Add in the dragon shared memory hack. Make it so we don't deadlock

src/spyral/core/dragon_start.py

@@ -25,7 +25,12 @@ def _run_shared_memory_manager(
         and indicate that all shared memory should be cleaned up.
     """
     handles = {}
-    pipeline.create_shared_data(handles)
+    try:
+        pipeline.create_shared_data(handles)
+    except Exception:
+        # Hack to make sure we don't hang when creation fails
+        ready.set()
+        return
     ready.set()
     shutdown.wait()
     for handle in handles.values():
@@ -50,6 +55,9 @@ def start_pipeline_dragon(
     worker_cpus = total_cpus - n_nodes - 1
 
     print(SPLASH)
+    print(f"Total cpus available: {total_cpus}")
+    print(f"Number of nodes: {n_nodes}")
+    print(f"Calculated worker cpus: {worker_cpus}")
     print(f"Creating workspace: {pipeline.workspace} ...", end=" ")
     pipeline.create_workspace()
     print("Done.")

src/spyral/phases/interp_solver_phase.py

@@ -173,9 +173,21 @@ def create_shared_data(
         # Create a block of shared memory of the same total size as the mesh
         # Note that we don't have a lock on the shared memory as the mesh is
         # used read-only
-        handle = SharedMemory(
-            name=self.shared_mesh_name, create=True, size=mesh_data.nbytes
-        )
+        # Dragon hack: Dragon does not currently implement SharedMemory, so for now
+        # we fall back to the base multiprocessing impl when we receive a
+        # NotImplementedError
+        handle = None
+        try:
+            handle = SharedMemory(
+                name=self.shared_mesh_name, create=True, size=mesh_data.nbytes
+            )
+        except NotImplementedError:
+            # Hacky
+            MultiprocessingSharedMemory = SharedMemory.__bases__[0]
+            handle = MultiprocessingSharedMemory(
+                name=self.shared_mesh_name, create=True, size=mesh_data.nbytes
+            )
+
         handles[handle.name] = handle
         spyral_info(
             __name__,