Merge pull request #1531 from pyiron/dependabot/pip/pympipool-0.9.1

Bump pympipool from 0.8.5 to 0.9.1

.ci_support/environment-docs.yml

@@ -19,7 +19,7 @@ dependencies:
 - psutil =6.0.0
 - pyfileindex =0.0.25
 - pyiron_snippets =0.1.2
-- pympipool =0.8.5
+- executorlib =0.0.1
 - pysqa =0.1.21
 - pytables =3.9.2
 - sqlalchemy =2.0.31

.ci_support/environment-old.yml

@@ -13,7 +13,7 @@ dependencies:
 - psutil =5.8.0
 - pyfileindex =0.0.16
 - pyiron_snippets =0.1.1
-- pympipool =0.8.0
+- executorlib =0.0.1
 - pysqa =0.1.12
 - pytables =3.6.1
 - sqlalchemy =2.0.22

.ci_support/environment.yml

@@ -17,7 +17,7 @@ dependencies:
 - psutil =6.0.0
 - pyfileindex =0.0.25
 - pyiron_snippets =0.1.2
-- pympipool =0.8.5
+- executorlib =0.0.1
 - pysqa =0.1.21
 - pytables =3.9.2
 - sqlalchemy =2.0.31

README.md

@@ -18,7 +18,7 @@ simulation in `pyiron_atomistics`.
   can be wrapped in `pyiron_base` to enable parameter studies with thousands or millions of calculation.
 * The calculation can either be executed locally on the same computer or on high performance computing (HPC) resources.
   The python simple queuing system adapter [pysqa](https://pysqa.readthedocs.io) is used to interface with the HPC 
-  queuing systems directly from python and the [pympipool](https://pympipool.readthedocs.io) package is employed to 
+  queuing systems directly from python and the [pympipool](https://executorlib.readthedocs.io) package is employed to 
   assign dedicated resources like multiple CPU cores and GPUs to individual python functions.
 * Scientific data is efficiently stored using the [hierarchical data format (HDF)](https://www.hdfgroup.org) via the 
   [h5py](https://www.h5py.org) python library and more specifically the [h5io](https://github.com/h5io) packages to 

binder/environment.yml

@@ -16,7 +16,7 @@ dependencies:
 - psutil =6.0.0
 - pyfileindex =0.0.25
 - pyiron_snippets =0.1.2
-- pympipool =0.8.5
+- executorlib =0.0.1
 - pysqa =0.1.21
 - pytables =3.9.2
 - sqlalchemy =2.0.31

docs/tutorial.md

@@ -131,7 +131,7 @@ selected. Still it is important to mention, that assigning 120 CPU cores does no
 function. Only by implementing internal parallelization inside the python functions with solutions like 
 [concurrent.futures.ProcessPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#processpoolexecutor)
 it is possible to parallelize the execution of python functions on a single compute node. Finally, the pyiron developers
-released the [pympipool](https://pympipool.readthedocs.io) to enable parallelization of python functions as well as the
+released the [pympipool](https://executorlib.readthedocs.io) to enable parallelization of python functions as well as the
 direct assignment of GPU resources inside a given queuing system allocation over multiple compute nodes using the 
 hierarchical queuing system [flux](https://flux-framework.org). 
 

pyiron_base/jobs/datamining.py

@@ -255,7 +255,7 @@ def create_table(self, file, job_status_list, executor=None, enforce_update=Fals
             The executor, if given, must not naively pickle the mapped functions or
             arguments, as PyironTable relies on lambda functions internally.  Use
             with executors that rely on dill or cloudpickle instead.  Pyiron
-            provides such executors in the `pympipool` sub packages.
+            provides such executors in the `executorlib` sub packages.
 
         Args:
             file (FileHDFio): HDF were the previous state of the table is stored
@@ -785,7 +785,7 @@ def update_table(self, job_status_list=None):
             self.project.db.item_update({"timestart": datetime.now()}, self.job_id)
         with self.project_hdf5.open("input") as hdf5_input:
             if self._executor_type is None and self.server.cores > 1:
-                self._executor_type = "pympipool.Executor"
+                self._executor_type = "executorlib.Executor"
             if self._executor_type is not None:
                 with self._get_executor(max_workers=self.server.cores) as exe:
                     self._pyiron_table.create_table(

pyiron_base/jobs/job/generic.py

@@ -1620,15 +1620,15 @@ def _get_executor(self, max_workers=None):
                 "No executor type defined - Please set self.executor_type."
             )
         elif (
-            self._executor_type == "pympipool.Executor"
+            self._executor_type == "executorlib.Executor"
             and platform.system() == "Darwin"
         ):
             # The Mac firewall might prevent connections based on the network address - especially Github CI
             return import_class(self._executor_type)(
                 max_cores=max_workers, hostname_localhost=True
             )
-        elif self._executor_type == "pympipool.Executor":
-            # The pympipool Executor defines max_cores rather than max_workers
+        elif self._executor_type == "executorlib.Executor":
+            # The executorlib Executor defines max_cores rather than max_workers
             return import_class(self._executor_type)(max_cores=max_workers)
         elif isinstance(self._executor_type, str):
             return import_class(self._executor_type)(max_workers=max_workers)

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["cloudpickle", "gitpython", "h5io", "h5py", "jinja2", "numpy", "pandas", "pint", "psutil", "pyfileindex", "pysqa", "sqlalchemy", "tables", "tqdm", "traitlets", "setuptools", "versioneer[toml]==0.29"]
+requires = ["cloudpickle", "executorlib", "gitpython", "h5io", "h5py", "jinja2", "numpy", "pandas", "pint", "psutil", "pyfileindex", "pysqa", "sqlalchemy", "tables", "tqdm", "traitlets", "setuptools", "versioneer[toml]==0.29"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -25,6 +25,7 @@ classifiers = [
 ]
 dependencies = [
     "cloudpickle==3.0.0",
+    "executorlib==0.0.1",
     "gitpython==3.1.43",
     "h5io_browser==0.0.15",
     "h5py==3.11.0",
@@ -36,7 +37,6 @@ dependencies = [
     "psutil==6.0.0",
     "pyfileindex==0.0.25",
     "pyiron_snippets==0.1.2",
-    "pympipool==0.8.5",
     "pysqa==0.1.21",
     "sqlalchemy==2.0.31",
     "tables==3.9.2",

tests/unit/table/test_datamining.py

@@ -10,7 +10,7 @@
 
 
 try:
-    import pympipool
+    import executorlib
 
     skip_parallel_test = False
 except ImportError:
@@ -73,7 +73,7 @@ def test_numpy_reload(self):
 
 @unittest.skipIf(
     skip_parallel_test,
-    "pympipool is not installed, so the pympipool based tests are skipped.",
+    "executorlib is not installed, so the executorlib based tests are skipped.",
 )
 class TestProjectDataParallel(TestWithProject):
     @classmethod