JUmPER: Jupyter meets Performance

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

• JUmPER Ipython extension (https://github.com/ScaDS/scorep_jupyter_ipython_extension)

The extension allows you to take coarse-grained performance measurements and create visualizations in your Python kernels.

• Score-P Jupyter kernel Python (this repository)

The Score-P kernel allows you to instrument, and trace or profile your Python code in Jupyter using Score-P.

For binding to Score-P, the kernel uses the Score-P Python bindings.

Table of Content

• A Jupyter Kernel for Performance Engineering
• Table of Content
• Installation
• Usage
  • Score-P Instrumentation
    • Configuring Score-P in Jupyter
  • Multi-Cell Mode
  • Write Mode
• Presentation of Performance Data
• Limitations
  • Serialization Type Support
  • Overhead
  • Logging Configuration
• Future Work
• Citing
• Contact
• Acknowledgments

Installation

To install the kernel and required dependencies:

pip install scorep_jupyter
python -m scorep_jupyter.install

You can also build the kernel from source via:

pip install .

The kernel will then be installed in your active python environment. You can select the kernel in Jupyter as Score-P_Python.

For using the Score-P features of the kernel you need a proper Score-P installation.

From the Score-P Python bindings:

> You need at least Score-P 5.0, build with --enable-shared and the gcc compiler plugin. > Please make sure that scorep-config is in your PATH variable. > For Ubuntu LTS systems there is a non-official ppa of Score-P available: https://launchpad.net/~andreasgocht/+archive/ubuntu/scorep .

To use the coarse-grained performance measurements, simply install the scorep_jupyter extension via:

pip install jumper_extension

Then load it in a Jupyter cell with:

%load_ext jumper_extension

You can find usage information in the repository of the extension.

Usage

Score-P Instrumentation

Configuring Score-P in Jupyter

Set up your Score-P environment with %env line magic, e.g.:

%env SCOREP_ENABLE_TRACING=1
%env SCOREP_TOTAL_MEMORY=3g

For a documentation of Score-P environment variables, see: Score-P Measurement Configuration.

%%scorep_python_binding_arguments

Set the Score-P Python bindings arguments. For a documentation of arguments, see Score-P Python bindings.

%%marshalling_settings

Set marshaller/serializer used for persistence and mode of communicating persistence between notebook and subprocess. Currently tested marshallers: dill, cloudpickle, parallel_marshall; modes of communication: disk, memory. If no arguments were provided, will print current configuration. Use:

%%marshalling_settings
MARSHALLER=[dill,cloudpickle]
MODE=[disk,memory]

When using persistence in disk mode, user can also define directory to which serializer output will be saved with SCOREP_KERNEL_PERSISTENCE_DIR environment variable.

%env SCOREP_KERNEL_PERSISTENCE_DIR=path/to/dir

To see the detailed report for marshalling steps - scorep_jupyter_MARSHALLING_DETAILED_REPORT environment variable can be set.

%env scorep_jupyter_MARSHALLING_DETAILED_REPORT=1

You can disable visual animations shown during long-running tasks by setting the scorep_jupyter_DISABLE_PROCESSING_ANIMATIONS environment variable.

%env scorep_jupyter_DISABLE_PROCESSING_ANIMATIONS=1

%%execute_with_scorep

Executes a cell with Score-P, i.e. it calls python -m scorep

Multi-Cell Mode

You can also treat multiple cells as one single cell by using the multi cell mode. Therefore you can mark the cells in the order you wish to execute them.

%%enable_multicellmode

Enables the multi-cell mode and starts the marking process. Subsequently, "running" cells will not execute them but mark them for execution after %%finalize_multicellmode.

%%finalize_multicellmode

Stop the marking process and executes all the marked cells. All the marked cells will be executed with Score-P.

%%abort_multicellmode

Stops the marking process, without executing the cells.

Hints:

• The %%execute_with_scorep command has no effect in the multi cell mode.

• There is no "unmark" command available but you can abort the multicellmode by the %%abort_multicellmode command. Start your marking process again if you have marked your cells in the wrong order.

• The %%enable_multicellmode, %%finalize_multicellmode and %%abort_multicellmode commands should be run in an exclusive cell. Additional code in the cell will be ignored.

Write Mode

Analogous to %%writefile command in IPykernel, you can convert a set of cells to the Python script which is to be executed with Score-P Python bindings (with settings and environment described in auxillary bash script).

%%start_writefile [scriptname]

Enables the write mode and starts the marking process. Subsequently, "running" cells will not execute them but mark them for writing into a python file after %%end_writefile. scriptname is jupyter_to_script.py by default.

%%end_writefile

Stops the marking process and writes the marked cells in a Python script. Additionally, a bash script will be created for setting the Score-P environment variables, Pyhton bindings arguments and executing the Python script.

Hints:

• Recording a cell containing %%scorep_python_binding_arguments will add the Score-P Python bindings to the bash script.

• Code of a cell which is not to be executed with Score-P (not inside the multicell mode and without %%execute_with_scorep) will be framed with with scorep.instrumenter.disable() in the Python script to prevent instrumentation.

• Other cells will be recorded without any changes, except for dropping all magic commands.

• %%abort_multicellmode will be ignored in the write mode and will not unmark previous cells from instrumentation.

Presentation of Performance Data

To inspect the Score-P collected performance data, use tools as Vampir (Trace) or Cube (Profile).

Limitations

Serialization Type Support

For the execution of a cell, the kernel uses the default IPython kernel. For a cell with Score-P it starts a new Python process. Before starting this process, the state of the previous executed cells is persisted using dill (https://github.com/uqfoundation/dill) or cloudpickle (https://github.com/cloudpipe/cloudpickle/releases). However:

> dill cannot yet pickle these standard types: > frame, generator, traceback

Similar yields for cloudpickle. Use the %%marshalling_settings magic command to switch between both depending on your needs.

Overhead

When dealing with big data structures, there might be a big runtime overhead at the beginning and the end of a Score-P cell. This is due to additional data saving and loading processes for persistency in the background. However this does not affect the actual user code and the Score-P measurements.

Logging Configuration

To adjust logging and obtain more detailed output about the behavior of the scorep_jupyter kernel, refer to the src/logging_config.py file.

This file contains configuration options for controlling the verbosity, format, and destination of log messages. You can customize it to suit your debugging needs.

Future Work

The kernel is still under development.

PRs are welcome.

Citing

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).
scorep_jupyter: 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

or

Werner, E., Manjunath, L., Frenzel, J., & Torge, S. (2021, October).
Bridging between Data Science and Performance Analysis: Tracing of Jupyter Notebooks.
In The First International Conference on AI-ML-Systems (pp. 1-7).
https://dl.acm.org/doi/abs/10.1145/3486001.3486249

Additionally, please refer to the Score-P Python bindings, published here:

Gocht A., Schöne R., Frenzel J. (2021)
Advanced Python Performance Monitoring with Score-P.
In: Mix H., Niethammer C., Zhou H., Nagel W.E., Resch M.M. (eds) Tools for High Performance Computing 2018 / 2019. Springer, Cham.
https://doi.org/10.1007/978-3-030-66057-4_14 

or

Gocht-Zech A., Grund A. and Schöne R. (2021)
Controlling the Runtime Overhead of Python Monitoring with Selective Instrumentation
In: 2021 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools)
https://doi.org/10.1109/ProTools54808.2021.00008

Contact

elias.werner@tu-dresden.de

Acknowledgments

This work was supported by the German Federal Ministry of Education and Research (BMBF, SCADS22B) and the Saxon State Ministry for Science, Culture and Tourism (SMWK) by funding the competence center for Big Data and AI "ScaDS.AI Dresden/Leipzig