document class, not init

docs/conf.py

@@ -55,7 +55,6 @@ extensions = [
 
 # see http://stackoverflow.com/q/12206334/562769
 numpydoc_show_class_members = False
-napoleon_include_init_with_doc = True
 
 mathjax_path = ('https://cdn.mathjax.org/mathjax/latest/MathJax.js?'
                 'config=TeX-AMS-MML_HTMLorMML')

docs/index.rst

@@ -1,11 +1,9 @@
 ``learning@home::tesseract``
 ====================================
 
-Tesseract lets you train huge neural networks on computers provided by volunteers.
+Tesseract lets you train huge neural networks on computers provided by volunteers. Powered by pytorch
 
-Written in pytorch for your creative freedom.
-
-.. image:: https://i.imgur.com/GPxolxb.gif
+.. image:: _static/bug.gif
 
 User guide:
 

tesseract/client/gating_function.py

@@ -12,23 +12,23 @@ from ..utils import nested_map, check_numpy, run_and_await_k
 
 
 class GatingFunction(nn.Module):
+    """
+    A torch module that selects experts across the network and averages their predictions
+
+    :param in_features: common input size for experts and gating function
+    :param grid_size: tesseract dimensions that form expert uid (see below)
+    :param uid_prefix: common prefix for all expert uids
+    expert uid follows the pattern {uid_prefix}{0...grid_size[0]}.{0...grid_size[1]}...{0...grid_size[-1]}
+    :param network: TesseractNetwork where the experts reside
+    :param num_workers: number of threads for parallel network operation
+    :param k_best: queries this many experts with highest scores
+    :param k_min: makes sure at least this many experts returned output
+    :param timeout_after_k_min: waits for this many seconds after k_min experts returned results.
+    Any expert that didn't manage to return output after that delay is considered unavailable
+    :param expert_padding: internal value used to denote "absent expert". Should not coincide with any expert uid.
+    """
     def __init__(self, *, in_features, grid_size: Tuple[int], network, num_workers=None,
                  k_best, k_min=1, timeout_after_k_min=1.0, uid_prefix='', expert_padding=None):
-        """
-        A torch module that selects experts across the network and averages their predictions
-
-        :param in_features: common input size for experts and gating function
-        :param grid_size: tesseract dimensions that form expert uid (see below)
-        :param uid_prefix: common prefix for all expert uids
-        expert uid follows the pattern {uid_prefix}{0...grid_size[0]}.{0...grid_size[1]}...{0...grid_size[-1]}
-        :param network: TesseractNetwork where the experts reside
-        :param num_workers: number of threads for parallel network operation
-        :param k_best: queries this many experts with highest scores
-        :param k_min: makes sure at least this many experts returned output
-        :param timeout_after_k_min: waits for this many seconds after k_min experts returned results.
-        Any expert that didn't manage to return output after that delay is considered unavailable
-        :param expert_padding: internal value used to denote "absent expert". Should not coincide with any expert uid.
-        """
         super().__init__()
         self.network, self.grid_size = network, grid_size
         self.uid_prefix, self.expert_padding = uid_prefix, expert_padding