PyLauncher at TACC

Last update: February 17, 2025

What is PyLauncher

PyLauncher (Python + Launcher) is a Python-based parametric job launcher, a utility for distributing and executing many small jobs in parallel, using fewer resources than would be necessary to execute all jobs simultaneously. On many batch-based cluster computers this is a better strategy than submitting many small individual small jobs.

While TACC's deprecated Launcher utility worked on serial codes, PyLauncher works with multi-threaded and MPI executables.

Example: You need to run a program with 1000 different input values, and you want to use 100 cores for that; PyLauncher will cycle through your list of commands using cores as they become available.

The PyLauncher source code is written in Python, but this need not concern you: in the simplest scenario you use a two line Python script. However, for more sophisticated scenarios the code can be extended or integrated into a Python application.

Installations

PyLauncher is available on all TACC systems via the Lmod modules system. Use the following in your batch script or idev session.

$ module load pylauncher

Basic setup

PyLauncher, like any compute-intensive application, must be invoked from a Slurm job script, or interactively within an idev session. PyLauncher interrogates Slurm's environment variables to query the available computational resources, but the only parameter you have to set is Slurm's -N directive. The number of nodes depends on how much work you have.

#SBATCH -N 5 # number of nodes you want to use

Pylauncher will then use all the cores of these nodes, running by default one commandline per core. See below for exceptions.

The pylauncher module sets the TACC_PYLAUNCHER_DIR and PYTHONPATH environment variables.

Your batch script can then invoke Python3 on the launcher code:

## file: mylauncher.py
import pylauncher 
pylauncher.ClassicLauncher("commandlines")

PyLauncher will now execute the lines in the file commandlines:

# this is the commandlines file
./yourprogram value1
./yourprogram value2

The commands can be complicated as you wish, e.g.:

mkdir output1 && cd output1 && ../yourprogram value1

./yourprogram -n PYL_ID #1
./yourprogram -n PYL_ID #2
./yourprogram -n PYL_ID #3
./yourprogram -n PYL_ID #4

At the end of the run, PyLauncher will produce final statistics:

Launcherjob run completed.

total running time: 222.22

# tasks completed: 160
tasks aborted: 0
max runningtime:  97.95
avg runningtime:  36.59
aggregate   : 5854.60
speedup     :  26.35

Host pool of size 40.

Number of tasks executed per node:
max: 11
avg: 4

This reports that 160 commands were executed, using 40 cores. Ideally we would expect a 40 times speedup, but because of variations in run time the aggregate running time of all commands was reduced by only 25.

If you want more detailed trace output during the run, add an option:

launcher.ClassicLauncher("commandlines",debug="host+job")

Output files

PyLauncher will create a directory "pylauncher_tmp123456" where "123456" maps to the job number. You can set the name of this directory explicitly:

pylauncher.ClassicLauncher("commandlines",workdir="pylauncher_out")

However, note that PyLauncher will not allow you to re-use that directory, so you need to delete it in between runs.

You need to take care of the output of your commandlines explicitly. For instance, your commandlines file could say

mkdir -p myoutput && cd myoutput && ${HOME}/myprogram input1
mkdir -p myoutput && cd myoutput && ${HOME}/myprogram input2
mkdir -p myoutput && cd myoutput && ${HOME}/myprogram input3
...

A file "queuestate" is generated with a listing of which of your commands were successfully executed, and, in case your job times out, which ones were pending or not scheduled. This can be used to restart your job. See below.

Parallel runs

Multi-Threaded

If your program is multi-threaded, you can give each command line more than one core with:

launcher.ClassicLauncher("commandlines",cores=4)

This can also be used if your program takes more memory than would normally be assigned to a single core.

If you want each command line to use all the cores of a node, specify:

launcher.ClassicLauncher("commandlines",cores="node")

The number of simultaneously running commands is then equal to the number of nodes you requested.

If you have a multi-threaded program and you want to set the number of cores individually for each commandline, use the option cores="file" (literally, the word "file" in quotes) and prefix each commandline with the core count:

5,myprogram value1
2,myprogram value2
7,myprogram value3
# et cetera

MPI

If your program is MPI parallel, replace the ClassicLauncher call:

launcher.IbrunLauncher("parallellines",cores=3)

The "parallellines" file consists of command-lines without the MPI job starter, which is supplied by PyLauncher:

./parallelprogram 0 10
./parallelprogram 1 10
./parallelprogram 2 10

In the launcher invocation, the "debug" parameter causes trace output to be printed during the run. Example:

tick 104
Queue:
completed  60 jobs: 0-44 47-48 50-53 56 58 60-61 64 66 68 70 75
aborted     0 jobs:
queued      5 jobs: 99-103
running 39 jobs: 45-46 49 54-55 57 59 62-63 65 67 69 71-74 76-98

Which states that in the 104'th stage some jobs were completed/queued for running/actually running.

The "tick" message is output every half second. This can be changed, for instance to 1/10th of a second, by specifying "delay=.1" in the launcher command. In some cases, for instance if each command is a python invocation that does many "imports", you could increase the delay parameter.

Sample Job Setup

Slurm Job Script File on Frontera

#!/bin/bash
#SBATCH   -p development
#SBATCH   -J pylaunchertest
#SBATCH   -o pylaunchertest.o%j
#SBATCH   -e pylaunchertest.o%j
#SBATCH   –ntasks-per-node 1 # this parameter is ignored
#SBATCH   -N 2
#SBATCH   -t 0:40:00
#SBATCH   -A YourProject

module load python3
python3 example_classic_launcher.py

PyLauncher File

where "example_classic_launcher.py" contains:

import pylauncher
pylauncher.ClassicLauncher("commandlines",debug="host+job")

Command Lines File

and "commandlines" contains your parameter sweep.

./myparallelprogram arg1 argA
./myparallelprogram arg1 argB
...

Debugging and tracing

If you want more detailed trace output during the run, add an option:

launcher.ClassicLauncher("commandlines",debug="host+job")

In the launcher invocation, the debug parameter causes trace output to be printed during the run. Example:

tick 104
Queue:
completed  60 jobs: 0-44 47-48 50-53 56 58 60-61 64 66 68 70 75
aborted     0 jobs:
queued      5 jobs: 99-103
running 39 jobs: 45-46 49 54-55 57 59 62-63 65 67 69 71-74 76-98

Which states that in the 104’th stage some jobs were completed/queued for running/actually running.

The tick message is output every half second. This can be changed, for instance to 1/10th of a second, by specifying delay=.1 in the launcher command. In some cases, for instance if each command is a python invocation that does many imports, you could increase the delay parameter.

Advanced PyLauncher usage

PyLauncher in an idev Session

PyLauncher creates a working directory with a name based on the SLURM job number. PyLauncher will also refuse to reuse a working directory. Together this has implications for running PyLauncher twice in an idev session: after the first run, the second run will complain that the working directory already exists. You have to delete it yourself, or explicitly designate a different working directory name in the launcher command:

pylauncher.ClassicLauncher( "mycommandlines",workdir=<unique name>).

Restart File

PyLauncher leaves behind a restart file titled "queuestate" that lists which commandlines were finished, and which ones were under way, or to be scheduled when the launcher job finished. You can use this in case your launcher job is killed for exceeding the time limit. You can then resume:

pylauncher.ResumeClassicLauncher("queuestate",debug="job")

The default name "queuestate" can be overridden by giving an explicit name

pylauncher.ClassicLauncher( "commandlines",queuestate="queustate5")

GPU Launcher

PyLauncher can handle programs that need a GPU. Use:

pylauncher.GPULauncher("gpucommandlines")

Submit Launcher

If your command lines take wildly different amounts of time a launcher job may be wasteful since it will leave cores (and nodes) unused while the longest running commandlines finish. One solution is the SubmitLauncher which runs outside of Slurm, and which submits Slurm jobs: For instance, the following command submits jobs to Frontera's small queue, and makes sure that the maximum queue limit of 2 nodes is not exceeded:

launcher.SubmitLauncher\
    ("commandlines",
    "-A YourProject -N 1 -n 1 -p small -t 0:15:0", # slurm arguments
    nactive=2, # queue limit
    )

Debugging PyLauncher Output

Each PyLauncher run stores output to a unique automatically generated subdirectory based on the job ID.

This directory contains three types of files:

• Files with your command lines as they are executed by the launcher. Names: exec0, exec1, etc.
• Time stamp files that the PyLauncher uses to determine whether commandlines have finished. Names: expire0, expire1, etc
• Standard out/error files. These can be useful if you observe that some commandlines don't finish or don't give the right result. Names: out0, out1, et.

Parameters

Here are some parameters that may sometimes come in handy.

References

• Github: PyLauncher
• YouTube: Intro to PyLauncher
• idev at TACC