pyprofqueue serves as a python package that can take in existing bash scripts, add initialisations and calls to use profilers on the bash script, and submit the script to an HPC queue system on the users' behalf.
PyProfQueue serves as a python package that can take in existing bash scripts, add prometheus monitoring calls, likwid performance measure calls, linaro forge map calls, and submit the script to an HPC queue system on the users' behalf.
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, linaro forge map profiling and/or likwid performance measuring of the users bash script.
The user facing components of PyProfQueue are the Script class and submit function.
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 queue options that PyProfQueue supports are dependent on the batch system, for more details, we advise looking at the dictionaries in ./pyprofqueue/batch_systems/.py in order to find option compatibility.
Any Script object, then comes with three additional methods intended to be used by users. These methods are:
change_options(queue_options: dict)
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 maintains all previous options that are not listed in the dictionary passed to change_options.
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. In order to use profiling tools, a dictionary has to be provided to the profiling option of the Script class. The keys of the dictionaries are the name of the profiling tools to be used, and the values of the dictionary is a dictionary of required and optional arguments for each profiler. As long as there is no clashes between profiling tools, multiple can be used at once. We list examples of inputs below.
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 dictionary which can contain the key "requirements" which would 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 command is required, it could look like this
profiling = {"likwid": {"requirements":["module load likwid"]}}
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 dictionary containing the key "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"]}}
In order to use Linaro Forge map profiling, the key 'linaro_forge' needs to be used in the profiling option for the Script object. This key then requires have a value of "code_line" listing the strings to look for in the user provided bash script which should be profiled using Linaro Forge map. It is important to note, that any entry into the "code_line" list will be used to search lines of the user provided bash script, providing a "code_line" such as 'echo', would add Linaro Forge map profiling to every line containing the string 'echo', not just a line that only has 'echo' on it. Additionally, a "requirements" key can be provided which should list any commands that need to be executed prior to being able to use Linaro Forge on an HPC system, and it can also contain an "options" key to allow options to be passed to the Linaro Forge map calls. Here is an example of how the profiling option could look like if a user wanted to only use Linaro Forge map profiling
profiling = {"linaro_forge": {'code_line': ['echo "Hello World"']}}
Because of the overhead of Linaro Forge map, we do not recommend using Likwid and Linaro Forge map together. The results from Likwid would be less representative of the user provided bash script as the overhead of Linaro Forge map would be included into the data, but non-separable.
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.
Where profilers are set up to return plots, the outputs are .png files. While the plots are autogenerated in most cases, it is possible to replot them in post using the functions found within the respective python scripts for a profiler.
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 |
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 |
| 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 |
| network_three_mean (Optional) | Boolean on if the network y-limit should be restricted to three times the mean value |
| gant (Optional) | Boolean on if a gant chart like plot should be created if CWL was used to run a workflow |
| 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 |
This function plots the results of a likwid profiling effort as a single point, meaning that it is the average FLOP/s and average operational intensity over the entire duration of the job.
| 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 |
This function plots the results of a likwid profiling effort as a single point, meaning that it is the average FLOP/s and average operational intensity over the entire duration of the job. The performance is plotted in Log scale.
| Option | Description |
|---|---|
| likwid_file | Path to likwid output file |
| 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 |
Linaro Forge map, provides a .map file for each call of Linaro Forge map that was added to the user provided bash script. This file can be opened using Linaro Forge in order to see the results of the profiling performed on those calls.
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',
'job_name': 'NewName'},
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, in our case:
#!/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 plots and output files. 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 -t 300s -o ${LIKWID_RUNNING_DIR}/likwid_output.txt -O -f bash ./test_workfile.sh "Hello " "World!"
For the sake of PyProfQueue, the required python version is at least 3.10, as this package utilises the match functionality.
In addition to the python requirements listed above, PyProfQueue also needs to have the following software available on the system to which the job will be submitted:
${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.
likwid-perfctr -g MEM_DP -t 300s <output directory> <executable> <options for executable>
map --profile --no-queue -o <output file path> <user command>
as this is what PyProfQueue will use in order to profile parts of the user provided work script.
As of now, no minimum Hardware requirements are known other than those forced by python 3.10, prometheus, node_exporter and likwid.
PyProfQueue
├── pyprofqueue
│ ├── batch_systems
│ │ ├── pbs.py
│ │ ├── slurm.py
│ │ └── _template_batch.txt
│ ├── profilers
│ │ ├── data
│ │ │ ├── read_prometheus.py
│ │ │ ├── likwid_commands.txt
│ │ │ ├── linaro_forge_commands.txt
│ │ │ ├── prometheus_commands.txt
│ │ │ └── _template_commands.txt
│ │ ├── likwid.py
│ │ ├── linaro_forge.py
│ │ ├── prometheus.py
│ │ └── _template_profiler.txt
│ ├── __init__.py
│ ├── plot.py
│ ├── script.py
│ └── submission.py
├── ReadMe.md
└── setup.py
The directory PyProfQueue/pyprofqueue contains the script.py and submission.py scripts which house the definition of the Script class and submission() function respectively.
The directory PyProfQueue/pyprofqueue/batch_systems contains the python files which house the dictionaries for each batch system that PyProfQueue is compatible with. 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.
In order to new batch system compatibility, a new .py file has to be created that follows the PyProfQueue/pyprofqueue/batch_systems/_template_batch.txt format. If this is added correctly, then any options that have overlap to pre-existing batch systems files will automatically be able to translate between each other.
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:
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 profiler to the profiling file.
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:
Contributors:
FAQs
pyprofqueue serves as a python package that can take in existing bash scripts, add initialisations and calls to use profilers on the bash script, and submit the script to an HPC queue system on the users' behalf.
We found that pyprofqueue demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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