jumper-extension 0.2.1

IPython extension for monitoring cell performance

JUmPER: Jupyter meets Performance

JUmPER brings performance engineering to Jupyter. It consists of the two repositories:

• JUmPER Ipython extension (this repository)

This extension is for real-time performance monitoring in IPython environments and Jupyter notebooks. It allows you to gather performance data on CPU usage, memory consumption, GPU utilization, and I/O operations for individual cells and present it in the notebook/IPython session either as text report or as a plot.

• Score-P Jupyter kernel Python (https://github.com/score-p/scorep_jupyter_kernel_python)

The Score-P kernel allows you to instrument, and trace or profile your Python code in Jupyter using Score-P for in-detail performance analysis tasks. The Score-P kernel and the IPython extension can be seamlessly integrated.

Table of Content

• Installation
• Quick Start
  • Load the Extension
  • Basic Usage
• Metrics Collection
  • Performance Monitoring Levels
  • Collected Metrics
• Available Commands
• Contribution and Citing

Installation

pip install jumper_extension

or install it from source:

pip install .

Quick Start

Load the Extension

%load_ext jumper_extension

Basic Usage

1. Start monitoring:

   %perfmonitor_start [interval]
   

   interval is an optional argument for configuring frequency of performance data gathering (in seconds), set to 1 by default. This command launches a performance monitoring daemon.

2. Run your code

3. View performance report:

   %perfmonitor_perfreport
   %perfmonitor_perfreport --cell 2:5 --level user
   

   Will print aggregate performance report for entire notebook execution so far:

   ----------------------------------------
   Performance Report
   ----------------------------------------
   Duration: 11.08s
   Metric                    AVG      MIN      MAX      TOTAL   
   -----------------------------------------------------------------
   CPU Util (Across CPUs)    10.55    3.86     45.91    -       
   Memory (GB)               7.80     7.74     7.99     15.40   
   GPU Util (Across GPUs)    27.50    5.00     33.00    -       
   GPU Memory (GB)           0.25     0.23     0.32     4.00    
   

   Options:

   • --cell RANGE: Specify cell range (e.g., 5, 2:8, :5)
   • --level LEVEL: Choose monitoring level (process, user, system, slurm)

4. Plot performance data:

   %perfmonitor_plot
   

   Opens an interactive plot with widgets to explore performance metrics over time, filter by cell ranges, and select different monitoring levels.

5. View cell execution history:

   %cell_history
   

   Shows an interactive table of all executed cells with timestamps and durations.

6. Stop monitoring:

   %perfmonitor_stop
   

7. Export data for external analysis:

   %perfmonitor_export_perfdata my_performance.csv --level system
   %perfmonitor_export_cell_history my_cells.json
   

   Export performance measurements for entire notebook and cell execution history with timestamps, allowing you to project measurements onto specific cells.

Metrics Collection

Performance Monitoring Levels

The extension supports four different levels of metric collection, each providing different scopes of system monitoring:

• Process: Metrics for the current Python process only
• User: Metrics for all processes belonging to the current user
• System: System-wide metrics across all processes and users (if visible)
• Slurm: Metrics for processes within the current SLURM job

Collected Metrics

Metric	Description
cpu_util	CPU utilization percentage
memory	Memory usage in GB
io_read_count	Total number of read I/O operations
io_write_count	Total number of write I/O operations
io_read_mb	Total data read in MB
gpu_util	GPU compute utilization percentage across GPUs
gpu_band	GPU memory bandwidth utilization percentage
gpu_mem	GPU memory usage in GB across GPUs
io_write_mb	Total data written in MB

Note: GPU metrics require NVIDIA GPUs with pynvml library. Memory limits are automatically detected from SLURM cgroups when available.

Available Commands

Command	Description
%perfmonitor_help	Show all available commands with examples
%perfmonitor_resources	Display available hardware resources
%perfmonitor_start [interval]	Start monitoring (default: 1 second interval)
%perfmonitor_stop	Stop monitoring
%perfmonitor_perfreport [--cell RANGE] [--level LEVEL]	Show performance report for specific cell range and monitoring level
%perfmonitor_plot	Interactive plot with widgets for exploring performance data
%cell_history	Show execution history of all cells with interactive table
%perfmonitor_enable_perfreports	Auto-generate reports after each cell
%perfmonitor_disable_perfreports	Disable auto-reports
%perfmonitor_export_perfdata [filename] [--level LEVEL]	Export performance data to CSV
%perfmonitor_perfdata_to_dataframe [df_name] [--level LEVEL]	Export performance data to Pandas dataframe
%perfmonitor_export_cell_history [filename]	Export cell history to CSV/JSON

Contribution and Citing:

PRs are welcome. Feel free to use the pre-commit hooks provided in .githooks

If you publish some work using the kernel, we would appreciate if you cite one of the following papers:

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