add slurm scripts

python/tffpy/experiments/exp_variance.py

@@ -53,7 +53,7 @@ class VarianceExperiment(SolveTffExperiment):
                               or_mask=True,
                               crop=None,
                               fig_dir=None)
-        solver_params = dict(tol_subregions=1e-5,
+        solver_params = dict(tol_subregions=[None, 1e-5],
                              tolerance_arrf=1e-3,
                              proba_arrf=1 - 1e-4,
                              rand_state=np.arange(100))

python/tffpy/experiments/tests/test_exp_variance.py

+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2020-2020
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * `Valentin Emiya <mailto:valentin.emiya@lis-lab.fr>`_
+# * `Ama Marina Krémé <mailto:ama-marina.kreme@lis-lab.fr>`_
+#
+# This package has been created thanks to the joint work with Florent Jaillet
+# and Ronan Hamon on other packages.
+#
+# Description
+# -----------
+#
+# Time frequency fading using Gabor multipliers
+#
+# Version
+# -------
+#
+# * tffpy version = 0.1.3
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+"""Test of the module :module:`tffpy.experiments.exp_variance`
+
+.. moduleauthor:: Valentin Emiya
+"""
+import unittest
+
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+mpl.rcParams['figure.max_open_warning'] = 40
+
+from tffpy.experiments.exp_variance import \
+    VarianceExperiment, create_and_run_light_experiment
+from tffpy.tests.ci_config import create_config_files
+
+
+class TestVarianceExperiment(unittest.TestCase):
+    def setUp(self):
+        create_config_files()
+
+    def test_light_experiment(self):
+        create_and_run_light_experiment()
+
+        light_exp = VarianceExperiment.get_experiment(
+            setting='light', force_reset=False)
+        for idt in range(light_exp.n_tasks):
+            light_exp.plot_task(idt=idt, fontsize=16)
+            plt.close('all')
+        light_exp.plot_results()
+        plt.close('all')
+
+    def test_create_full_experiment(self):
+        experiment = VarianceExperiment.get_experiment(
+            setting='full', force_reset=False)
+        experiment.display_status()

python/tffpy/experiments/yafe_slurm.py

+# -*- coding: utf-8 -*-
+"""
+
+.. moduleauthor:: Valentin Emiya
+"""
+
+import importlib
+import os
+from pathlib import Path
+import stat
+import sys
+
+
+def generate_slurm_script(script_file_path, xp_var_name, task_ids=None,
+                          n_simultaneous_jobs=10, slurm_walltime='02:00:00',
+                          activate_env_command=None, use_gpu=False):
+    """Generate a script to launch an experiment using Slurm.
+
+    Tasks are divided into batches that are executed by oar jobs.
+
+    The resulting script is written in the experiment folder, and the command
+    to launch the jobs with Slurm is displayed in the terminal.
+
+    An example script illustrating how to use
+    :func:`yafe.utils.generate_slurm_script` is available in the corresponding
+    :ref:`tutorial <tutorial_oar>`.
+
+    Parameters
+    ----------
+    script_file_path : str
+        File path to the script that defines the experiment.
+    xp_var_name : str
+        Name of the variable containing the experiment in the script.
+    task_ids : list
+        List of tasks ids to run.
+        If ``task_ids`` is ``None``, the list of pending tasks of the
+        experiment is used.
+    batch_size : int
+        Number of tasks run in each batch.
+    slurm_walltime : str
+        Wall time for each Slurm job ('HH:MM:SS').
+    activate_env_command : str or None
+        Optional command that must be run to activate a Python virtual
+        environment before launching the experiment.
+        Typically, this is a command of the form
+        ``source some_virtual_env/bin/activate`` when using virtualenv and
+        ``source activate some_conda_env`` when using conda.
+        If ``activate_env_command`` is ``None``, no virtual environment is
+        activated.
+    use_gpu : bool
+        Flag specifying if a gpu ressource is needed when running the
+        experiment.
+    """
+    # TODO update documentation
+    script_file_path = Path(script_file_path)
+    script_dir = script_file_path.parent
+    script_name = script_file_path.stem
+
+    sys.path.append(str(script_dir))
+    mod = importlib.import_module(script_name)
+    xp = getattr(mod, xp_var_name)
+    script_dir = xp.xp_path / 'job_scripts'
+    script_dir.mkdir(exist_ok=True)
+    for f in script_dir.glob('*.sh'):
+        os.remove(f)
+    # script_dir.rm
+
+    if task_ids is None:
+        task_ids = xp.get_pending_task_ids()
+
+    # split and save the tasks
+    # task_ids = list(map(str, task_ids))
+    # batches = [
+    #     task_ids[i:(i + batch_size)]
+    #     for i in range(0, len(task_ids), batch_size)
+    # ]
+    # file_path = xp.xp_path / 'listoftasks.txt'
+    #
+    # with open(str(file_path), 'wt') as fout:
+    #     fout.write('\n'.join(map(lambda batch: ','.join(batch), batches)))
+
+    # generate and save script
+    # script_path = Path(os.path.abspath(script_file_path))
+    # script_dir = script_path.parent
+    # script_name = script_path.stem
+
+    # Generate job script
+
+    script = '#!/bin/sh\n'
+
+    # define parameters
+    script += '#SBATCH --job-name={}\n'.format(xp.name)
+    script += '#SBATCH --array={}%{}\n'.format(
+        ','.join(str(i) for i in task_ids), n_simultaneous_jobs)
+    script += '#SBATCH --output={}/stdout_%A_%a.slurm\n'.format(xp.xp_path)
+    script += '#SBATCH --error={}/stderr_%A_%a.slurm\n'.format(xp.xp_path)
+    script += '#SBATCH --time={}\n'.format(slurm_walltime)
+    # if use_gpu:
+    #     script += '#SBATCH -p gpu IS NOT NULL\n'
+    # else:
+    #     script += '#SBATCH -p gpu IS NULL\n'
+
+    script += 'srun -N1 -n1 run_$SLURM_ARRAY_TASK_ID.sh'
+    # script += 'echo "OAR_JOB_ID: $OAR_JOB_ID"\n'
+    # script += 'echo "OAR_ARRAY_ID: $OAR_ARRAY_ID"\n'
+    # script += 'echo "SLURM_ARRAY_TASK_ID: $SLURM_ARRAY_TASK_ID"\n'
+
+    script_path = script_dir / 'script_slurm.sh'
+    with script_path.open('w') as file:
+        file.write(script)
+    status = os.stat(script_path)
+    os.chmod(script_path, status.st_mode | stat.S_IXUSR)
+
+    # Generate a script of each array element
+    for i_elt, idt in enumerate(task_ids):
+        script = '#!/bin/sh\n'
+
+        # activate the virtual env
+        if activate_env_command is not None and len(activate_env_command) > 0:
+            script += '{}\n'.format(activate_env_command)
+
+        # python command
+        script += 'echo "Running {}.launch_experiment(task_ids={})"\n'\
+            .format(xp_var_name, idt)
+        script += 'python -c "import sys; sys.path.append(\'{0}\'); ' \
+            'from {1} import {2}; ' \
+            '{2}.launch_experiment(task_ids={3})"\n'.format(
+                script_dir, script_name, xp_var_name, idt)
+        script += 'exit $?'
+
+        script_i_path = script_dir / 'run_{}.sh'.format(i_elt)
+        with script_i_path.open('w') as file:
+            file.write(script)
+        status = os.stat(script_i_path)
+        os.chmod(script_i_path, status.st_mode | stat.S_IXUSR)
+
+    print('*' * 80)
+    print('Submit the job array using:')
+    print('sbatch {}'.format(str(script_path)))
+    print('*' * 80)

python/tffpy/scripts/script_exp_variance.py

@@ -52,13 +52,11 @@ Run this script to handle the main experiment :class:`SolveTffExperiment`.
 
 .. moduleauthor:: Valentin Emiya
 """
-from yafe.utils import generate_oar_script
-
 import matplotlib.pyplot as plt
 
 from tffpy.experiments.exp_variance import \
     VarianceExperiment, create_and_run_light_experiment
-
+from tffpy.experiments.yafe_slurm import generate_slurm_script
 
 try:
     experiment = VarianceExperiment.get_experiment(setting='full',
@@ -74,7 +72,7 @@ if __name__ == '__main__':
         input_msg = '\n'.join(['1 - Create and run light experiment',
                                '2 - Display results of light experiment',
                                '3 - Full experiment: create full experiment',
-                               '4 - Generate OAR script',
+                               '4 - Generate Slurm script',
                                '5 - Full experiment: collect results',
                                '6 - Full experiment: download results',
                                '7 - Full experiment: display results',
@@ -98,11 +96,13 @@ if __name__ == '__main__':
             experiment.display_status()
         elif answer == 4:
             experiment.display_status()
-            batch_size = int(input('Batch size (#tasks per job)?'))
-            generate_oar_script(script_file_path=__file__,
+            n_simultaneous_jobs = int(
+                input('Max number of simultaneous jobs?'))
+            experiment.display_status()
+            generate_slurm_script(script_file_path=__file__,
                                   xp_var_name='experiment',
-                                batch_size=batch_size,
-                                oar_walltime='01:00:00',
+                                  n_simultaneous_jobs=n_simultaneous_jobs,
+                                  slurm_walltime='02:00:00',
                                   activate_env_command='source activate py36',
                                   use_gpu=False)
         elif answer == 5: