Additional functionality docs (NVIDIA#1621)

Signed-off-by: Sameer Raheja <sraheja@nvidia.com>
nartal1 · Jan 29, 2021 · d932209 · d932209
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diff --git a/docs/FAQ.md b/docs/FAQ.md
@@ -1,7 +1,7 @@
 ---
 layout: page
 title: Frequently Asked Questions
-nav_order: 10
+nav_order: 11
 ---
 # Frequently Asked Questions
 
@@ -205,7 +205,7 @@ user-defined functions on the GPU:
 #### RAPIDS-Accelerated UDFs
 
 UDFs can provide a RAPIDS-accelerated implementation which allows the RAPIDS Accelerator to perform
-the operation on the GPU.  See the [RAPIDS-accelerated UDF documentation](../docs/rapids-udfs.md)
+the operation on the GPU.  See the [RAPIDS-accelerated UDF documentation](additional-functionality/rapids-udfs.md)
 for details.
 
 #### Automatic Translation of Scala UDFs to Apache Spark Operations

diff --git a/docs/additional-functionality/README.md b/docs/additional-functionality/README.md
@@ -0,0 +1,7 @@
+---
+layout: page
+title: Additional Functionality
+nav_order: 9
+has_children: true
+permalink: /additional-functionality/
+---
diff --git a/docs/cache-serializer.md → ...itional-functionality/cache-serializer.md b/docs/cache-serializer.md → ...itional-functionality/cache-serializer.md
@@ -1,7 +1,8 @@
 ---
 layout: page
 title: RAPIDS Cache Serializer
-nav_order: 12
+parent: Additional Functionality
+nav_order: 2
 ---
 # RAPIDS Cache Serializer  
   Apache Spark provides an important feature to cache intermediate data and provide
@@ -36,4 +37,4 @@ nav_order: 12
   ```
   spark-shell --conf spark.sql.cache.serializer=com.nvidia.spark.rapids.shims.spark311.ParquetCachedBatchSerializer"
   ```
-  To use the default serializer don't set the `spark.sql.cache.serializer` conf
+  To use the default serializer don't set the `spark.sql.cache.serializer` conf
diff --git a/docs/ml-integration.md → ...dditional-functionality/ml-integration.md b/docs/ml-integration.md → ...dditional-functionality/ml-integration.md
@@ -1,7 +1,8 @@
 ---
 layout: page
 title: ML Integration
-nav_order: 8
+parent: Additional Functionality
+nav_order: 1
 ---
 # RAPIDS Accelerator for Apache Spark ML Library Integration
 

diff --git a/docs/rapids-udfs.md → docs/additional-functionality/rapids-udfs.md b/docs/rapids-udfs.md → docs/additional-functionality/rapids-udfs.md
@@ -1,7 +1,8 @@
 ---
 layout: page
 title: RAPIDS-Accelerated User-Defined Functions
-nav_order: 9
+parent: Additional Functionality
+nav_order: 3
 ---
 # RAPIDS-Accelerated User-Defined Functions
 

diff --git a/docs/benchmarks.md b/docs/benchmarks.md
@@ -1,7 +1,7 @@
 ---
 layout: page
 title: Benchmarks
-nav_exclude: true
+nav_order: 8
 ---
 # Benchmarks
 

diff --git a/docs/dev/README.md b/docs/dev/README.md
@@ -1,7 +1,7 @@
 ---
 layout: page
 title: Developer Overview
-nav_order: 12
+nav_order: 10
 has_children: true
 permalink: /developer-overview/
 ---

diff --git a/docs/dev/testing.md b/docs/dev/testing.md
@@ -7,4 +7,3 @@ parent: Developer Overview
 An overview of testing can be found within the repository at:
 * [Unit tests](https://github.com/NVIDIA/spark-rapids/tree/branch-0.3/tests)
 * [Integration testing](https://github.com/NVIDIA/spark-rapids/tree/branch-0.3/integration_tests)
-* [Benchmarks](../benchmarks.md)
diff --git a/docs/examples.md b/docs/examples.md
@@ -1,7 +1,7 @@
 ---
 layout: page
-title: Demos
-nav_order: 11
+title: Examples
+nav_order: 12
 ---
 # Demos
 

diff --git a/docs/tuning-guide.md b/docs/tuning-guide.md
@@ -42,7 +42,7 @@ called [RMM](https://github.com/rapidsai/rmm) to mitigate this overhead. By defa
 the plugin will allocate `90%` (`0.9`) of the memory on the GPU and keep it as a pool that can
 be allocated from. If the pool is exhausted more memory will be allocated and added to the pool.
 Most of the time this is a huge win, but if you need to share the GPU with other 
-[libraries](ml-integration.md) that are not aware of RMM this can lead to memory issues, and you
+[libraries](additional-functionality/ml-integration.md) that are not aware of RMM this can lead to memory issues, and you
 may need to disable pooling.
 
 ## Pinned Memory