Replace settings variable with function

openff/bespokefit/cli/cache.py

@@ -24,7 +24,7 @@
     exit_with_messages,
     print_header,
 )
-from openff.bespokefit.executor.services import settings
+from openff.bespokefit.executor.services import current_settings
 from openff.bespokefit.executor.services.qcgenerator.cache import _canonicalize_task
 from openff.bespokefit.executor.utilities.redis import is_redis_available, launch_redis
 from openff.bespokefit.schema.data import LocalQCData
@@ -160,6 +160,8 @@ def _update(
 
     console.print(Padding("2. connecting to redis cache", (1, 0, 1, 0)))
 
+    settings = current_settings()
+
     if launch_redis_if_unavailable and not is_redis_available(
         host=settings.BEFLOW_REDIS_ADDRESS, port=settings.BEFLOW_REDIS_PORT
     ):

openff/bespokefit/cli/executor/list.py

@@ -41,12 +41,14 @@ def list_cli():
     console = rich.get_console()
     print_header(console)
 
-    from openff.bespokefit.executor.services import settings
+    from openff.bespokefit.executor.services import current_settings
     from openff.bespokefit.executor.services.coordinator.models import (
         CoordinatorGETPageResponse,
     )
     from openff.bespokefit.executor.utilities import handle_common_errors
 
+    settings = current_settings()
+
     base_href = (
         f"http://127.0.0.1:"
         f"{settings.BEFLOW_GATEWAY_PORT}"

openff/bespokefit/executor/executor.py

@@ -19,7 +19,7 @@
 from rich.padding import Padding
 from typing_extensions import Literal
 
-from openff.bespokefit.executor.services import Settings, settings
+from openff.bespokefit.executor.services import Settings, current_settings
 from openff.bespokefit.executor.services.coordinator.models import (
     CoordinatorGETResponse,
     CoordinatorGETStageStatus,
@@ -41,12 +41,14 @@
 
 
 def _base_endpoint():
+    settings = current_settings()
     return (
         f"http://127.0.0.1:{settings.BEFLOW_GATEWAY_PORT}{settings.BEFLOW_API_V1_STR}/"
     )
 
 
 def _coordinator_endpoint():
+    settings = current_settings()
     return f"{_base_endpoint()}{settings.BEFLOW_COORDINATOR_PREFIX}"
 
 
@@ -251,6 +253,8 @@ def _cleanup_processes(self):
     def _launch_redis(self):
         """Launches a redis server if an existing one cannot be found."""
 
+        settings = current_settings()
+
         if self._launch_redis_if_unavailable and not is_redis_available(
             host=settings.BEFLOW_REDIS_ADDRESS, port=settings.BEFLOW_REDIS_PORT
         ):
@@ -275,22 +279,12 @@ def _launch_workers(self):
             BEFLOW_OPTIMIZER_WORKER_MAX_MEM=self._optimizer_worker_config.max_memory,
         ).apply_env():
 
-            for worker_settings, n_workers, config in (
-                (
-                    settings.fragmenter_settings,
-                    self._n_fragmenter_workers,
-                    self._fragmenter_worker_config,
-                ),
-                (
-                    settings.qc_compute_settings,
-                    self._n_qc_compute_workers,
-                    self._qc_compute_worker_config,
-                ),
-                (
-                    settings.optimizer_settings,
-                    self._n_optimizer_workers,
-                    self._optimizer_worker_config,
-                ),
+            settings = current_settings()
+
+            for worker_settings, n_workers in (
+                (settings.fragmenter_settings, self._n_fragmenter_workers),
+                (settings.qc_compute_settings, self._n_qc_compute_workers),
+                (settings.optimizer_settings, self._n_optimizer_workers),
             ):
 
                 if n_workers == 0:

openff/bespokefit/executor/services/__init__.py

@@ -1,5 +1,8 @@
 from openff.bespokefit.executor.services._settings import Settings
 
-settings = Settings()
 
-__all__ = ["settings", "Settings"]
+def current_settings() -> Settings:
+    return Settings()
+
+
+__all__ = ["current_settings", "Settings"]

openff/bespokefit/executor/services/coordinator/app.py

@@ -10,7 +10,7 @@
 from fastapi.responses import Response
 from openff.toolkit.topology import Molecule
 
-from openff.bespokefit.executor.services import settings
+from openff.bespokefit.executor.services import current_settings
 from openff.bespokefit.executor.services.coordinator import worker
 from openff.bespokefit.executor.services.coordinator.models import (
     CoordinatorGETPageResponse,
@@ -32,8 +32,10 @@
 _logger = logging.getLogger(__name__)
 _worker_task: Optional[asyncio.Future] = None
 
+__settings = current_settings()
+
 __GET_TASK_IMAGE_ENDPOINT = (
-    "/" + settings.BEFLOW_COORDINATOR_PREFIX + "/{optimization_id}/image"
+    "/" + __settings.BEFLOW_COORDINATOR_PREFIX + "/{optimization_id}/image"
 )
 
 
@@ -56,7 +58,7 @@ def _get_task(
     return CoordinatorTask.parse_obj(pickle.loads(task_pickle))
 
 
-@router.get("/" + settings.BEFLOW_COORDINATOR_PREFIX)
+@router.get("/" + __settings.BEFLOW_COORDINATOR_PREFIX)
 def get_optimizations(skip: int = 0, limit: int = 1000) -> CoordinatorGETPageResponse:
     """Retrieves all bespoke optimizations that have been submitted to this server."""
 
@@ -75,8 +77,8 @@ def get_optimizations(skip: int = 0, limit: int = 1000) -> CoordinatorGETPageRes
     contents = [
         Link(
             self=(
-                f"{settings.BEFLOW_API_V1_STR}/"
-                f"{settings.BEFLOW_COORDINATOR_PREFIX}/"
+                f"{__settings.BEFLOW_API_V1_STR}/"
+                f"{__settings.BEFLOW_COORDINATOR_PREFIX}/"
                 f"{optimization_id}"
             ),
             id=optimization_id,
@@ -89,26 +91,26 @@ def get_optimizations(skip: int = 0, limit: int = 1000) -> CoordinatorGETPageRes
 
     return CoordinatorGETPageResponse(
         self=(
-            f"{settings.BEFLOW_API_V1_STR}/"
-            f"{settings.BEFLOW_COORDINATOR_PREFIX}?skip={skip}&limit={limit}"
+            f"{__settings.BEFLOW_API_V1_STR}/"
+            f"{__settings.BEFLOW_COORDINATOR_PREFIX}?skip={skip}&limit={limit}"
         ),
         prev=None
         if prev_index >= skip
         else (
-            f"{settings.BEFLOW_API_V1_STR}/"
-            f"{settings.BEFLOW_COORDINATOR_PREFIX}?skip={prev_index}&limit={limit}"
+            f"{__settings.BEFLOW_API_V1_STR}/"
+            f"{__settings.BEFLOW_COORDINATOR_PREFIX}?skip={prev_index}&limit={limit}"
         ),
         next=None
         if (next_index <= skip or next_index == n_optimizations)
         else (
-            f"{settings.BEFLOW_API_V1_STR}/"
-            f"{settings.BEFLOW_COORDINATOR_PREFIX}?skip={next_index}&limit={limit}"
+            f"{__settings.BEFLOW_API_V1_STR}/"
+            f"{__settings.BEFLOW_COORDINATOR_PREFIX}?skip={next_index}&limit={limit}"
         ),
         contents=contents,
     )
 
 
-@router.get("/" + settings.BEFLOW_COORDINATOR_PREFIX + "/{optimization_id}")
+@router.get("/" + __settings.BEFLOW_COORDINATOR_PREFIX + "/{optimization_id}")
 def get_optimization(optimization_id: str) -> CoordinatorGETResponse:
     """Retrieves a bespoke optimization that has been submitted to this server
     using its unique id."""
@@ -118,15 +120,15 @@ def get_optimization(optimization_id: str) -> CoordinatorGETResponse:
     )
     response.links = {
         "image": (
-            settings.BEFLOW_API_V1_STR
+            __settings.BEFLOW_API_V1_STR
             + __GET_TASK_IMAGE_ENDPOINT.format(optimization_id=optimization_id)
         )
     }
 
     return response
 
 
-@router.post("/" + settings.BEFLOW_COORDINATOR_PREFIX)
+@router.post("/" + __settings.BEFLOW_COORDINATOR_PREFIX)
 def post_optimization(body: CoordinatorPOSTBody) -> CoordinatorPOSTResponse:
     """Submit a bespoke optimization to be performed by the server."""
 
@@ -159,8 +161,8 @@ def post_optimization(body: CoordinatorPOSTBody) -> CoordinatorPOSTResponse:
     return CoordinatorPOSTResponse(
         id=task_id,
         self=(
-            f"{settings.BEFLOW_API_V1_STR}/"
-            f"{settings.BEFLOW_COORDINATOR_PREFIX}/{task.id}"
+            f"{__settings.BEFLOW_API_V1_STR}/"
+            f"{__settings.BEFLOW_COORDINATOR_PREFIX}/{task.id}"
         ),
     )
 

openff/bespokefit/executor/services/coordinator/models.py

@@ -2,7 +2,7 @@
 
 from pydantic import Field
 
-from openff.bespokefit.executor.services import settings
+from openff.bespokefit.executor.services import current_settings
 from openff.bespokefit.executor.services.coordinator.stages import StageType
 from openff.bespokefit.executor.services.models import Link, PaginatedCollection
 from openff.bespokefit.executor.utilities.typing import Status
@@ -48,6 +48,8 @@ def from_stage(cls, stage: StageType):
         else:
             raise NotImplementedError()
 
+        settings = current_settings()
+
         base_endpoint = f"{settings.BEFLOW_API_V1_STR}/"
 
         endpoints = {
@@ -91,6 +93,8 @@ class CoordinatorGETResponse(Link):
     @classmethod
     def from_task(cls, task: "CoordinatorTask"):
 
+        settings = current_settings()
+
         stages = [
             *task.pending_stages,
             *([] if task.running_stage is None else [task.running_stage]),

openff/bespokefit/executor/services/coordinator/stages.py

@@ -20,7 +20,7 @@
 from qcengine.procedures.torsiondrive import TorsionDriveResult
 from typing_extensions import Literal
 
-from openff.bespokefit.executor.services import settings
+from openff.bespokefit.executor.services import current_settings
 from openff.bespokefit.executor.services.coordinator.utils import get_cached_parameters
 from openff.bespokefit.executor.services.fragmenter.models import (
     FragmenterGETResponse,
@@ -111,6 +111,8 @@ class FragmentationStage(_Stage):
 
     async def _enter(self, task: "CoordinatorTask"):
 
+        settings = current_settings()
+
         async with httpx.AsyncClient() as client:
 
             raw_response = await client.post(
@@ -143,6 +145,8 @@ async def _update(self):
         if self.status == "errored":
             return
 
+        settings = current_settings()
+
         async with httpx.AsyncClient() as client:
 
             raw_response = await client.get(
@@ -209,6 +213,9 @@ def _generate_torsion_parameters(
         Returns:
             The list of generated smirks patterns including any cached values, and a list of fragments which require torsiondrives.
         """
+
+        settings = current_settings()
+
         cached_torsions = None
 
         if is_redis_available(
@@ -341,6 +348,8 @@ async def _generate_parameters(
 
     async def _enter(self, task: "CoordinatorTask"):
 
+        settings = current_settings()
+
         fragment_stage = next(
             iter(
                 stage
@@ -423,6 +432,8 @@ async def _enter(self, task: "CoordinatorTask"):
 
     async def _update(self):
 
+        settings = current_settings()
+
         if self.status == "errored":
             return
 
@@ -537,6 +548,8 @@ async def _inject_bespoke_qc_data(
 
     async def _enter(self, task: "CoordinatorTask"):
 
+        settings = current_settings()
+
         completed_stages = {stage.type: stage for stage in task.completed_stages}
 
         input_schema = task.input_schema.copy(deep=True)
@@ -581,6 +594,8 @@ async def _enter(self, task: "CoordinatorTask"):
 
     async def _update(self):
 
+        settings = current_settings()
+
         if self.status == "errored":
             return
 

openff/bespokefit/executor/services/coordinator/worker.py

@@ -4,15 +4,17 @@
 
 import redis
 
-from openff.bespokefit.executor.services import settings
+from openff.bespokefit.executor.services import current_settings
 from openff.bespokefit.executor.services.coordinator.models import CoordinatorTask
 
 _logger = logging.getLogger(__name__)
 
+__settings = current_settings()
+
 redis_connection = redis.Redis(
-    host=settings.BEFLOW_REDIS_ADDRESS,
-    port=settings.BEFLOW_REDIS_PORT,
-    db=settings.BEFLOW_REDIS_DB,
+    host=__settings.BEFLOW_REDIS_ADDRESS,
+    port=__settings.BEFLOW_REDIS_PORT,
+    db=__settings.BEFLOW_REDIS_DB,
 )
 
 

openff/bespokefit/executor/services/fragmenter/app.py

@@ -5,7 +5,7 @@
 from openff.fragmenter.depiction import _oe_render_fragment
 from openff.fragmenter.fragment import FragmentationResult
 
-from openff.bespokefit.executor.services import settings
+from openff.bespokefit.executor.services import current_settings
 from openff.bespokefit.executor.services.fragmenter import worker
 from openff.bespokefit.executor.services.fragmenter.cache import (
     cached_fragmentation_task,
@@ -20,10 +20,12 @@
 
 router = APIRouter()
 
-__GET_ENDPOINT = "/" + settings.BEFLOW_FRAGMENTER_PREFIX + "/{fragmentation_id}"
+__settings = current_settings()
+
+__GET_ENDPOINT = "/" + __settings.BEFLOW_FRAGMENTER_PREFIX + "/{fragmentation_id}"
 __GET_FRAGMENT_IMAGE_ENDPOINT = (
     "/"
-    + settings.BEFLOW_FRAGMENTER_PREFIX
+    + __settings.BEFLOW_FRAGMENTER_PREFIX
     + "/{fragmentation_id}/fragment/{fragment_id}/image"
 )
 
@@ -36,14 +38,14 @@ def get_fragment(fragmentation_id: str) -> FragmenterGETResponse:
 
     return FragmenterGETResponse(
         id=fragmentation_id,
-        self=settings.BEFLOW_API_V1_STR
+        self=__settings.BEFLOW_API_V1_STR
         + __GET_ENDPOINT.format(fragmentation_id=fragmentation_id),
         status=task_info["status"],
         result=task_result,
         error=json.dumps(task_info["error"]),
         _links={
             f"fragment-{i}-image": (
-                settings.BEFLOW_API_V1_STR
+                __settings.BEFLOW_API_V1_STR
                 + __GET_FRAGMENT_IMAGE_ENDPOINT.format(
                     fragmentation_id=fragmentation_id, fragment_id=i
                 )
@@ -55,7 +57,7 @@ def get_fragment(fragmentation_id: str) -> FragmenterGETResponse:
     )
 
 
-@router.post("/" + settings.BEFLOW_FRAGMENTER_PREFIX)
+@router.post("/" + __settings.BEFLOW_FRAGMENTER_PREFIX)
 def post_fragment(body: FragmenterPOSTBody) -> FragmenterPOSTResponse:
     # We use celery delay method in order to enqueue the task with the given
     # parameters
@@ -65,7 +67,7 @@ def post_fragment(body: FragmenterPOSTBody) -> FragmenterPOSTResponse:
     )
     return FragmenterPOSTResponse(
         id=task_id,
-        self=settings.BEFLOW_API_V1_STR
+        self=__settings.BEFLOW_API_V1_STR
         + __GET_ENDPOINT.format(fragmentation_id=task_id),
     )