archive 1.6.0 doc

Author: mchoi8739

doc/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.rst

@@ -19,8 +19,11 @@ Sagemaker Distributed Model Parallel 1.7.0 Release Notes
 
 **New Features**
 
+Additional tensor parallelism features for PyTorch:
+
 * Support for query key layer scaling to avoid overflow for large model
-* Support for FP32 residual addition to avoid overflow (NaN loss values) for large models when using FP16.
+* Support for FP32 residual addition to avoid overflow (NaN loss values)
+  for large models when using FP16
 
 **Improvements**
 

doc/api/training/smp_versions/archives.rst

@@ -3,6 +3,7 @@
 .. toctree::
     :maxdepth: 1
 
+    v1_6_0.rst
     v1_5_0.rst
     v1_4_0.rst
     v1_3_0.rst

doc/api/training/smp_versions/latest/smd_model_parallel_pytorch.rst

@@ -16,7 +16,7 @@ you need to add the following import statement at the top of your training scrip
    <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-customize-training-script-pt.html>`_
    to learn how to use the following API in your PyTorch training script.
 
-.. py:class:: smp.DistributedModel()
+.. class:: smp.DistributedModel
 
    A sub-class of ``torch.nn.Module`` which specifies the model to be
    partitioned. Accepts a ``torch.nn.Module`` object ``module`` which is