Simplified interface to have only two parameters

bob/pipelines/distributed/__init__.py

@@ -67,31 +67,27 @@ def dask_get_partition_size(cluster, n_objects, lower_bound=200):
         else n_objects
     )
 
-def get_local_parallel_client(n_workers=None, processes=True, threads_per_worker=1):
+def get_local_parallel_client(parallel=None, processes=True):
     """Returns a local Dask client with the given parameters, see the dask documentation for details: https://docs.dask.org/en/latest/how-to/deploy-dask/single-distributed.html?highlight=localcluster#localcluster
 
     Parameters
     ----------
-        n_workers: int or None
-            The number of workers (processes) to use; if `None`, take as many processors as we have on the system
+        parallel: int or None
+            The number of workers (processes or threads) to use; if `None`, take as many processors as we have on the system
 
         processes: boolean
-            Shall the dask client start processes (True, recommended) or threads (False). Note that threads in pyton do not run in parallel
-
-        threads_per_worker: int
-            How many threads (not processes) per worker to you allow?
-
+            Shall the dask client start processes (True, recommended) or threads (False). Note that threads in pure pyton do not run in parallel, see: https://www.quantstart.com/articles/Parallelising-Python-with-Threading-and-Multiprocessing/
     """
 
     from multiprocessing import cpu_count
 
     from dask.distributed import Client, LocalCluster
 
-    n_workers = n_workers or cpu_count()
+    parallel = parallel or cpu_count()
 
     cluster = LocalCluster(
         processes=processes,
-        n_workers=n_workers,
-        threads_per_worker=threads_per_worker,
+        n_workers=parallel if processes else 1,
+        threads_per_worker= 1 if processes else parallel,
     )
     return Client(cluster)