Remove warnings from autodoc and sphinx

deepspeed/runtime/fp16/onebit/zoadam.py

@@ -12,9 +12,11 @@
 
 
 class ZeroOneAdam(torch.optim.Optimizer):
-    """Implements the 0/1 Adam algorithm. Currently GPU-only.
+    """
+    Implements the 0/1 Adam algorithm. Currently GPU-only.
     For usage example please see https://www.deepspeed.ai/tutorials/zero-one-adam/
     For technical details please read https://arxiv.org/abs/2202.06009
+
     Arguments:
         params (iterable): iterable of parameters to optimize or dicts defining
             parameter groups.

deepspeed/runtime/lr_schedules.py

@@ -274,7 +274,7 @@ class LRRangeTest(object):
     """Sets the learning rate of each parameter group according to
     learning rate range test (LRRT) policy. The policy increases learning
     rate starting from a base value with a constant frequency, as detailed in
-    the paper `A disciplined approach to neural network hyper-parameters: Part1`_.
+    the paper `A disciplined approach to neural network hyper-parameters: Part 1 <https://arxiv.org/abs/1803.09820>`_
 
     LRRT policy is used for finding maximum LR that trains a model without divergence, and can be used to
     configure the LR boundaries for Cyclic LR schedules.
@@ -379,7 +379,7 @@ class OneCycle(object):
     1CLR policy changes the learning rate after every batch.
     `step` should be called after a batch has been used for training.
 
-    This implementation was adapted from the github repo: `pytorch/pytorch`_
+    This implementation was adapted from the github repo: `PyTorch <https://github.com/pytorch/pytorch>`_.
 
     Args:
         optimizer (Optimizer): Wrapped optimizer.

docs/code-docs/source/monitor.rst

@@ -9,15 +9,15 @@ overview of what DeepSpeed will log automatically.
     :header: "Field", "Description", "Condition"
     :widths: 20, 20, 10
 
-    `Train/Samples/train_loss`,The training loss.,None
-    `Train/Samples/lr`,The learning rate during training.,None
-    `Train/Samples/loss_scale`,The loss scale when training using `fp16`.,`fp16` must be enabled.
-    `Train/Eigenvalues/ModelBlockParam_{i}`,Eigen values per param block.,`eigenvalue` must be enabled.
-    `Train/Samples/elapsed_time_ms_forward`,The global duration of the forward pass.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_backward`,The global duration of the forward pass.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_backward_inner`,The backward time that does not include the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_backward_allreduce`,The global duration of the allreduce operation.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_step`,The optimizer step time,`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/train_loss`,"The training loss.",None
+    `Train/Samples/lr`,"The learning rate during training.",None
+    `Train/Samples/loss_scale`,"The loss scale when training using `fp16`.",`fp16` must be enabled.
+    `Train/Eigenvalues/ModelBlockParam_{i}`,"Eigen values per param block.",`eigenvalue` must be enabled.
+    `Train/Samples/elapsed_time_ms_forward`,"The global duration of the forward pass.",`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_backward`,"The global duration of the forward pass.",`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_backward_inner`,"The backward time that does not include the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time.",`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_backward_allreduce`,"The global duration of the allreduce operation.",`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_step`,"The optimizer step time.",`flops_profiler.enabled` or `wall_clock_breakdown`.
 
 TensorBoard
 -----------