diff --git a/software/analytics/ibm-wml-ce.rst b/software/analytics/distributed-dl-env.rst
similarity index 94%
rename from software/analytics/ibm-wml-ce.rst
rename to software/analytics/distributed-dl-env.rst
index 14f6743d..81aad722 100644
--- a/software/analytics/ibm-wml-ce.rst
+++ b/software/analytics/distributed-dl-env.rst
@@ -1,11 +1,20 @@
 
 *************************************************************************************
-IBM Watson Machine Learning CE -> Open CE 
+Distributed Deep Learning Environment 
 *************************************************************************************
 
 Getting Started
 ===============
 
+The deep learning frameworks, TensorFlow and PyTorch, are available
+on Summit in a conda environment via ``ibm-wml-ce`` or latest ``open-ce``
+module. In addition to framework's built-in data-parallel library, 
+such as `tf.distribute.Strategy` or `torch.nn.parallel`, Horovod 
+library is also available for distributed training. For performance 
+profiling, Nvidia deep learning profiler is provided via ``dlprof`` 
+module. For parallel hyperparameter search, ``ray-tune`` is also available 
+on Summit.       
+
 IBM Watson Machine Learning Community Edition is provided on Summit
 through the module ``ibm-wml-ce``, and after version ``1.7.0``, the 
 module has been renamed to ``open-ce``, which is built based on the 
@@ -71,6 +80,7 @@ Comparing to IBM WML CE, `Open-CE <https://github.com/open-ce/open-ce>`_ no long
     | WML-CE on Summit (`slides <https://www.olcf.ornl.gov/wp-content/uploads/2019/10/DDLonSummit.pdf>`__ | `recording <https://vimeo.com/377551223>`__) 
     | Scaling up deep learning application on Summit (`slides <https://www.olcf.ornl.gov/wp-content/uploads/2019/12/Scaling-DL-on-Summit.pdf>`__ | `recording <https://vimeo.com/391520479>`__) 
     | ML/DL on Summit (`slides <https://www.olcf.ornl.gov/wp-content/uploads/2020/02/MLDL-on-Summit-June2020.pdf>`__ | `recording <https://vimeo.com/427791205>`__)
+    | OpenCE on Summit (`slides <https://www.olcf.ornl.gov/wp-content/uploads/2020/09/open-ce.pdf>`__)
 
 Running Distributed Deep Learning Jobs
 ======================================
@@ -174,7 +184,8 @@ Performance Profiling
 
 There are several tools that can be used to profile the performance of a
 deep learning job. Below are links to several tools that are available
-as part of the ibm-wml-ce and open-ce modules.
+as part of the ibm-wml-ce and open-ce modules, or Nvidia deep learning
+profiler.
 
 NVIDIA Profiling Tools
 ^^^^^^^^^^^^^^^^^^^^^^
@@ -185,6 +196,16 @@ different CUDA kernels are being launched and how long they take to complete.
 For more information on using the NVIDA profiling tools on Summit, please see
 these `slides <https://www.olcf.ornl.gov/wp-content/uploads/2019/08/NVIDIA-Profilers.pdf>`_.
 
+The `Nvidia deep learning profiler <https://docs.nvidia.com/deeplearning/frameworks/dlprof-user-guide>`_ is also available on Summit via
+
+.. code-block:: console
+
+    $ module use /sw/aaims/summit/modulefiles
+    $ module load dlprof 
+
+Note that only PyTorch in ``open-ce`` module is currently supported, and usage 
+`examples <https://github.com/at-aaims/dlprof-examples>`_ are provided.    
+
 Horovod Timeline
 ^^^^^^^^^^^^^^^^
 
@@ -250,6 +271,11 @@ We can break the reservation string down to understand each piece.
 
     * The ``maxcus=1`` specifies that the nodes can come from at most 1 rack.
 
+
+Hyperparameter Search 
+==========================================
+(coming soon!)
+
 Example
 ===================
 
diff --git a/software/analytics/index.rst b/software/analytics/index.rst
index fac87d2f..e5e4bed2 100644
--- a/software/analytics/index.rst
+++ b/software/analytics/index.rst
@@ -9,7 +9,7 @@ and data analytics tasks on OLCF systems.
 .. toctree::
    :maxdepth: 2
 
-   ibm-wml-ce
+   distributed-dl-env
    pbdR
    nvidia-rapids
    blazingsql