%global _empty_manifest_terminate_build 0
Name: python-simple-slurm
Version: 0.2.6
Release: 1
Summary: A simple Python wrapper for Slurm with flexibility in mind.
License: GNU Affero General Public License v3
URL: https://github.com/amq92/simple_slurm
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/00/65/d8cace7abf6a41ac4ae451736644898e786c90515acf31d23a292d0edc79/simple_slurm-0.2.6.tar.gz
BuildArch: noarch
%description
Simple Slurm
A simple Python wrapper for Slurm with flexibility in mind
```python
import datetime
from simple_slurm import Slurm
slurm = Slurm(
array=range(3, 12),
cpus_per_task=15,
dependency=dict(after=65541, afterok=34987),
gres=['gpu:kepler:2', 'gpu:tesla:2', 'mps:400'],
ignore_pbs=True,
job_name='name',
output=f'{Slurm.JOB_ARRAY_MASTER_ID}_{Slurm.JOB_ARRAY_ID}.out',
time=datetime.timedelta(days=1, hours=2, minutes=3, seconds=4),
)
slurm.sbatch('python demo.py ' + Slurm.SLURM_ARRAY_TASK_ID)
```
The above snippet is equivalent to running the following command:
```bash
sbatch << EOF
#!/bin/sh
#SBATCH --array 3-11
#SBATCH --cpus-per-task 15
#SBATCH --dependency after:65541,afterok:34987
#SBATCH --gres gpu:kepler:2,gpu:tesla:2,mps:400
#SBATCH --ignore-pbs
#SBATCH --job-name name
#SBATCH --output %A_%a.out
#SBATCH --time 1-02:03:04
python demo.py \$SLURM_ARRAY_TASK_ID
EOF
```
## Contents
+ [Introduction](#introduction)
+ [Installation instructions](#installation-instructions)
+ [Many syntaxes available](#many-syntaxes-available)
- [Using configuration files](#using-configuration-files)
- [Using the command line](#using-the-command-line)
+ [Job dependencies](#job-dependencies)
+ [Additional features](#additional-features)
- [Filename Patterns](#filename-patterns)
- [Output Environment Variables](#output-environment-variables)
## Introduction
The [`sbatch`](https://slurm.schedmd.com/sbatch.html) and [`srun`](https://slurm.schedmd.com/srun.html) commands in [Slurm](https://slurm.schedmd.com/overview.html) allow submitting parallel jobs into a Linux cluster in the form of batch scripts that follow a certain structure.
The goal of this library is to provide a simple wrapper for these functions (`sbatch` and `srun`) so that Python code can be used for constructing and launching the aforementioned batch script.
Indeed, the generated batch script can be shown by printing the `Slurm` object:
```python
from simple_slurm import Slurm
slurm = Slurm(array=range(3, 12), job_name='name')
print(slurm)
```
```bash
>> #!/bin/sh
>>
>> #SBATCH --array 3-11
>> #SBATCH --job-name name
```
Then, the job can be launched with either command:
```python
slurm.srun('echo hello!')
slurm.sbatch('echo hello!')
```
```bash
>> Submitted batch job 34987
```
While both commands are quite similar, [`srun`](https://slurm.schedmd.com/srun.html) will wait for the job completion, while [`sbatch`](https://slurm.schedmd.com/sbatch.html) will launch and disconnect from the jobs.
> More information can be found in [Slurm's Quick Start Guide](https://slurm.schedmd.com/quickstart.html) and in [here](https://stackoverflow.com/questions/43767866/slurm-srun-vs-sbatch-and-their-parameters).
## Installation instructions
From PyPI
```bash
pip install simple_slurm
```
From Conda
```bash
conda install -c conda-forge simple_slurm
```
From git
```bash
pip install git+https://github.com/amq92/simple_slurm.git
```
## Many syntaxes available
```python
slurm = Slurm('-a', '3-11')
slurm = Slurm('--array', '3-11')
slurm = Slurm('array', '3-11')
slurm = Slurm(array='3-11')
slurm = Slurm(array=range(3, 12))
slurm.add_arguments(array=range(3, 12))
slurm.set_array(range(3, 12))
```
All these arguments are equivalent!
It's up to you to choose the one(s) that best suits you needs.
> *"With great flexibility comes great responsability"*
You can either keep a command-line-like syntax or a more Python-like one
```python
slurm = Slurm()
slurm.set_dependency('after:65541,afterok:34987')
slurm.set_dependency(['after:65541', 'afterok:34987'])
slurm.set_dependency(dict(after=65541, afterok=34987))
```
All the possible arguments have their own setter methods
(ex. `set_array`, `set_dependency`, `set_job_name`).
Please note that hyphenated arguments, such as `--job-name`, need to be underscored
(so to comply with Python syntax and be coherent).
```python
slurm = Slurm('--job_name', 'name')
slurm = Slurm(job_name='name')
# slurm = Slurm('--job-name', 'name') # NOT VALID
# slurm = Slurm(job-name='name') # NOT VALID
```
Moreover, boolean arguments such as `--contiguous`, `--ignore_pbs` or `--overcommit`
can be activated with `True` or an empty string.
```python
slurm = Slurm('--contiguous', True)
slurm.add_arguments(ignore_pbs='')
slurm.set_wait(False)
print(slurm)
```
```bash
#!/bin/sh
#SBATCH --contiguous
#SBATCH --ignore-pbs
```
### Using configuration files
Let's define the *static* components of a job definition in a YAML file `default.slurm`
```yaml
cpus_per_task: 15
job_name: 'name'
output: '%A_%a.out'
```
Including these options with the using the `yaml` package is very *simple*
```python
import yaml
from simple_slurm import Slurm
slurm = Slurm(**yaml.load(open('default.slurm')))
...
slurm.set_array(range(NUMBER_OF_SIMULATIONS))
```
The job can be updated according to the *dynamic* project needs (ex. `NUMBER_OF_SIMULATIONS`).
### Using the command line
For simpler dispatch jobs, a comand line entry point is also made available.
```bash
simple_slurm [OPTIONS] "COMMAND_TO_RUN_WITH_SBATCH"
```
As such, both of these `python` and `bash` calls are equivalent.
```python
slurm = Slurm(partition='compute.p', output='slurm.log', ignore_pbs=True)
slurm.sbatch('echo \$HOSTNAME')
```
```bash
simple_slurm --partition=compute.p --output slurm.log --ignore_pbs "echo \$HOSTNAME"
```
## Job dependencies
The `sbatch` call prints a message if successful and returns the corresponding `job_id`
```python
job_id = slurm.sbatch('python demo.py ' + Slurm.SLURM_ARRAY_TAKSK_ID)
```
If the job submission was successful, it prints:
```
Submitted batch job 34987
```
And returns the variable `job_id = 34987`, which can be used for setting dependencies on subsequent jobs
```python
slurm_after = Slurm(dependency=dict(afterok=job_id)))
```
## Additional features
For convenience, Filename Patterns and Output Environment Variables are available as attributes of the Simple Slurm object.
See [https://slurm.schedmd.com/sbatch.html](https://slurm.schedmd.com/sbatch.html#lbAH) for details on the commands.
```python
from slurm import Slurm
slurm = Slurm(output=('{}_{}.out'.format(
Slurm.JOB_ARRAY_MASTER_ID,
Slurm.JOB_ARRAY_ID))
slurm.sbatch('python demo.py ' + slurm.SLURM_ARRAY_JOB_ID)
```
This example would result in output files of the form `65541_15.out`.
Here the job submission ID is `65541`, and this output file corresponds to the submission number `15` in the job array. Moreover, this index is passed to the Python code `demo.py` as an argument.
> Note that they can be accessed either as `Slurm.` or `slurm.`, here `slurm` is an instance of the `Slurm` class.
### Filename Patterns
`sbatch` allows for a filename pattern to contain one or more replacement symbols.
They can be accessed with `Slurm.`
name | value | description
:-------------------|------:|:-----------
JOB_ARRAY_MASTER_ID | %A | job array's master job allocation number
JOB_ARRAY_ID | %a | job array id (index) number
JOB_ID_STEP_ID | %J | jobid.stepid of the running job. (e.g. "128.0")
JOB_ID | %j | jobid of the running job
HOSTNAME | %N | short hostname. this will create a separate io file per node
NODE_IDENTIFIER | %n | node identifier relative to current job (e.g. "0" is the first node of the running job) this will create a separate io file per node
STEP_ID | %s | stepid of the running job
TASK_IDENTIFIER | %t | task identifier (rank) relative to current job. this will create a separate io file per task
USER_NAME | %u | user name
JOB_NAME | %x | job name
PERCENTAGE | %% | the character "%"
DO_NOT_PROCESS | \\\\ | do not process any of the replacement symbols
### Output Environment Variables
The Slurm controller will set the following variables in the environment of the batch script.
They can be accessed with `Slurm.`.
name | description
:----------------------|:-----------
SLURM_ARRAY_TASK_COUNT | total number of tasks in a job array
SLURM_ARRAY_TASK_ID | job array id (index) number
SLURM_ARRAY_TASK_MAX | job array's maximum id (index) number
SLURM_ARRAY_TASK_MIN | job array's minimum id (index) number
SLURM_ARRAY_TASK_STEP | job array's index step size
SLURM_ARRAY_JOB_ID | job array's master job id number
... | ...
See [https://slurm.schedmd.com/sbatch.html](https://slurm.schedmd.com/sbatch.html#lbAK) for a complete list.
%package -n python3-simple-slurm
Summary: A simple Python wrapper for Slurm with flexibility in mind.
Provides: python-simple-slurm
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-simple-slurm
Simple Slurm
A simple Python wrapper for Slurm with flexibility in mind
```python
import datetime
from simple_slurm import Slurm
slurm = Slurm(
array=range(3, 12),
cpus_per_task=15,
dependency=dict(after=65541, afterok=34987),
gres=['gpu:kepler:2', 'gpu:tesla:2', 'mps:400'],
ignore_pbs=True,
job_name='name',
output=f'{Slurm.JOB_ARRAY_MASTER_ID}_{Slurm.JOB_ARRAY_ID}.out',
time=datetime.timedelta(days=1, hours=2, minutes=3, seconds=4),
)
slurm.sbatch('python demo.py ' + Slurm.SLURM_ARRAY_TASK_ID)
```
The above snippet is equivalent to running the following command:
```bash
sbatch << EOF
#!/bin/sh
#SBATCH --array 3-11
#SBATCH --cpus-per-task 15
#SBATCH --dependency after:65541,afterok:34987
#SBATCH --gres gpu:kepler:2,gpu:tesla:2,mps:400
#SBATCH --ignore-pbs
#SBATCH --job-name name
#SBATCH --output %A_%a.out
#SBATCH --time 1-02:03:04
python demo.py \$SLURM_ARRAY_TASK_ID
EOF
```
## Contents
+ [Introduction](#introduction)
+ [Installation instructions](#installation-instructions)
+ [Many syntaxes available](#many-syntaxes-available)
- [Using configuration files](#using-configuration-files)
- [Using the command line](#using-the-command-line)
+ [Job dependencies](#job-dependencies)
+ [Additional features](#additional-features)
- [Filename Patterns](#filename-patterns)
- [Output Environment Variables](#output-environment-variables)
## Introduction
The [`sbatch`](https://slurm.schedmd.com/sbatch.html) and [`srun`](https://slurm.schedmd.com/srun.html) commands in [Slurm](https://slurm.schedmd.com/overview.html) allow submitting parallel jobs into a Linux cluster in the form of batch scripts that follow a certain structure.
The goal of this library is to provide a simple wrapper for these functions (`sbatch` and `srun`) so that Python code can be used for constructing and launching the aforementioned batch script.
Indeed, the generated batch script can be shown by printing the `Slurm` object:
```python
from simple_slurm import Slurm
slurm = Slurm(array=range(3, 12), job_name='name')
print(slurm)
```
```bash
>> #!/bin/sh
>>
>> #SBATCH --array 3-11
>> #SBATCH --job-name name
```
Then, the job can be launched with either command:
```python
slurm.srun('echo hello!')
slurm.sbatch('echo hello!')
```
```bash
>> Submitted batch job 34987
```
While both commands are quite similar, [`srun`](https://slurm.schedmd.com/srun.html) will wait for the job completion, while [`sbatch`](https://slurm.schedmd.com/sbatch.html) will launch and disconnect from the jobs.
> More information can be found in [Slurm's Quick Start Guide](https://slurm.schedmd.com/quickstart.html) and in [here](https://stackoverflow.com/questions/43767866/slurm-srun-vs-sbatch-and-their-parameters).
## Installation instructions
From PyPI
```bash
pip install simple_slurm
```
From Conda
```bash
conda install -c conda-forge simple_slurm
```
From git
```bash
pip install git+https://github.com/amq92/simple_slurm.git
```
## Many syntaxes available
```python
slurm = Slurm('-a', '3-11')
slurm = Slurm('--array', '3-11')
slurm = Slurm('array', '3-11')
slurm = Slurm(array='3-11')
slurm = Slurm(array=range(3, 12))
slurm.add_arguments(array=range(3, 12))
slurm.set_array(range(3, 12))
```
All these arguments are equivalent!
It's up to you to choose the one(s) that best suits you needs.
> *"With great flexibility comes great responsability"*
You can either keep a command-line-like syntax or a more Python-like one
```python
slurm = Slurm()
slurm.set_dependency('after:65541,afterok:34987')
slurm.set_dependency(['after:65541', 'afterok:34987'])
slurm.set_dependency(dict(after=65541, afterok=34987))
```
All the possible arguments have their own setter methods
(ex. `set_array`, `set_dependency`, `set_job_name`).
Please note that hyphenated arguments, such as `--job-name`, need to be underscored
(so to comply with Python syntax and be coherent).
```python
slurm = Slurm('--job_name', 'name')
slurm = Slurm(job_name='name')
# slurm = Slurm('--job-name', 'name') # NOT VALID
# slurm = Slurm(job-name='name') # NOT VALID
```
Moreover, boolean arguments such as `--contiguous`, `--ignore_pbs` or `--overcommit`
can be activated with `True` or an empty string.
```python
slurm = Slurm('--contiguous', True)
slurm.add_arguments(ignore_pbs='')
slurm.set_wait(False)
print(slurm)
```
```bash
#!/bin/sh
#SBATCH --contiguous
#SBATCH --ignore-pbs
```
### Using configuration files
Let's define the *static* components of a job definition in a YAML file `default.slurm`
```yaml
cpus_per_task: 15
job_name: 'name'
output: '%A_%a.out'
```
Including these options with the using the `yaml` package is very *simple*
```python
import yaml
from simple_slurm import Slurm
slurm = Slurm(**yaml.load(open('default.slurm')))
...
slurm.set_array(range(NUMBER_OF_SIMULATIONS))
```
The job can be updated according to the *dynamic* project needs (ex. `NUMBER_OF_SIMULATIONS`).
### Using the command line
For simpler dispatch jobs, a comand line entry point is also made available.
```bash
simple_slurm [OPTIONS] "COMMAND_TO_RUN_WITH_SBATCH"
```
As such, both of these `python` and `bash` calls are equivalent.
```python
slurm = Slurm(partition='compute.p', output='slurm.log', ignore_pbs=True)
slurm.sbatch('echo \$HOSTNAME')
```
```bash
simple_slurm --partition=compute.p --output slurm.log --ignore_pbs "echo \$HOSTNAME"
```
## Job dependencies
The `sbatch` call prints a message if successful and returns the corresponding `job_id`
```python
job_id = slurm.sbatch('python demo.py ' + Slurm.SLURM_ARRAY_TAKSK_ID)
```
If the job submission was successful, it prints:
```
Submitted batch job 34987
```
And returns the variable `job_id = 34987`, which can be used for setting dependencies on subsequent jobs
```python
slurm_after = Slurm(dependency=dict(afterok=job_id)))
```
## Additional features
For convenience, Filename Patterns and Output Environment Variables are available as attributes of the Simple Slurm object.
See [https://slurm.schedmd.com/sbatch.html](https://slurm.schedmd.com/sbatch.html#lbAH) for details on the commands.
```python
from slurm import Slurm
slurm = Slurm(output=('{}_{}.out'.format(
Slurm.JOB_ARRAY_MASTER_ID,
Slurm.JOB_ARRAY_ID))
slurm.sbatch('python demo.py ' + slurm.SLURM_ARRAY_JOB_ID)
```
This example would result in output files of the form `65541_15.out`.
Here the job submission ID is `65541`, and this output file corresponds to the submission number `15` in the job array. Moreover, this index is passed to the Python code `demo.py` as an argument.
> Note that they can be accessed either as `Slurm.` or `slurm.`, here `slurm` is an instance of the `Slurm` class.
### Filename Patterns
`sbatch` allows for a filename pattern to contain one or more replacement symbols.
They can be accessed with `Slurm.`
name | value | description
:-------------------|------:|:-----------
JOB_ARRAY_MASTER_ID | %A | job array's master job allocation number
JOB_ARRAY_ID | %a | job array id (index) number
JOB_ID_STEP_ID | %J | jobid.stepid of the running job. (e.g. "128.0")
JOB_ID | %j | jobid of the running job
HOSTNAME | %N | short hostname. this will create a separate io file per node
NODE_IDENTIFIER | %n | node identifier relative to current job (e.g. "0" is the first node of the running job) this will create a separate io file per node
STEP_ID | %s | stepid of the running job
TASK_IDENTIFIER | %t | task identifier (rank) relative to current job. this will create a separate io file per task
USER_NAME | %u | user name
JOB_NAME | %x | job name
PERCENTAGE | %% | the character "%"
DO_NOT_PROCESS | \\\\ | do not process any of the replacement symbols
### Output Environment Variables
The Slurm controller will set the following variables in the environment of the batch script.
They can be accessed with `Slurm.`.
name | description
:----------------------|:-----------
SLURM_ARRAY_TASK_COUNT | total number of tasks in a job array
SLURM_ARRAY_TASK_ID | job array id (index) number
SLURM_ARRAY_TASK_MAX | job array's maximum id (index) number
SLURM_ARRAY_TASK_MIN | job array's minimum id (index) number
SLURM_ARRAY_TASK_STEP | job array's index step size
SLURM_ARRAY_JOB_ID | job array's master job id number
... | ...
See [https://slurm.schedmd.com/sbatch.html](https://slurm.schedmd.com/sbatch.html#lbAK) for a complete list.
%package help
Summary: Development documents and examples for simple-slurm
Provides: python3-simple-slurm-doc
%description help
Simple Slurm
A simple Python wrapper for Slurm with flexibility in mind
```python
import datetime
from simple_slurm import Slurm
slurm = Slurm(
array=range(3, 12),
cpus_per_task=15,
dependency=dict(after=65541, afterok=34987),
gres=['gpu:kepler:2', 'gpu:tesla:2', 'mps:400'],
ignore_pbs=True,
job_name='name',
output=f'{Slurm.JOB_ARRAY_MASTER_ID}_{Slurm.JOB_ARRAY_ID}.out',
time=datetime.timedelta(days=1, hours=2, minutes=3, seconds=4),
)
slurm.sbatch('python demo.py ' + Slurm.SLURM_ARRAY_TASK_ID)
```
The above snippet is equivalent to running the following command:
```bash
sbatch << EOF
#!/bin/sh
#SBATCH --array 3-11
#SBATCH --cpus-per-task 15
#SBATCH --dependency after:65541,afterok:34987
#SBATCH --gres gpu:kepler:2,gpu:tesla:2,mps:400
#SBATCH --ignore-pbs
#SBATCH --job-name name
#SBATCH --output %A_%a.out
#SBATCH --time 1-02:03:04
python demo.py \$SLURM_ARRAY_TASK_ID
EOF
```
## Contents
+ [Introduction](#introduction)
+ [Installation instructions](#installation-instructions)
+ [Many syntaxes available](#many-syntaxes-available)
- [Using configuration files](#using-configuration-files)
- [Using the command line](#using-the-command-line)
+ [Job dependencies](#job-dependencies)
+ [Additional features](#additional-features)
- [Filename Patterns](#filename-patterns)
- [Output Environment Variables](#output-environment-variables)
## Introduction
The [`sbatch`](https://slurm.schedmd.com/sbatch.html) and [`srun`](https://slurm.schedmd.com/srun.html) commands in [Slurm](https://slurm.schedmd.com/overview.html) allow submitting parallel jobs into a Linux cluster in the form of batch scripts that follow a certain structure.
The goal of this library is to provide a simple wrapper for these functions (`sbatch` and `srun`) so that Python code can be used for constructing and launching the aforementioned batch script.
Indeed, the generated batch script can be shown by printing the `Slurm` object:
```python
from simple_slurm import Slurm
slurm = Slurm(array=range(3, 12), job_name='name')
print(slurm)
```
```bash
>> #!/bin/sh
>>
>> #SBATCH --array 3-11
>> #SBATCH --job-name name
```
Then, the job can be launched with either command:
```python
slurm.srun('echo hello!')
slurm.sbatch('echo hello!')
```
```bash
>> Submitted batch job 34987
```
While both commands are quite similar, [`srun`](https://slurm.schedmd.com/srun.html) will wait for the job completion, while [`sbatch`](https://slurm.schedmd.com/sbatch.html) will launch and disconnect from the jobs.
> More information can be found in [Slurm's Quick Start Guide](https://slurm.schedmd.com/quickstart.html) and in [here](https://stackoverflow.com/questions/43767866/slurm-srun-vs-sbatch-and-their-parameters).
## Installation instructions
From PyPI
```bash
pip install simple_slurm
```
From Conda
```bash
conda install -c conda-forge simple_slurm
```
From git
```bash
pip install git+https://github.com/amq92/simple_slurm.git
```
## Many syntaxes available
```python
slurm = Slurm('-a', '3-11')
slurm = Slurm('--array', '3-11')
slurm = Slurm('array', '3-11')
slurm = Slurm(array='3-11')
slurm = Slurm(array=range(3, 12))
slurm.add_arguments(array=range(3, 12))
slurm.set_array(range(3, 12))
```
All these arguments are equivalent!
It's up to you to choose the one(s) that best suits you needs.
> *"With great flexibility comes great responsability"*
You can either keep a command-line-like syntax or a more Python-like one
```python
slurm = Slurm()
slurm.set_dependency('after:65541,afterok:34987')
slurm.set_dependency(['after:65541', 'afterok:34987'])
slurm.set_dependency(dict(after=65541, afterok=34987))
```
All the possible arguments have their own setter methods
(ex. `set_array`, `set_dependency`, `set_job_name`).
Please note that hyphenated arguments, such as `--job-name`, need to be underscored
(so to comply with Python syntax and be coherent).
```python
slurm = Slurm('--job_name', 'name')
slurm = Slurm(job_name='name')
# slurm = Slurm('--job-name', 'name') # NOT VALID
# slurm = Slurm(job-name='name') # NOT VALID
```
Moreover, boolean arguments such as `--contiguous`, `--ignore_pbs` or `--overcommit`
can be activated with `True` or an empty string.
```python
slurm = Slurm('--contiguous', True)
slurm.add_arguments(ignore_pbs='')
slurm.set_wait(False)
print(slurm)
```
```bash
#!/bin/sh
#SBATCH --contiguous
#SBATCH --ignore-pbs
```
### Using configuration files
Let's define the *static* components of a job definition in a YAML file `default.slurm`
```yaml
cpus_per_task: 15
job_name: 'name'
output: '%A_%a.out'
```
Including these options with the using the `yaml` package is very *simple*
```python
import yaml
from simple_slurm import Slurm
slurm = Slurm(**yaml.load(open('default.slurm')))
...
slurm.set_array(range(NUMBER_OF_SIMULATIONS))
```
The job can be updated according to the *dynamic* project needs (ex. `NUMBER_OF_SIMULATIONS`).
### Using the command line
For simpler dispatch jobs, a comand line entry point is also made available.
```bash
simple_slurm [OPTIONS] "COMMAND_TO_RUN_WITH_SBATCH"
```
As such, both of these `python` and `bash` calls are equivalent.
```python
slurm = Slurm(partition='compute.p', output='slurm.log', ignore_pbs=True)
slurm.sbatch('echo \$HOSTNAME')
```
```bash
simple_slurm --partition=compute.p --output slurm.log --ignore_pbs "echo \$HOSTNAME"
```
## Job dependencies
The `sbatch` call prints a message if successful and returns the corresponding `job_id`
```python
job_id = slurm.sbatch('python demo.py ' + Slurm.SLURM_ARRAY_TAKSK_ID)
```
If the job submission was successful, it prints:
```
Submitted batch job 34987
```
And returns the variable `job_id = 34987`, which can be used for setting dependencies on subsequent jobs
```python
slurm_after = Slurm(dependency=dict(afterok=job_id)))
```
## Additional features
For convenience, Filename Patterns and Output Environment Variables are available as attributes of the Simple Slurm object.
See [https://slurm.schedmd.com/sbatch.html](https://slurm.schedmd.com/sbatch.html#lbAH) for details on the commands.
```python
from slurm import Slurm
slurm = Slurm(output=('{}_{}.out'.format(
Slurm.JOB_ARRAY_MASTER_ID,
Slurm.JOB_ARRAY_ID))
slurm.sbatch('python demo.py ' + slurm.SLURM_ARRAY_JOB_ID)
```
This example would result in output files of the form `65541_15.out`.
Here the job submission ID is `65541`, and this output file corresponds to the submission number `15` in the job array. Moreover, this index is passed to the Python code `demo.py` as an argument.
> Note that they can be accessed either as `Slurm.` or `slurm.`, here `slurm` is an instance of the `Slurm` class.
### Filename Patterns
`sbatch` allows for a filename pattern to contain one or more replacement symbols.
They can be accessed with `Slurm.`
name | value | description
:-------------------|------:|:-----------
JOB_ARRAY_MASTER_ID | %A | job array's master job allocation number
JOB_ARRAY_ID | %a | job array id (index) number
JOB_ID_STEP_ID | %J | jobid.stepid of the running job. (e.g. "128.0")
JOB_ID | %j | jobid of the running job
HOSTNAME | %N | short hostname. this will create a separate io file per node
NODE_IDENTIFIER | %n | node identifier relative to current job (e.g. "0" is the first node of the running job) this will create a separate io file per node
STEP_ID | %s | stepid of the running job
TASK_IDENTIFIER | %t | task identifier (rank) relative to current job. this will create a separate io file per task
USER_NAME | %u | user name
JOB_NAME | %x | job name
PERCENTAGE | %% | the character "%"
DO_NOT_PROCESS | \\\\ | do not process any of the replacement symbols
### Output Environment Variables
The Slurm controller will set the following variables in the environment of the batch script.
They can be accessed with `Slurm.`.
name | description
:----------------------|:-----------
SLURM_ARRAY_TASK_COUNT | total number of tasks in a job array
SLURM_ARRAY_TASK_ID | job array id (index) number
SLURM_ARRAY_TASK_MAX | job array's maximum id (index) number
SLURM_ARRAY_TASK_MIN | job array's minimum id (index) number
SLURM_ARRAY_TASK_STEP | job array's index step size
SLURM_ARRAY_JOB_ID | job array's master job id number
... | ...
See [https://slurm.schedmd.com/sbatch.html](https://slurm.schedmd.com/sbatch.html#lbAK) for a complete list.
%prep
%autosetup -n simple-slurm-0.2.6
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-simple-slurm -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot - 0.2.6-1
- Package Spec generated