[FIX] improve doc-strings

Author: franchuterivera

autoPyTorch/utils/parallel.py

@@ -3,6 +3,21 @@
 
 
 def preload_modules(context: multiprocessing.context.BaseContext) -> None:
+    """
+    This function is meant to be used with the forkserver multiprocessing context.
+    More details about it can be found here:
+    https://docs.python.org/3/library/multiprocessing.html
+
+    Forkserver is known to be slower than other contexts. We use it, because it helps
+    reduce the probability of a deadlock. To make it fast, we pre-load modules so that
+    forked children have the desired modules available.
+
+    We do not inherit dead-lock problematic modules like logging.
+
+    Arguments:
+        context (multiprocessing.context.BaseContext): One of the three supported multiprocessing
+            contexts being fork, forkserver or spawn.
+    """
     all_loaded_modules = sys.modules.keys()
     preload = [
         loaded_module for loaded_module in all_loaded_modules

autoPyTorch/utils/single_thread_client.py

@@ -30,8 +30,15 @@ def __del__(self) -> None:
 
 class SingleThreadedClient(dask.distributed.Client):
     """
-    A class to Mock the Distributed Client class, in case
-    Auto-Sklearn is meant to run in the current Thread.
+    A class to Mock the Distributed Client class.
+
+    Using dask requires a scheduler which submits jobs on a different process. Also,
+    pynisher submits jobs in a further additional process.
+
+    When using a single core, we would prefer using the same main process without any
+    multiprocessing overhead (that is, without the need of a LocalCluster in
+    dask.distributed.Client). In other words, this class enriches the Client() class
+    with the capability to run a future in the same thread (without any deadlock).
     """
     def __init__(self) -> None:
 

test/test_pipeline/components/training/base.py

@@ -23,6 +23,8 @@ def prepare_trainer(self,
                         trainer: BaseTrainerComponent,
                         task_type: int,
                         epochs=50):
+        # make this test reproducible
+        torch.manual_seed(42)
         if task_type in CLASSIFICATION_TASKS:
             X, y = make_classification(
                 n_samples=n_samples,

test/test_utils/test_single_thread_client.py

@@ -23,5 +23,10 @@ def test_single_thread_client_like_dask_client():
     assert str(future) != ""
     assert str(single_thread_client) != ""
 
+    # Single thread client is an inherited version of dask client
+    # so that futures run in the same thread as the main job.
+    # We carefully selected what methods are inherited, and any other
+    # method should raise a not implemented error to be safe of major
+    # dask client api changes.
     with pytest.raises(NotImplementedError):
         single_thread_client.get_scheduler_logs()