Merge branch 'Parsl:master' into Glossary

docs/reference.rst

@@ -93,6 +93,7 @@ Launchers
     parsl.launchers.SrunMPILauncher
     parsl.launchers.GnuParallelLauncher
     parsl.launchers.MpiExecLauncher
+    parsl.launchers.MpiRunLauncher
     parsl.launchers.JsrunLauncher
     parsl.launchers.WrappedLauncher
 

docs/userguide/configuring.rst

@@ -536,12 +536,27 @@ Center's **Expanse** supercomputer. The example is designed to be executed on th
 .. literalinclude:: ../../parsl/configs/expanse.py
 
 
+Improv (Argonne LCRC)
+---------------------
+
+.. image:: https://www.lcrc.anl.gov/sites/default/files/styles/965_wide/public/2023-12/20231214_114057.jpg?itok=A-Rz5pP9
+
+**Improv** is a PBS Pro based  supercomputer at Argonne's Laboratory Computing Resource
+Center (LCRC). The following snippet is an example configuration that uses `parsl.providers.PBSProProvider`
+and `parsl.launchers.MpiRunLauncher` to run on multinode jobs.
+
+.. literalinclude:: ../../parsl/configs/improv.py
+
+
 .. _configuring_nersc_cori:
 
 Perlmutter (NERSC)
 ------------------
 
 NERSC provides documentation on `how to use Parsl on Perlmutter <https://docs.nersc.gov/jobs/workflow/parsl/>`_.
+Perlmutter is a Slurm based HPC system and parsl uses `parsl.providers.SlurmProvider` with `parsl.launchers.SrunLauncher`
+to launch tasks onto this machine.
+
 
 Frontera (TACC)
 ---------------
@@ -599,6 +614,8 @@ Polaris (ALCF)
     :width: 75%
 
 ALCF provides documentation on `how to use Parsl on Polaris <https://docs.alcf.anl.gov/polaris/workflows/parsl/>`_.
+Polaris uses `parsl.providers.PBSProProvider` and `parsl.launchers.MpiExecLauncher` to launch tasks onto the HPC system.
+
 
 
 Stampede2 (TACC)

parsl/configs/improv.py

@@ -0,0 +1,34 @@
+from parsl.config import Config
+from parsl.executors import HighThroughputExecutor
+from parsl.launchers import MpiRunLauncher
+from parsl.providers import PBSProProvider
+
+config = Config(
+        executors=[
+            HighThroughputExecutor(
+                label="Improv_multinode",
+                max_workers_per_node=32,
+                provider=PBSProProvider(
+                    account="YOUR_ALLOCATION_ON_IMPROV",
+                    # PBS directives (header lines), for example:
+                    # scheduler_options='#PBS -l mem=4gb',
+                    scheduler_options='',
+
+                    queue="compute",
+
+                    # Command to be run before starting a worker:
+                    # **WARNING** Improv requires an openmpi module to be
+                    # loaded for the MpiRunLauncher. Add additional env
+                    # load commands to this multiline string.
+                    worker_init='''
+module load gcc/13.2.0;
+module load openmpi/5.0.3-gcc-13.2.0; ''',
+                    launcher=MpiRunLauncher(),
+
+                    # number of compute nodes allocated for each block
+                    nodes_per_block=2,
+                    walltime='00:10:00'
+                ),
+            ),
+        ],
+)

parsl/executors/high_throughput/executor.py

@@ -790,7 +790,8 @@ def status(self) -> Dict[str, JobStatus]:
         connected_blocks = self.connected_blocks()
         for job_id in job_status:
             job_info = job_status[job_id]
-            if job_info.terminal and job_id not in connected_blocks:
+            if job_info.terminal and job_id not in connected_blocks and job_info.state != JobState.SCALED_IN:
+                logger.debug("Rewriting job %s from status %s to MISSING", job_id, job_info)
                 job_status[job_id].state = JobState.MISSING
                 if job_status[job_id].message is None:
                     job_status[job_id].message = (

parsl/executors/status_handling.py

@@ -347,7 +347,10 @@ def scale_in_facade(self, n: int, max_idletime: Optional[float] = None) -> List[
         if block_ids is not None:
             new_status = {}
             for block_id in block_ids:
-                new_status[block_id] = JobStatus(JobState.CANCELLED)
-                del self._status[block_id]
+                logger.debug("Marking block %s as SCALED_IN", block_id)
+                s = JobStatus(JobState.SCALED_IN)
+                new_status[block_id] = s
+                self._status[block_id] = s
+                self._simulated_status[block_id] = s
             self.send_monitoring_info(new_status)
         return block_ids

parsl/jobs/states.py

@@ -46,12 +46,17 @@ class JobState(IntEnum):
     bad worker environment or network connectivity issues.
     """
 
+    SCALED_IN = 9
+    """This job has been deliberately scaled in. Scaling code should not be concerned
+    that the job never ran (for example for error handling purposes).
+    """
+
     def __str__(self) -> str:
         return f"{self.__class__.__name__}.{self.name}"
 
 
 TERMINAL_STATES = [JobState.CANCELLED, JobState.COMPLETED, JobState.FAILED,
-                   JobState.TIMEOUT, JobState.MISSING]
+                   JobState.TIMEOUT, JobState.MISSING, JobState.SCALED_IN]
 
 
 class JobStatus:

parsl/tests/test_htex/test_multiple_disconnected_blocks.py

@@ -21,16 +21,14 @@ def local_config():
                 poll_period=100,
                 max_workers_per_node=1,
                 provider=LocalProvider(
-                    worker_init="conda deactivate; export PATH=''; which python; exit 0",
-                    init_blocks=2,
-                    max_blocks=4,
-                    min_blocks=0,
+                    worker_init="exit 0",
+                    init_blocks=2
                 ),
             )
         ],
         run_dir="/tmp/test_htex",
         max_idletime=0.5,
-        strategy='htex_auto_scale',
+        strategy='none',
     )
 
 

parsl/tests/test_monitoring/test_htex_init_blocks_vs_monitoring.py

@@ -78,6 +78,6 @@ def test_row_counts(tmpd_cwd, strategy):
         (c, ) = result.first()
         assert c == 1, "There should be a single pending status"
 
-        result = connection.execute(text("SELECT COUNT(*) FROM block WHERE block_id = 0 AND status = 'CANCELLED' AND run_id = :run_id"), binds)
+        result = connection.execute(text("SELECT COUNT(*) FROM block WHERE block_id = 0 AND status = 'SCALED_IN' AND run_id = :run_id"), binds)
         (c, ) = result.first()
         assert c == 1, "There should be a single cancelled status"

parsl/tests/test_scaling/test_regression_3568_scaledown_vs_MISSING.py

@@ -0,0 +1,85 @@
+import time
+
+import pytest
+
+import parsl
+from parsl.channels import LocalChannel
+from parsl.config import Config
+from parsl.executors import HighThroughputExecutor
+from parsl.launchers import WrappedLauncher
+from parsl.providers import LocalProvider
+
+
+def local_config():
+    # see the comments inside test_regression for reasoning about why each
+    # of these parameters is set why it is.
+    return Config(
+        max_idletime=1,
+
+        strategy='htex_auto_scale',
+        strategy_period=1,
+
+        executors=[
+            HighThroughputExecutor(
+                label="htex_local",
+                encrypted=True,
+                provider=LocalProvider(
+                    init_blocks=1,
+                    min_blocks=0,
+                    max_blocks=1,
+                    launcher=WrappedLauncher(prepend="sleep inf ; "),
+                ),
+            )
+        ],
+    )
+
+
+@parsl.python_app
+def task():
+    return 7
+
+
+@pytest.mark.local
+def test_regression(try_assert):
+    # The above config means that we should start scaling out one initial
+    # block, but then scale it back in after a second or so if the executor
+    # is kept idle (which this test does using try_assert).
+
+    # Because of 'sleep inf' in the WrappedLaucher, the block will not ever
+    # register.
+
+    # The bug being tested is about mistreatment of blocks which are scaled in
+    # before they have a chance to register, and the above forces that to
+    # happen.
+
+    # After that scaling in has happened, we should see that we have one block
+    # and it should be in a terminal state. The below try_assert waits for
+    # that to become true.
+
+    # At that time, we should also see htex reporting no blocks registered - as
+    # mentioned above, that is a necessary part of the bug being tested here.
+
+    # Give 10 strategy periods for the above to happen: each step of scale up,
+    # and scale down due to idleness isn't guaranteed to happen in exactly one
+    # scaling step.
+
+    htex = parsl.dfk().executors['htex_local']
+
+    try_assert(lambda: len(htex.status_facade) == 1 and htex.status_facade['0'].terminal,
+               timeout_ms=10000)
+
+    assert htex.connected_blocks() == [], "No block should have connected to interchange"
+
+    # Now we can reconfigure the launcher to let subsequent blocks launch ok,
+    # and run a trivial task. That trivial task will scale up a new block and
+    # run the task successfully.
+
+    # Prior to issue #3568, the bug was that the scale in of the first
+    # block earlier in the test case would have incorrectly been treated as a
+    # failure, and then the block error handler would have treated that failure
+    # as a permanent htex failure, and so the task execution below would raise
+    # a BadStateException rather than attempt to run the task.
+
+    assert htex.provider.launcher.prepend != "", "Pre-req: prepend attribute should exist and be non-empty"
+    htex.provider.launcher.prepend = ""
+    assert task().result() == 7