pyprofqueue 0.2.2a2

PyProfQueue serves as a python package that can take in existing bash scripts, add prometheus monitoring calls and likwid performance measure calls, and submit the script to an HPC queue system on the users' behalf.

PyProfQueue

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PyProfQueue serves as a python package that can take in existing bash scripts, add prometheus monitoring calls and likwid performance measure calls, and submit the script to an HPC queue system on the users' behalf.

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Description

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PyProfQueue takes existing bash scripts, scrapes any queue options from them and creates two temporary files. The first file, is equivalent to the user provided bash script but with queue options removed from it. The second bash script is the file that will be submitted to the queue on the users' behalf. This second temporary script contains the queue options provided by the user as well as any additionally required commands to initialise prometheus monitoring and/or likwid performance measuring of the users bash script.

Component details

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The two main components for users of PyProfQueue are the Script class and the submit function.

Script Class

The Script class is used in the following way, and the following options are available:

script = PyProfQueue.Script(queue_system: str,
                            work_script: str,
                            read_queue_system: str =None,
                            queue_options: dict = None,
                            profiling: dict = None
                            )   

Option	Description
queue_system	The intended target queue system (Supports Slurm and PBS Torque)
work_script	The bash script which contains the queue options and work to be done
read_queue_system (Optional)	The name of the queue system for which the script was written if it was written for a queue system
queue_options (Optional)	Any queue options to add or override when compared to the work_script
profiling (Optional)	Dictionary with keys representing which profiler to use with values of dictionaries listing profiler options such as "requirements"

The options queue_options that PyProfQueue currently supports are:

• 'user'
  • The user ID of the system with which to submit the job (requires admin rights usually)
• 'nodes'
  • The number of nodes to be requested.
• 'cores'
  • The number of cores each task will need.
• 'tasks'
  • The number of tasks to perform.
• 'time'
  • The walltime this job is allowed to run for in hh:mm:ss.
• 'partition'
  • The specific queue/partition to submit to.
• 'account'
  • The account to charge for the used resources.
• 'subname'
  • Name of the submitted job.
• 'workdir'
  • The directory in which the work should be done.
• 'output'
  • The file, including path, to write the STDOUT to.

Any Script object, then comes with three additional methods intended to be used by users. These methods are:

change_options

change_options(queue_options: dict)

• Allows for options to be changed post initiation of a Script object, in case the options given in the initialisation are no longer desired.

As an example usage of change_options, let us assume we have a Script object that has the option {'time': 12:00:00} meaning that the script would be terminated if it takes longer than 12 hours. We now wish to make it so that the script is allowed to run for 24 hours. So we use the following:

script.change_options(queue_options={'time':'24:00:00'})

change_options does not overwrite existing options if they aren't specifically listed to be changed.

submit function

The submit function serves as the point of execution for PyProfQueue. When called, it will take the given Script object, and submit it to the queue system the Script object is configured for.

PyProfQueue.submit(script: Script,
                   tmp_work_script: str = './tmp_workfile.sh',
                   tmp_profile_script: str = './tmp_profilefile.sh',
                   bash_options: list = [''],
                   leave_scripts: bool = True,
                   test: bool = False):

Option	Description
script	Script object to be submitted to queue
tmp_work_script (Optional)	Desired name of temporary work script. Defaults to "tmp_workfile.sh".
tmp_profile_script (Optional)	Desired name of temporary profile script. Defaults to "tmp_profilefile.sh".
bash_options (Optional)	List of options that the user provided bash script may require. Defaults to [' '].
leave_scripts (Optional)	Boolean to determine if the temporary scripts should be left or removed after submission. Defaults to True
test (Optional)	Boolean to determine if the script should be submitted, or if the command that would be used should be printed to the terminal. Additionally, this leaves the temporary scripts in tackt so they can be inspected.

plot.load_df function

This function reads the prometheus database created by using prometheus profiling with PyProfQueue and stores it into a pandas.DataFrame. This then has the time converted into the format of "yyyy-mm-dd HH:MM:SS" for user readability. The times at which datapoints exist are then also given out as a numpy.array on top of returning the dataframe.

Option	Description
feather_path	path to the scraped prometheus database

plot.plot_profiling function

This function plots the results of a prometheus profiling effort. It is compatible with additional features for Common Workflow Language (CWL) workflows, if the output from a CWL call is saved to a file.

Option	Description
df	pandas.DataFrame of the prometheus profiling data. Obtained from load_df
time_series	numpy.array of the times at which data was collected. Obtained from load_df
name_prefix	Desired path and name prefix for the plots
network_three_mean (Optional)	Boolean on if the network y-limit should be restricted to three times the mean value
mean_cpu (Optional)	Boolean on if the mean_cpu usage should be plotted
all_cpu (Optional)	Boolean on if all cpu usages should be plotted
memory (Optional)	Boolean on if the memory usage should be plotted
network (Optional)	Boolean on if the network usage should be plotted
cwl_file (Optional)	Path to a text file containing the ouput of CWL, if it was used to run a workflow. This is used to shade when each step occured.
label (Optional)	Boolean to label each CWL step on shaded graphs if cwl_file was provided
gant (Optional)	Boolean on if a gant chart like plot should be created if CWL was used to run a workflow

plot.plot_roof function

This function plots the results of a likwid profiling effort.

Option	Description
name_prefix	Desired path and name prefix for the plot
maxperf	Float of the maximum performance listed in likwid output file
maxband	Float of the maximum memory bandwidth listed in likwid output file
code_name (Optional)	String of what to call the code in the legend of the plot
code_mflop (Optional)	Float of the codes MFLOP/s listed in the likwid output
code_opint (Optional)	Float of the codes Operational Intensity listed in the likwid output

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In- and Outputs

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Inputs

The inputs to the Script class and submit function have already been described above, what has not been described in detail in the input file that users provide. Users must provide the bash script that they wish to profile and submit to a queue system. If a bash script with no queue options is provided, then the read_queue_system option can be left off, or set to None. In this case, unless both read_queue_system and queue_system are set to None, queue_options have to be provided. If queue options are available in the script then read_queue_system should be set to the name of the queue system that the original script was intended for.

Likwid specific inputs

In order to use likwid, the key 'likwid' needs to be used in the profiling option for the Script object. This key then needs to have a value of "requirements" which contains a list of all commands that need to be executed prior to being able to use likwid on the HPC system the script is being submitted to. If, for example, a simple module loading is required, it could look like this

profiling = {"likwid": {"requirements":["module load likwid"]}}

Prometheus specific inputs

In order to use prometheus, the key 'prometheus' needs to be used in the profiling option for the Script object. This key then needs to have a value of "requirements" which has to contain the path to the prometheus instance to use, or it has to contain "ip_address" which then has an IP address, stored as a string, of a pre-existing prometheus instance that can be scraped. Here is an example of both, where "<path/to/prometheus>" is used to represent the path to the prometheus instance the user would want to use.

profiling = {"prometheus": {"requirements":["export PROMETHEUS_SOFTWARE=<path/to/prometheus>"]}}
# OR
profiling = {"prometheus": {"ip_address":["127.0.0.1:9090"]}}

Both likwid and prometheus

Here is an example of how the dictionary could look, if both likwid and prometheus were to be used:

profiling = {"likwid": {"requirements":["module load likwid"]}, 
             "prometheus": {"requirements":["export PROMETHEUS_SOFTWARE=<path/to/prometheus>"]}
             }

Outputs

The output of the Script class, is an object that contains all the given options, file paths and other variables needed in order to create the bash scripts that can be submitted to a queue system. The outputs from submit depend on the given options. If the test and leave_scripts are set to False, then the output of submit is the same as the output of the submission command for the respective queue system being used. If test or leave_scripts are set to True then the output includes two bash scripts. The first contains the original work to be performed, the other contains all the necessary variable declarations and command calls in order to perform the profiling of the given bash script. It is the profiling script that is submitted to the queue. Additionally, if test is True, then the command line will output what command would be used in order to submit the job, but the command will not actually be called.

For the plotting functions, the outputs are .png files of each requested plot.

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Example usage

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Let us look at a toy example to show how this script would be used. Let us assume we have an HPC system that uses slurm. This system requires loading the likwid module if we want to use it, and we have downloaded the prometheus codes to the directory /home/Software and ensured that we can execute both without sudo commands. Let us assume we have the following bash script:

#!/bin/bash
#SBATCH -A example_project
#SBATCH -c 16
#SBATCH -N 1
#SBATCH -o /home/queue_work/%x.%j/output.out
#SBATCH -p example_partition
#SBATCH -J TestSubmission
#SBATCH -n 1
#SBATCH -t 00:05:00

echo "The first option was:"
echo ${1}
echo "The second option was:"
echo ${2}

The following example python script can be used to add the prometheus monitoring, likwid performance profiling and to submit the script to the queue. We have listed the queue options in the Script object initialisation even though it would pull them from the bash script in order to show an example of how they would be listed.

import PyProfQueue as ppq

ProfileScript = ppq.Script(queue_system='slurm',
                           work_script='./tmp_workfile.sh',
                           queue_options={
                               'workdir': '/home/queue_work/%x.%j',
                               'account': 'example_project',
                               'cores': '16',
                               'nodes': '1',
                               'output': '/home/queue_work/%x.%j/output.out',
                               'partition': 'example_partition',
                               'name': 'TestSubmission',
                               'tasks': '1',
                               'time': '00:05:00'},
                           profiling={"likwid": {'requirements': ['module load oneAPI_comp/2021.1.0',
                                                                  'module load likwid/5.2.0']},
                                      "prometheus": {'requirements': ['export PROMETHEUS_SOFTWARE=/home/Software']}
                                      })

ppq.submit(ProfileScript, 
           tmp_work_script = './test_workfile.sh',
           tmp_profile_script = './test_profilefile.sh',
           bash_options=['"Hello "', '"World!"'],
           test=True)

This python script prints the following to the command line, but does not submit a job:

The following command would be used to submit a job to the queue:
sbatch ./test_profilefile.sh

Following this, it has created two files, test_workfile.sh and test_profilefile.sh. test_workfile.sh should look like the original bash script provided by the user, but with the options removed:

#!/bin/bash

# Any work that users may want to do on an HPC system, including environment initialisations
# For the sake of example we simply call
echo "The first option was:"
echo ${1}
echo "The second option was:"
echo ${2}

While test_profiliefile.sh contains all the necessary initialisations and terminations for prometheus and likwid to run and provide the correct outputs. The entire file won't be listed here as it is quite length, however we will state how the test_workfile.sh is called within test_profilefile.sh

likwid-perfctr -g MEM_DP -f bash ./test_workfile.sh  "Hello " "World!"

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Software Requirements

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Python Requirements

For the sake of PyProfQueue, the required python version is at least 3.10, as this package utilises the match functionality.

• numpy
• pytz
• pyarrow
• matplotlib
• promql_http_api==0.3.3
• pandas<=2.2.1

Non Python Requirements

In addition to the python requirements listed above, PyProfQueue also needs to have the following software on the system to which the job will be submitted:

• node_exporter
• prometheus
• likwid

For prometheus and node_exporter, it is enough to download the software as long as they can both be launched by the user without sudo rights. However, they need to be put into the same directory so that the following directory structure is in place:

${PROMETHEUS_SOFTWARE}
├── node_exporter
│   └── node_exporter
└── prometheus
    ├── prometheus
    └── prometheus.yml

Where node_exporter/node_exporter is the executable for node_exporter, prometheus/prometheus is the executable for prometheus, and prometheus/prometheus.yml is the configuration file to be used for prometheus.

For the sake of likwid, it needs to be installed or loaded in, in such a way that a user could run the following command without sudo rights:

likwid-perfctr -g MEM_DP -f <executable>

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Container Compatibility

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This package does not require a container and since it submits to a queue, we do not know how recommended it is to use it from within a container.

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Hardware Requirements

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As of now, no minimum Hardware requirements are known other than those forced by python 3.10, prometheus, node_exporter and likwid.

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Directory Structure

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PyProfQueue
├── PyProfQueue
│   ├── profilers
│   │   ├── data
│   │   │   ├── read_prometheus.py
│   │   │   ├── _template_commands.txt
│   │   │   ├── likwid_commands.txt
│   │   │   └── prometheus_commands.txt
│   │   ├── __init__.py
│   │   ├── _template_profiler.py
│   │   ├── likwid.py
│   │   └── prometheus.py
│   ├── __init__.py
│   ├── plot.py
│   ├── script.py
│   └── submission.py
├── ReadMe.md
└── setup.py

The directory PyProfQueue/PyProfQueue contains the plot.py. script.py and submission.py scripts which contain the definition of the plotting functions, Script class and submission() function respectively.

The directory PyProfQueue/PyProfQueue/profilers contains scripts of the individual profilers that PyProfQueue is compatible with including a template version that can be used to add additional profiling software compatibility to PyProfQueue. PyProfQueue/PyProfQueue/profilers/data contains a script called read_prometheus.py which is used to scrape the prometheus database into a pandas dataframe. It also includes the text files that list the bash commands needed to initialise run and end profiling software, as well as a template version for adding more profiling software compatibility.

The base directory contains the ReadMe.md file, and the setup.py file so that the package can be installed.

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Adding new Queue systems

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A future goal for development on PyProfQueue is to add additional queue suport beyond slurm and torque. In order to add new queue system compatibility refer to the block comments in the Script class in script.py. Each of the four section that needs changes is marked with "Queue System specifics" followed by a {1}, {2}, {3} or {4}.

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Adding new Profiling software

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In order to add new profiling software compatibility, a new script would need to be added to the PyProfQueue/PyProfQueue/profilers directory. This script would need to have the name of how the specific profiler should be called, as well as the following two functions:

• define_initialise(profilefile: io.TextIOWrapper, profilerdict: dict = None)
• define_end(profilefile: io.TextIOWrapper)

Both of these functions can read from a .txt file, which can be stored in PyProfQueue/PyProfQueue/profilers/data. The first define_initialise is there to add any variable declarations or code to the profiling bash script that would be needed in order to run the profiler. The second is any calls needed in order to terminate, or collect data post running the profiler.

If the profiler being added needs to be used in order to execute the user provided bash script, then the function define_run(profilefile: io.TextIOWrapper, bash_options: list, tmp_work_script: str) should be defined in the script for the specific profiler. This function should write the needed command to use the profilerto the profiling file.

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To Do

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• Update the package format to follow more modern conventions for installing requirements
  • e.g. add pyproject.toml
• Add Vtune profiling support
• Add Linaro Forge profiling support

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UKSRC related Links

This package was initially created because of the Jira Ticket TEAL-391.

A Confluence page of what the prometheus and likwid results look like can be found on the SKAO Confluence

Developers and Contributors

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Developers:

• Keil, Marcus (UCL)
• Qaiser, Fawada (Durham)

Contributors:

• Morabito, Leah (Durham)
• Yates, Jeremy (UCL)

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