Added "How to preserve RAM"

docs/source/index.rst

@@ -78,8 +78,9 @@ Now, pick a tutorial or code sample and start utilizing Gen2 capabilities
    tutorials/hello_world.rst
    tutorials/multiple.rst
    tutorials/maximize_fov.rst
-   tutorials/dispaying_detections.rst
    tutorials/debugging.rst
+   tutorials/ram_usage.rst
+   tutorials/dispaying_detections.rst
 
 .. toctree::
    :maxdepth: 1

docs/source/tutorials/debugging.rst

@@ -4,6 +4,8 @@ Debugging DepthAI pipeline
 Currently, tools for debugging the DepthAI pipeline are limited. We plan on creating a software that would track all messages and queues,
 which would allow users to debug a "frozen" pipeline much easier, which is usually caused by a filled up :ref:`blocking queue <Blocking behaviour>`.
 
+.. _depthai_logging:
+
 DepthAI debugging level
 =======================
 

docs/source/tutorials/ram_usage.rst

@@ -0,0 +1,54 @@
+RAM usage
+=========
+
+All devices have 512 MiB (4 Gbit) on-board RAM, which is used for firmware (about 15MB), assets (a few KB up to 100MB+, eg. NN models), and other
+resources, such as message pools where messages are stored.
+
+If you enable :code:`info` :ref:`logging <depthai_logging>`, you will see how RAM is used:
+
+.. code-block:: bash
+
+    [info] Memory Usage - DDR: 41.23 / 358.82 MiB, CMX: 2.17 / 2.50 MiB,
+    LeonOS Heap: 20.70 / 78.63 MiB, LeonRT Heap: 3.51 / 23.84 MiB
+
+As you can see, RAM is split between the two LEON (CPU) cores, :code:`CMX` (used for image manipulation), and :code:`DDR` (everything else).
+If :code:`DDR` usage is close to the max (in this example, 358 MiB), you might get an error such as:
+
+.. code-block:: bash
+
+    [error] Neural network executor '0' out of '2' error: OUT_OF_MEMORY
+
+This means you should decrease RAM consumption, and we will take a look at a few ways on how to do this.
+
+Decreasing RAM consumption
+##########################
+
+- **Large frames**
+
+    If we change the resolution from 1080P to 4K in the :ref:`RGB video` example, DDR usage will increase from 41 MiB to 161 MiB. That's because
+    4K uses 4x more RAM compared to 1080P frame. An easy way to decrease RAM consumption is to use lower resolution / smaller frames.
+
+- **VideoEncoder**
+
+    :ref:`VideoEncoder` nodes can consume a lot of RAM, especially at high resolutions. For example, :ref:`RGB Encoding` example consumes
+    259 MiB. If we change the resolution from 4K to 1080P, we decrease DDR consumption to only 65 MiB.
+
+- **ImageManip**
+
+    Each :ref:`ImageManip` node will have it's own (output) pool of 4 frames (by default), so having multiple ImageManips that are manipulating
+    high resolution frames will consume a lot of DDR RAM. You can change the pool size with :code:`manip.setNumFramesPool(4)`. By default,
+    each pool "spot" will consume 1 MiB, even if it's a small 300x300 RGB frame (which is 270kB). Specifying the output frame size
+    can therefore decrease the RAM as well, eg. for a 300x300 RGB frame, you can set :code:`manip.setMaxOutputFrameSize(270000)`.
+
+- **XLinkIn**
+
+    Just like :ref:`ImageManip`, each :ref:`XLinkIn` node has it's own message pool as well. By default, each XLinkIn will consume 40 MiB, as
+    each pool "spot" has 5 MiB reserved, and there are 8 "spots" in the pool. If you are sending 300x300 RGB frames from the host to the device,
+    you can set :code:`xin.setMaxDataSize(270000)`, and also limit number of messages per pool :code:`xin.setNumFrames(4)`. This will decrease
+    DDR RAM consumption from 40 MiB to about 1 MiB.
+
+    If you are just sending control/config from the host, you can
+    set :code:`xin.setMaxDataSize(1)`, as :ref:`CameraControl` and :ref:`ImageManipConfig` don't have any extra data
+    (like :ref:`NNData`/:ref:`ImgFrame`/:ref:`Buffer`).
+
+.. include::  ../includes/footer-short.rst