pyprofqueue 0.4.2
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.
License: MIT License
Requires Python: >=3.11

# PyProfQueue

___
___
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.
___
___
# Description

___
___
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.
## Component details
___
The user facing components of PyProfQueue are the *Script* class and *submit* function.

<details>
<summary>Installation</summary>
Installation should be simple through PyPi. A simple pip install should pull the most up-to-date version of pyprofqueue.

```
$pip install pyprofqueue
```
We recommend using a python environment when using this library to ensure version compatibilities of dependencies.

</details>
<details>
<summary>Script Class</summary>

### 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,
                            tmp_work_script: str = "./tmp_workfile.sh",
                            tmp_profile_script: str = "./tmp_profilefile.sh",
                            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                                                               |
| 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".                                                                                                          |
|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/<batch system of interets>.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
```
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* maintains all previous options that are not listed in the dictionary passed to *change_options*.
</details>


<details>
<summary>Submit Function</summary>

### 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,
                   bash_options: list = [''],
                   test: bool = False):
```
|           Option            | Description                                                                                                                                                                            |
|:---------------------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|           script            | *Script* object to be submitted to queue                                                                                                                                               |
|   bash_options (Optional)   | List of options that the user provided bash script may require. Defaults to [''].                                                                                                      |
|       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.                                                        |

</details>

<details>
<summary>Utility Functions</summary>

### Utility Functions

The current utility functions provided by pyprofqueue allow for the extraction and plotting of profiling data from the
[slurm profiling plugin](https://slurm.schedmd.com/hdf5_profile_user_guide.html) when a workflow has been executed 
using the [toil-cwl](https://toil.readthedocs.io/en/3.10.1/running/cwl.html) package to orchestrate slurm submission. 
THIS IS CURRENTLY NOT COMPATIBLE WITH TOIL VERSIONS >= 8.0

When a toil workflow has finished, the following function will create a dictionary containing the information of the 
executed jobs:

```python
import pyprofqueue as pypr

ouptut_dictionary = pypr.plot_profiling_data(</TOIL/BASE/OUTPUT/DIR>)
```
Replacing ***</TOIL/BASE/OUTPUT/DIR>*** with the base output directory that was provided to toil. This allows the function to
search the output directory for the necessary files in order to find the *job_id*, requested memory, requested cpu core
count, and the profiling data for each submitted step. Additionally, if the ***pd.DataFrame*** of the step contains
three or more time steps, then the average CPU utilisation, RAM usage and I/O usage can be plotted. The outputs from 
the slurm profiling, as well as the plots, are stored in a new directory, *slurm_profiling*, created in the toil
output directory.


</details>

___
##  In- and Outputs
___
### 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. 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.

<details>
<summary>Likwid Inputs</summary>

#### 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 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
```python
profiling = {"likwid": {"requirements":["module load likwid"]}}
```
</details>

<details>
<summary>Prometheus Inputs</summary>

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

<details>
<summary>Linaro Forge Inputs</summary>

#### Linaro Forge Map specific inputs
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
```python
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. 
</details>


### 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* value is 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* 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.

<details>
<summary>Prometheus Plotting Functions</summary>
The following plot functions are called automatically by the script that PyProfQueue creates, but can be called in post
by users if so desired.

### profilers.prometheus.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 |
### profilers.prometheus.plot_prom_profiling function
This function plots the results of a prometheus profiling effort. It is compatible with additional features for 
[Common Workflow Language](https://www.commonwl.org/) (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                                                         |
</details>

<details>
<summary>Likwid Plotting Functions</summary>

### profilers.likwid.plot_likwid_roof_single function
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 |

### profilers.likwid.plot_roof_timeseries function
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          |
</details>

<details>
<summary>Linaro Forge Output</summary>

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.

</details>

___
## Example usage
___
<details>
<summary>Detailed example</summary>

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:
```bash
#!/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 is meant for the Cosma system in Durham, which has modules to load likwid. This 
script can be used as a basis to add the prometheus monitoring, likwid performance profiling and to submit the script 
to the queue, but may require some changes by users. 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.
```python 
import pyprofqueue as ppq

ProfileScript = ppq.Script(queue_system='slurm',
                           work_script='./tmp_workfile.sh',
                           tmp_work_script = './test_workfile.sh',
                           tmp_profile_script = './test_profilefile.sh',
                           read_queue_system='slurm',
                           queue_options={
                             'work_dir': '/home/queue_work/job_name.job_id',
                             '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, 
           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:
```bash
#!/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
```bash
likwid-perfctr -g PYPROFQUEUE -t 300s -o ${LIKWID_RUNNING_DIR}/likwid_output.txt -O -f bash ./test_workfile.sh  "Hello " "World!"
```
Where PYPROFQUEUE is a custom group that is created by the pyprofqueue code when likwid is used. It should automatically
detect the architecture of the system on which it is being run, search existing metric groups and extract the needed
information in order to calculate the Performance in [FLOP/s] as well as the operational intensity in [FLOP/Byte]
</details>

___
## Software Requirements
___

<details>
<summary>Python Requirements</summary>

For the sake of PyProfQueue, the required python version is at least 3.10, as this package utilises the match 
functionality.
- pytz
- h5py
- numpy
- tables
- pyarrow
- matplotlib
- pandas<=2.2.1
- promql_http_api==0.3.3
</details>


<details>
<summary>Non Python Requirements</summary>

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:
- [node_exporter](https://prometheus.io/docs/guides/node-exporter/)
- [prometheus](https://prometheus.io/)
- [likwid](https://github.com/RRZE-HPC/likwid)
- [Linaro Forge](https://www.linaroforge.com/)

<details>
<summary>Prometheus Requirements</summary>
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:

```md
${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. If no pre-existing
instance of prometheus and node_exporter is running on the machine of interest, the configuration file needs to be
amended to account for the changed IP addresses of node_exporter and prometheus. It should look like so
```md
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: prometheus
    scrape_interval: 15s
    static_configs:
      - targets: ['localhost:9301']

  - job_name: 'node_exporter'
    static_configs:
      - targets: ['localhost:9303']
```
where the global scrape interval can be adjusted according to usage needs and requirements. Shorter scrape times, leads
to more data, but larger overhead for getting the data.
</details>

<details>
<summary>Likwid Requirements</summary>
For the sake of likwid, it needs to be installed or loaded, in such a way that a user could run the following 
command without sudo rights:

```
likwid-perfctr -g MEM_DP -t 300s <output directory> <executable> <options for executable>
```
</details>

<details>
<summary>Linaro Forge Requirements</summary>
For the sake of Linaro Forge, users should load linaro forge, through modules or whatever format an HPC system has,
such that they can call a function similar to 

```
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.

</details>

</details>

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

<details>
<summary>File Structure Diagram</summary>

```md
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
│   └── utils.py
├── ReadMe.md
└── setup.py
```
</details>

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.
___
## Adding new Batch systems
___
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.
___
## Adding new Profiling software
___
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 profiler to the profiling 
file.
___
___

# To Do
___
___
- Update the package format to follow more modern conventions for installing requirements
  - e.g. add pyproject.toml 
- Add Vtune profiling support
___
___
# UKSRC related Links
This package was initially created because of the Jira Ticket [TEAL-391](https://jira.skatelescope.org/browse/TEAL-391).

A Confluence page of what the prometheus and likwid results look like can be found on the 
[SKAO Confluence](https://confluence.skatelescope.org/display/SRCSC/Profiling+LOFAR+VLBI+Workflow)

### Developers and Contributors
___
Developers:
- Keil, Marcus (UCL)
- van der Wilde, Matthijs (Durham)

Contributors:
- Morabito, Leah (Durham)
- Qaiser, Fawada (Durham)
- Yates, Jeremy (UCL)
___