Updated release notes (#1874)

* Updated release notes to reflect recent changes in the v0.102.0-pending release

* Updated with feedback from team

* Fixed typo

* Clarified the required versons of Hydrogen and Aluminum

Author: bvanessen

ReleaseNotes.txt

@@ -9,21 +9,42 @@ Support for new training algorithms:
  - Improved documentation of training algorithm infrastructure.
 
 Support for new network structures:
+ - ATOM WAE model - character-based Wasserstein Autoencoder
+ - Community GAN model for graph data sets
 
 Support for new layers:
  - "DFTAbs" layer that computes the absolute value of the channel-wise
    DFT of the input data
+ - Adding support for 3D Matrix Multiplication
+ - Added scatter and gather neural network layers
+ - CPU-based GRU layers using oneDNN
+ - Added batch-wise reduce-sum
+ - ArcFace loss
 
 Python front-end:
+ - Added 3D U-Net Model
+ - Added Cosmoflow Model
+ - Ported CANDLE Pilot1 models
+ - Support nvprof
+ - Added channelwise fully connected layer
+ - Added support for non square kernels, padding, stride, and
+   dilation for the convolution module
+ - Support for OpenMPI launcher
 
 Performance optimizations:
  - Use cuDNN 8 RNN API and CUDA Graphs in GRU layer
  - Cache CUDA Graphs for each active mini-batch size
  - Tuned performance of slice, concatenate, and tessellate layers on
-   ARM processors.
+   ARM processors
+ - Parallelize computation of Gaussian random numbers
+ - Optimizing tessellate, concatenate, and slice layers on CPU
 
 Model portability & usability:
 
+Experiments & Applications:
+ - Added experiment scripts for ATOM cWAE Gordon Bell simulations
+ - LBANN-ATOM model inference and analysis
+
 Internal features:
  - Wrapper classes for CUDA Graphs API
  - Elementary examples of using complex numbers
@@ -37,8 +58,11 @@ Internal features:
    hook) in the name rather than the current execution context.
  - Added in-memory binary model exchange for LTFB.
  - Added support for ROCm and MIOpen
+ - Added support for oneDNN
  - Updated the bamboo test environment to use local executable rather
    than hard coded executables
+ - Overhauled and refactored serialization throughout code to use
+   Cereal serialization library
  - Significant cleanup and refactoring of code base to improve compile
    times.  Moving to ensure  that code adheres to standard split of
    header between declaration and implementation functions (for
@@ -47,17 +71,25 @@ Internal features:
    inclusions.
  - The relationship of execution_contexts and training_algorithms was
    clarified. There is still work to do here.
+ - Added DistConv tests both convolution and pooling layers
+ - Support padding in distributed embedding layer
+ - Added dump model graph callback
+ - Added perturb learning rate callback
+ - Added batched inference algorithm
+ - Switched ATOM tests to use CPU embedding and tessellate layers to
+   minimize noise
 
 I/O & data readers:
  - Experimental data reader that generates graph random walks with
    HavoqGT
  - Added explict tournament execution mode
  - Added support to split training data reader into validation and
    tournament readers
+ - node2vec data reader
 
 Build system:
- - Hydrogen v1.5.0
- - Aluminum v0.5.0
+ - Hydrogen v1.5.0+
+ - Aluminum v0.5.0+
  - DiHydrogen v0.2.0 is required
  - C++14 or newer standard with CUDA (CMake: "-DCMAKE_CUDA_STANDARD=14")
  - OpenCV is now an optional dependency via CMake "LBANN_WITH_VISION"
@@ -71,6 +103,8 @@ Build system:
    build_lbann.sh script to setup good defaults on known systems
  - Added application specific build scripts for users such as ATOM
  - Added support for pulling from Spack mirrors and setting them up
+ - Split embedded Python support from Python Front End
+ - Switched Spack-based build script to use Spack's clingo concretizer
 
 Bug fixes:
  - Fixed a bug where LBANN didn't set the Hydrogen RNG seed
@@ -80,6 +114,7 @@ Bug fixes:
    types
  - Fixed calculation of the linearized response size
  - Fixed the data coordinator's interface to input_layer
+ - Fixed error with deterministic execution of dropout layers
 
 Retired features:
  - Removed deprecated JAG leader mode which was made obsolete when the