Final release rendering

algorithms.rst

@@ -1,8 +1,6 @@
 Algorithms and Application Libraries
 ====================================
 
-Following are the list of algorithms and applications implemented using Lava:
-
 .. toctree::
    :maxdepth: 3
 

index.rst

@@ -53,6 +53,8 @@ Future planned enhancements include support for emerging computational paradigms
 
 For maximum developer productivity, Lava blends a simple Python Interface with accelerated performance using underlying C/C++/CUDA/OpenCL code.
 
+For more information, visit Lava on Github: `https://github.com/lava-nc <https://github.com/lava-nc>`__
+
 Release plan
 ============
 
@@ -129,9 +131,11 @@ Open a python terminal and run based on the OS you are on:
    cd lava
    pip install -r build-requirements.txt
    pip install -r requirements.txt
-   export PYTHONPATH=~/lava
+   export PYTHONPATH=$(pwd)/src
    pyb -E unit
 
+Note that you should install the core Lava repository (lava) before installing other Lava libraries such as lava-optimization or lava-dl. Test your PYTHONPATH using echo $PYTHONPATH and ensure 'lava/src' is the first entry that precedes any additional Lava library src paths.
+
 [Windows]
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -145,9 +149,11 @@ Open a python terminal and run based on the OS you are on:
    cd lava
    pip install -r build-requirements.txt
    pip install -r requirements.txt
-   set PYTHONPATH=%HOMEPATH%\lava
+   set PYTHONPATH=%cd%\src
    pyb -E unit
 
+Note that you should install the core Lava repository (lava) before installing other Lava libraries such as lava-optimization or lava-dl. Test your PYTHONPATH using echo %PYTHONPATH% and ensure 'lava\src' is the first entry that precedes any additional Lava library src paths.
+
 You should expect the following output after running the unit tests:
 
 .. code-block:: console
@@ -168,7 +174,7 @@ You should expect the following output after running the unit tests:
 -------------------------------------------
 
 If you only need the lava package in your python environment, we will publish
-Lava releases via `GitHub Releases<https://github.com/lava-nc/lava/releases>`.
+Lava releases via `GitHub Releases <https://github.com/lava-nc/lava/releases>`__.
 Please download the package and install it.
 
 Open a python terminal and run:

lava-lib-dl/bootstrap/bootstrap.rst

@@ -83,7 +83,7 @@ bootstrap mode.
 Tutorials
 ---------
 
--  `MNIST digit classification <dummy_link>`__ TODO: UPDATE LINK
+-  * `MNIST digit classification <notebooks/mnist/train.html>`__ 
 
 Modules
 -------

lava-lib-dl/slayer/slayer.rst

@@ -30,13 +30,13 @@ Tutorials
 
 **End to End** 
 
-* `Oxford spike train regression <dummy_link>`__ TODO: UPDATE LINK 
-* `NMNIST digit classification <dummy_link>`__ TODO: UPDATE LINK 
-* `PilotNet steering angle prediction <dummy_link>`__ TODO: UPDATE LINK
+* `Oxford spike train regression <notebooks/oxford/train.html>`__
+* `NMNIST digit classification <notebooks/nmnist/train.html>`__ 
+* `PilotNet steering angle prediction <notebooks/pilotnet/train.html>`__
 
 **Deep Dive** 
 
-* `Dynamics and Neurons <dummy_link>`__ TODO: UPDATE LINK
+* `Dynamics and Neurons <notebooks/neuron_dynamics/dynamics.html>`__
 
 Modules
 -------

lava-lib-optimization/index.rst

@@ -0,0 +1,5 @@
+Lava - Optimization
+===================
+
+.. note::
+   API documentation will arrive shortly. In the mean time, please reviewdocstrings in code.

lava/lava.rst

@@ -1,4 +1,4 @@
-lava package
+Lava Package
 ============
 
 Subpackages

lava/modules.rst

@@ -1,7 +1,11 @@
-lava
+Lava
 ====
 
-.. toctree::
+.. note::
+   API documentation will arrive shortly. In the mean time, please reviewdocstrings in code.
+
+..
+  .. toctree::
    :maxdepth: 4
 
    lava

lava_api_documentation.rst

@@ -1,13 +1,13 @@
 Lava API Documentation
 ======================
 
- .. note::
-    API documentation will arrive shortly. In the mean time, please review docstrings in code.
+Welcome to Lava API documentation.
 
 .. toctree::
    :maxdepth: 4
    :caption: Contents:
 
    lava/modules.rst
    lava-lib-dl/index.rst
+   lava-lib-optimization/index.rst
 

optimization.rst

@@ -1,11 +1,11 @@
-Neuromorphic Constraint Optimization Library
-============================================
+Neuromorphic Constraint Optimization
+====================================
 
 **A library of solvers that leverage neuromorphic hardware for constrained optimization.**
 
 Constrained optimization searches for the values of input variables that minimize or maximize a given objective function, while the variables are subject to constraints. This kind of problem is ubiquitous throughout scientific domains and industries.
 Constrained optimization is a promising application for neuromorphic computing as 
-it [naturally aligns with the dynamics of spiking neural networks](https://doi.org/10.1109/JPROC.2021.3067593). When individual neurons represent states of variables, the neuronal connections can directly encode constraints between the variables: in its simplest form, recurrent inhibitory synapses connect neurons that represent mutually exclusive variable states, while recurrent excitatory synapses link neurons representing reinforcing states. Implemented on massively parallel neuromorphic hardware, such a spiking neural network can simultaneously evaluate conflicts and cost functions involving many variables, and update all variables accordingly. This allows a quick convergence towards an optimal state. In addition, the fine-scale timing dynamics of SNNs allow them to readily escape from local minima.
+it `naturally aligns with the dynamics of spiking neural networks <https://doi.org/10.1109/JPROC.2021.3067593>`__. When individual neurons represent states of variables, the neuronal connections can directly encode constraints between the variables: in its simplest form, recurrent inhibitory synapses connect neurons that represent mutually exclusive variable states, while recurrent excitatory synapses link neurons representing reinforcing states. Implemented on massively parallel neuromorphic hardware, such a spiking neural network can simultaneously evaluate conflicts and cost functions involving many variables, and update all variables accordingly. This allows a quick convergence towards an optimal state. In addition, the fine-scale timing dynamics of SNNs allow them to readily escape from local minima.
 
 This Lava repository currently provides constraint optimization solvers that leverage the benefits of neuromorphic computing for the following problems: 
 
@@ -94,9 +94,3 @@ Setup
    "export PYTHONPATH=$PYTHONPATH:$(pwd)" from lava-optimization/src/ 
    on your terminal (You should do this only after having added Lava to your PYTHONPATH. 
    This order is required for namespaces to be well defined).
-
-Lava Optimization Documentation
--------------------------------
-
- .. note::
-    The documentation will arrive shortly. In the mean time, please review docstrings in code.