make all ClusterManager methods async

allows easier, more consistent override

- defer initialization to `await manager` instead of immediately (avoids invoking asyncio at import time)
- instantiate default manager as part of extension loading, not at import time

dask_labextension/__init__.py

@@ -2,12 +2,13 @@
 
 from jupyter_server.utils import url_path_join
 
-from . import config
+from . import config  # noqa
 from .clusterhandler import DaskClusterHandler
 from .dashboardhandler import DaskDashboardCheckHandler, DaskDashboardHandler
+from .manager import DaskClusterManager
 
 
-from ._version import __version__
+from ._version import __version__  # noqa
 
 
 def _jupyter_labextension_paths():
@@ -33,6 +34,7 @@ def load_jupyter_server_extension(nb_server_app):
     cluster_id_regex = r"(?P<cluster_id>[^/]+)"
     web_app = nb_server_app.web_app
     base_url = web_app.settings["base_url"]
+    web_app.settings["dask_cluster_manager"] = DaskClusterManager()
     get_cluster_path = url_path_join(base_url, "dask/clusters/" + cluster_id_regex)
     list_clusters_path = url_path_join(base_url, "dask/clusters/" + "?")
     get_dashboard_path = url_path_join(

dask_labextension/clusterhandler.py

@@ -4,25 +4,33 @@
 # Distributed under the terms of the Modified BSD License.
 
 import json
+from inspect import isawaitable
 
 from tornado import web
 from jupyter_server.base.handlers import APIHandler
 
-from .manager import manager
+from .manager import DaskClusterManager
 
 
 class DaskClusterHandler(APIHandler):
     """
     A tornado HTTP handler for managing dask clusters.
     """
+    manager: DaskClusterManager
+
+    async def prepare(self):
+        r = super().prepare()
+        if isawaitable(r):
+            await r
+        self.manager = await self.settings["dask_labextension_manager"]
 
     @web.authenticated
     async def delete(self, cluster_id: str) -> None:
         """
         Delete a cluster by id.
         """
         try:  # to delete the cluster.
-            val = await manager.close_cluster(cluster_id)
+            val = await self.manager.close_cluster(cluster_id)
             if val is None:
                 raise web.HTTPError(404, f"Dask cluster {cluster_id} not found")
 
@@ -37,12 +45,13 @@ async def get(self, cluster_id: str = "") -> None:
         """
         Get a cluster by id. If no id is given, lists known clusters.
         """
+        manager = self.manager
         if cluster_id == "":
-            cluster_list = manager.list_clusters()
+            cluster_list = await manager.list_clusters()
             self.set_status(200)
             self.finish(json.dumps(cluster_list))
         else:
-            cluster_model = manager.get_cluster(cluster_id)
+            cluster_model = await manager.get_cluster(cluster_id)
             if cluster_model is None:
                 raise web.HTTPError(404, f"Dask cluster {cluster_id} not found")
 
@@ -55,13 +64,13 @@ async def put(self, cluster_id: str = "") -> None:
         Create a new cluster with a given id. If no id is given, a random
         one is selected.
         """
-        if manager.get_cluster(cluster_id):
+        if await self.manager.get_cluster(cluster_id):
             raise web.HTTPError(
                 403, f"A Dask cluster with ID {cluster_id} already exists!"
             )
 
         try:
-            cluster_model = await manager.start_cluster(cluster_id)
+            cluster_model = await self.manager.start_cluster(cluster_id)
             self.set_status(200)
             self.finish(json.dumps(cluster_model))
         except Exception as e:
@@ -76,13 +85,13 @@ async def patch(self, cluster_id):
         new_model = json.loads(self.request.body)
         try:
             if new_model.get("adapt") is not None:
-                cluster_model = manager.adapt_cluster(
+                cluster_model = await self.manager.adapt_cluster(
                     cluster_id,
                     new_model["adapt"]["minimum"],
                     new_model["adapt"]["maximum"],
                 )
             else:
-                cluster_model = await manager.scale_cluster(
+                cluster_model = await self.manager.scale_cluster(
                     cluster_id, new_model["workers"]
                 )
             self.set_status(200)

dask_labextension/dashboardhandler.py

@@ -4,22 +4,32 @@
 server, preventing CORS issues.
 """
 import json
+from inspect import isawaitable
 from urllib import parse
 
 from tornado import httpclient, web
 
+
 from jupyter_server.base.handlers import APIHandler
 from jupyter_server.utils import url_path_join
 from jupyter_server_proxy.handlers import ProxyHandler
 
-from .manager import manager
+from .manager import DaskClusterManager
 
 
 class DaskDashboardCheckHandler(APIHandler):
     """
     A handler for checking validity of a dask dashboard.
     """
 
+    manager: DaskClusterManager
+
+    async def prepare(self):
+        r = super().prepare()
+        if isawaitable(r):
+            await r
+        self.manager = await self.settings["dask_labextension_manager"]
+
     @web.authenticated
     async def get(self, url) -> None:
         """
@@ -133,7 +143,7 @@ async def http_get(self, cluster_id, proxied_path):
         return await self.proxy(cluster_id, proxied_path)
 
     async def open(self, cluster_id, proxied_path):
-        host, port = self._get_parsed(cluster_id)
+        host, port = await self._get_parsed(cluster_id)
         return await super().proxy_open(host, port, proxied_path)
 
     # We have to duplicate all these for now, I've no idea why!
@@ -157,17 +167,17 @@ def patch(self, cluster_id, proxied_path):
     def options(self, cluster_id, proxied_path):
         return self.proxy(cluster_id, proxied_path)
 
-    def proxy(self, cluster_id, proxied_path):
-        host, port = self._get_parsed(cluster_id)
+    async def proxy(self, cluster_id, proxied_path):
+        host, port = await self._get_parsed(cluster_id)
         return super().proxy(host, port, proxied_path)
 
-    def _get_parsed(self, cluster_id):
+    async def _get_parsed(self, cluster_id):
         """
         Given a cluster ID, get the hostname and port of its bokeh server.
         """
         # Get the cluster by ID. If it is not found,
         # raise an error.
-        cluster_model = manager.get_cluster(cluster_id)
+        cluster_model = await self.manager.get_cluster(cluster_id)
         if not cluster_model:
             raise web.HTTPError(404, f"Dask cluster {cluster_id} not found")
 

dask_labextension/manager.py

@@ -3,6 +3,7 @@
 # Copyright (c) Jupyter Development Team.
 # Distributed under the terms of the Modified BSD License.
 
+import asyncio
 import importlib
 from inspect import isawaitable
 from typing import Any, Dict, List, Union
@@ -11,8 +12,6 @@
 import dask
 from dask.utils import format_bytes
 from dask.distributed import Adaptive
-from tornado.ioloop import IOLoop
-from tornado.concurrent import Future
 
 # A type for a dask cluster model: a serializable
 # representation of information about the cluster.
@@ -60,15 +59,28 @@ def __init__(self) -> None:
         self._adaptives: Dict[str, Adaptive] = dict()
         self._cluster_names: Dict[str, str] = dict()
         self._n_clusters = 0
+        self._initialized = None
 
-        self.initialized = Future()
+    async def _async_init(self):
+        """The async part of init
 
-        async def start_clusters():
-            for model in dask.config.get("labextension.initial"):
-                await self.start_cluster(configuration=model)
-            self.initialized.set_result(self)
+        Invoked by `await manager`
+        """
+        for model in dask.config.get("labextension.initial"):
+            await self.start_cluster(configuration=model)
+        return self
 
-        IOLoop.current().add_callback(start_clusters)
+    @property
+    def initialized(self):
+        """Don't create initialization task until it's been requested
+
+        typically via `await manager`
+
+        Makes it easier to ensure we don't do anything before we are in the event loop.
+        """
+        if self._initialized is None:
+            self._initialized = asyncio.create_task(self._async_init())
+        return self._initialized
 
     async def start_cluster(
         self, cluster_id: str = "", configuration: dict = {}
@@ -121,7 +133,9 @@ async def close_cluster(self, cluster_id: str) -> Union[ClusterModel, None]:
         """
         cluster = self._clusters.get(cluster_id)
         if cluster:
-            await cluster.close()
+            r = cluster.close()
+            if isawaitable(r):
+                await r
             self._clusters.pop(cluster_id)
             name = self._cluster_names.pop(cluster_id)
             adaptive = self._adaptives.pop(cluster_id, None)
@@ -130,7 +144,7 @@ async def close_cluster(self, cluster_id: str) -> Union[ClusterModel, None]:
         else:
             return None
 
-    def get_cluster(self, cluster_id) -> Union[ClusterModel, None]:
+    async def get_cluster(self, cluster_id) -> Union[ClusterModel, None]:
         """
         Get a Dask cluster model.
 
@@ -151,7 +165,7 @@ def get_cluster(self, cluster_id) -> Union[ClusterModel, None]:
 
         return make_cluster_model(cluster_id, name, cluster, adaptive)
 
-    def list_clusters(self) -> List[ClusterModel]:
+    async def list_clusters(self) -> List[ClusterModel]:
         """
         List the Dask cluster models known to the manager.
 
@@ -188,7 +202,7 @@ async def scale_cluster(self, cluster_id: str, n: int) -> Union[ClusterModel, No
             await t
         return make_cluster_model(cluster_id, name, cluster, adaptive=None)
 
-    def adapt_cluster(
+    async def adapt_cluster(
         self, cluster_id: str, minimum: int, maximum: int
     ) -> Union[ClusterModel, None]:
         cluster = self._clusters.get(cluster_id)
@@ -290,8 +304,3 @@ def make_cluster_model(
         model["adapt"] = {"minimum": adaptive.minimum, "maximum": adaptive.maximum}
 
     return model
-
-
-# Create a default cluster manager
-# to keep track of clusters.
-manager = DaskClusterManager()

dask_labextension/tests/test_manager.py

@@ -31,7 +31,7 @@ async def test_start():
             assert not model.get("adapt")
 
             # close cluster
-            assert len(manager.list_clusters()) == 1
+            assert len(await manager.list_clusters()) == 1
             await manager.close_cluster(model["id"])
 
             # add cluster with adaptive configuration
@@ -55,7 +55,7 @@ async def test_close():
 
             # close the cluster
             await manager.close_cluster(model["id"])
-            assert not manager.list_clusters()
+            assert not await manager.list_clusters()
 
 
 @gen_test()
@@ -66,10 +66,10 @@ async def test_get():
             model = await manager.start_cluster()
 
             # return None if a nonexistent cluster is requested
-            assert not manager.get_cluster("fake")
+            assert not await manager.get_cluster("fake")
 
             # get the cluster by id
-            assert model == manager.get_cluster(model["id"])
+            assert model == await manager.get_cluster(model["id"])
 
 
 @pytest.mark.filterwarnings("ignore")
@@ -78,12 +78,12 @@ async def test_list():
     with dask.config.set(config):
         async with DaskClusterManager() as manager:
             # start with an empty list
-            assert not manager.list_clusters()
+            assert not await manager.list_clusters()
             # start clusters
             model1 = await manager.start_cluster()
             model2 = await manager.start_cluster()
 
-            models = manager.list_clusters()
+            models = await manager.list_clusters()
             assert len(models) == 2
             assert model1 in models
             assert model2 in models
@@ -98,7 +98,7 @@ async def test_scale():
             start = time()
             while model["workers"] != 3:
                 await sleep(0.01)
-                model = manager.get_cluster(model["id"])
+                model = await manager.get_cluster(model["id"])
                 assert time() < start + 10, model["workers"]
 
             await sleep(0.2)  # let workers settle # TODO: remove need for this
@@ -108,7 +108,7 @@ async def test_scale():
             start = time()
             while model["workers"] != 6:
                 await sleep(0.01)
-                model = manager.get_cluster(model["id"])
+                model = await manager.get_cluster(model["id"])
                 assert time() < start + 10, model["workers"]
 
 
@@ -119,7 +119,7 @@ async def test_adapt():
             # add a new cluster
             model = await manager.start_cluster()
             assert not model.get("adapt")
-            model = manager.adapt_cluster(model["id"], 0, 4)
+            model = await manager.adapt_cluster(model["id"], 0, 4)
             adapt = model.get("adapt")
             assert adapt
             assert adapt["minimum"] == 0
@@ -144,21 +144,21 @@ async def test_initial():
     ):
         # Test asynchronous starting of clusters via a context
         async with DaskClusterManager() as manager:
-            clusters = manager.list_clusters()
+            clusters = await manager.list_clusters()
             assert len(clusters) == 1
             assert clusters[0]["name"] == "foo"
 
         # Test asynchronous starting of clusters outside of a context
         manager = DaskClusterManager()
-        assert len(manager.list_clusters()) == 0
+        assert len(await manager.list_clusters()) == 0
         await manager
-        clusters = manager.list_clusters()
+        clusters = await manager.list_clusters()
         assert len(clusters) == 1
         assert clusters[0]["name"] == "foo"
         await manager.close()
 
         manager = await DaskClusterManager()
-        clusters = manager.list_clusters()
+        clusters = await manager.list_clusters()
         assert len(clusters) == 1
         assert clusters[0]["name"] == "foo"
         await manager.close()