.. _building_pipelines:

Developing End-to-End Applications on MLSoC
###########################################

This guide aims to assist developers in building applications on an MLSoC (System on Chip) by leveraging its 
specialized hardware capabilities. It provides a structured approach to porting applications from traditional 
x86/Mac architectures to SiMa.ai's MLSoC environment, debugging and testing with GStreamer for high performance and efficiency.

This guide focuses on porting a classification application to run optimized for MLSoC. Advanced sections will focus on how to communicate with external systems, camera inputs, video decode/encode, etc.

What you will learn
===================

* How to integrate a model compiled for SiMa.ai's MLA into an application and run inference
* What graphs and capabilities are available for the EV74 DSP to be used with pre and post-processing
* What hardware-accelerated APIs, libraries and plugins are available to build an application with the MLSoC
* How to develop end-to-end pipelines using Python and Gstreamer on the MLSoC

Prerequisites
=============

.. list-table::
   :widths: 21 79
   :header-rows: 1

   * - Requirement Type
     - Requirement Details
   * - Palette and MLSOC Firmware
     - - Palette: |var_palette_reqs_version|
       - MLSoC Firmware: |var_palette_reqs_mlsoc_fw_version|

Development environment setup
=============================

* On your host development machine, follow the instructions to ensure Palette is fully installed by following the Palette :ref:`palette installation` guide.
* Have an MLSoC device or development kit available with the latest firmware installed as specified in the prerequisites.
  
    * Have ``ssh`` access to the development kit set up.

Development journey
===================

When developing applications, it is recommended to follow to main steps:

#. Develop a GStreamer pipeline using ``gst-launch`` to debug the application and build it step by step.
#. Once the application is working as expect it, create an MPK project to package it as an ``mpk`` and be able to deploy to any MLSoC device. 

.. toctree::
    :maxdepth: 1
    :caption: Contents:

    developing_gstreamer_apps/main.rst
    developing_gstreamer_apps/hw_sw_overview.rst
    developing_gstreamer_apps/ml_app_overview.rst
    developing_gstreamer_apps/reference_app.rst
    developing_gstreamer_apps/modelsdk_compile.rst
    developing_gstreamer_apps/gstreamer_app_overview.rst
    developing_gstreamer_apps/compiling_plugins.rst
    developing_gstreamer_apps/gstreamer_inputsrc_a65.rst
    developing_gstreamer_apps/gstreamer_preproc_cvu.rst
    developing_gstreamer_apps/gstreamer_inference_mla.rst
    developing_gstreamer_apps/gstreamer_postproc_cvu.rst
    developing_gstreamer_apps/gstreamer_argmaxprint.rst
    developing_mpk_apps/developing_mpk_app.rst