy:

1. Magic commands from jumper-extension are intercepted and executed locally in the wrapper kernel's IPython environment
2. All other code is forwarded to the wrapped kernel for execution
3. Output from the wrapped kernel is streamed back to the notebook

This allows you to monitor performance of any Jupyter kernel, regardless of its language.

Uninstallation

# Uninstall the kernel spec
python -m jumper_wrapper_kernel.install uninstall

# Uninstall the package
pip uninstall jumper_wrapper_kernel

License

MIT License

Citation

If you use this kernel in your research, please cite the jumper-extension paper:

Werner, E., Rygin, A., Gocht-Zech, A., Döbel, S., & Lieber, M. (2024, November). 
JUmPER: Performance Data Monitoring, Instrumentation and Visualization for Jupyter Notebooks. 
In SC24-W: Workshops of the International Conference for High Performance Computing, 
Networking, Storage and Analysis (pp. 2003-2011). IEEE. 
https://www.doi.org/10.1109/SCW63240.2024.00250