Clean up: warn when functionality is not available

executorlib/__init__.py

@@ -8,20 +8,15 @@
     SlurmClusterExecutor,
     SlurmJobExecutor,
 )
+from executorlib.standalone.cache import get_cache_data
 
 __all__: list[str] = [
+    "get_cache_data",
     "FluxJobExecutor",
     "FluxClusterExecutor",
     "SingleNodeExecutor",
     "SlurmJobExecutor",
     "SlurmClusterExecutor",
 ]
 
-try:
-    from executorlib.standalone.hdf import get_cache_data
-except ImportError:
-    pass
-else:
-    __all__ += ["get_cache_data"]
-
 __version__ = _get_versions()["version"]

executorlib/executor/flux.py

@@ -1,4 +1,3 @@
-import contextlib
 from typing import Callable, Optional, Union
 
 from executorlib.executor.base import ExecutorBase
@@ -17,12 +16,6 @@
 from executorlib.task_scheduler.interactive.dependency import DependencyTaskScheduler
 from executorlib.task_scheduler.interactive.onetoone import OneProcessTaskScheduler
 
-with contextlib.suppress(ImportError):
-    from executorlib.task_scheduler.interactive.fluxspawner import (
-        FluxPythonSpawner,
-        validate_max_workers,
-    )
-
 
 class FluxJobExecutor(ExecutorBase):
     """
@@ -440,6 +433,11 @@ def create_flux_executor(
     Returns:
         InteractiveStepExecutor/ InteractiveExecutor
     """
+    from executorlib.task_scheduler.interactive.fluxspawner import (
+        FluxPythonSpawner,
+        validate_max_workers,
+    )
+
     if resource_dict is None:
         resource_dict = {}
     cores_per_worker = resource_dict.get("cores", 1)

executorlib/standalone/cache.py

@@ -0,0 +1,42 @@
+import os
+
+import cloudpickle
+import numpy as np
+
+group_dict = {
+    "fn": "function",
+    "args": "input_args",
+    "kwargs": "input_kwargs",
+    "output": "output",
+    "error": "error",
+    "runtime": "runtime",
+    "queue_id": "queue_id",
+}
+
+
+def get_cache_data(cache_directory: str) -> list[dict]:
+    """
+    Collect all HDF5 files in the cache directory
+
+    Args:
+        cache_directory (str): The directory to store cache files.
+
+    Returns:
+        list[dict]: List of dictionaries each representing on of the HDF5 files in the cache directory.
+    """
+    import h5py
+
+    file_lst = []
+    for task_key in os.listdir(cache_directory):
+        file_name = os.path.join(cache_directory, task_key, "cache.h5out")
+        os.makedirs(os.path.join(cache_directory, task_key), exist_ok=True)
+        if os.path.exists(file_name):
+            with h5py.File(file_name, "r") as hdf:
+                file_content_dict = {
+                    key: cloudpickle.loads(np.void(hdf["/" + key]))
+                    for key in group_dict.values()
+                    if key in hdf
+                }
+            file_content_dict["filename"] = file_name
+            file_lst.append(file_content_dict)
+    return file_lst

executorlib/task_scheduler/file/backend.py

@@ -2,7 +2,7 @@
 import time
 from typing import Any
 
-from executorlib.standalone.hdf import dump, load
+from executorlib.task_scheduler.file.hdf import dump, load
 from executorlib.task_scheduler.file.shared import FutureItem
 
 

executorlib/standalone/hdf.py → executorlib/task_scheduler/file/hdf.py

@@ -1,19 +1,10 @@
-import os
 from typing import Any, Optional
 
 import cloudpickle
 import h5py
 import numpy as np
 
-group_dict = {
-    "fn": "function",
-    "args": "input_args",
-    "kwargs": "input_kwargs",
-    "output": "output",
-    "error": "error",
-    "runtime": "runtime",
-    "queue_id": "queue_id",
-}
+from executorlib.standalone.cache import group_dict
 
 
 def dump(file_name: Optional[str], data_dict: dict) -> None:
@@ -98,25 +89,17 @@ def get_runtime(file_name: str) -> float:
 
 
 def get_queue_id(file_name: Optional[str]) -> Optional[int]:
+    """
+    Get queuing system id from HDF5 file
+
+    Args:
+        file_name (str): file name of the HDF5 file as absolute path
+
+    Returns:
+        int: queuing system id from the execution of the python function
+    """
     if file_name is not None:
         with h5py.File(file_name, "r") as hdf:
             if "queue_id" in hdf:
                 return cloudpickle.loads(np.void(hdf["/queue_id"]))
     return None
-
-
-def get_cache_data(cache_directory: str) -> list[dict]:
-    file_lst = []
-    for task_key in os.listdir(cache_directory):
-        file_name = os.path.join(cache_directory, task_key, "cache.h5out")
-        os.makedirs(os.path.join(cache_directory, task_key), exist_ok=True)
-        if os.path.exists(file_name):
-            with h5py.File(file_name, "r") as hdf:
-                file_content_dict = {
-                    key: cloudpickle.loads(np.void(hdf["/" + key]))
-                    for key in group_dict.values()
-                    if key in hdf
-                }
-            file_content_dict["filename"] = file_name
-            file_lst.append(file_content_dict)
-    return file_lst

executorlib/task_scheduler/file/queue_spawner.py

@@ -4,8 +4,8 @@
 
 from pysqa import QueueAdapter
 
-from executorlib.standalone.hdf import dump, get_queue_id
 from executorlib.standalone.inputcheck import check_file_exists
+from executorlib.task_scheduler.file.hdf import dump, get_queue_id
 
 
 def execute_with_pysqa(

executorlib/task_scheduler/file/shared.py

@@ -7,8 +7,8 @@
 from typing import Any, Callable, Optional
 
 from executorlib.standalone.command import get_command_path
-from executorlib.standalone.hdf import dump, get_output
 from executorlib.standalone.serialize import serialize_funct_h5
+from executorlib.task_scheduler.file.hdf import dump, get_output
 
 
 class FutureItem:

executorlib/task_scheduler/interactive/shared.py

@@ -140,7 +140,7 @@ def _execute_task_with_cache(
         future_queue (Queue): Queue for receiving new tasks.
         cache_directory (str): The directory to store cache files.
     """
-    from executorlib.standalone.hdf import dump, get_output
+    from executorlib.task_scheduler.file.hdf import dump, get_output
 
     task_key, data_dict = serialize_funct_h5(
         fn=task_dict["fn"],

tests/test_cache_backend_execute.py

@@ -7,7 +7,7 @@
 try:
     from executorlib.task_scheduler.file.backend import backend_execute_task_in_file
     from executorlib.task_scheduler.file.shared import _check_task_output, FutureItem
-    from executorlib.standalone.hdf import dump, get_runtime
+    from executorlib.task_scheduler.file.hdf import dump, get_runtime
     from executorlib.standalone.serialize import serialize_funct_h5
 
     skip_h5io_test = False

tests/test_singlenodeexecutor_cache.py

@@ -2,11 +2,11 @@
 import shutil
 import unittest
 
-from executorlib import SingleNodeExecutor
+from executorlib import SingleNodeExecutor, get_cache_data
 from executorlib.standalone.serialize import cloudpickle_register
 
 try:
-    from executorlib import get_cache_data
+    import h5py
 
     skip_h5py_test = False
 except ImportError:

tests/test_standalone_hdf.py

@@ -4,7 +4,7 @@
 
 
 try:
-    from executorlib.standalone.hdf import (
+    from executorlib.task_scheduler.file.hdf import (
         dump,
         load,
         get_output,