.. _ENOT Tutorials:

#########
Tutorials
#########


.. _E1 ref:

`1. Tutorial - automatic quantization`_
=====================================================
.. _1. Tutorial - automatic quantization: https://github.com/ENOT-AutoDL/ENOT_Tutorials/blob/%ENOT_VERSION%/1.%20Tutorial%20-%20automatic%20quantization.ipynb

This notebook shows how to apply enot-autodl framework for automatic quantization to create quantized model for enot-lite framework.


.. _E2 ref:

`2. Tutorial - pruning`_
==================================
.. _2. Tutorial - pruning: https://github.com/ENOT-AutoDL/ENOT_Tutorials/blob/%ENOT_VERSION%/2.%20Tutorial%20-%20pruning.ipynb

This experimental notebook shows how to apply enot-autodl framework for
automatic network pruning and fine-tuning.


.. _E3 ref:

`3. Tutorial - Ultralytics YOLOv8 quantization`_
=================================================
.. _3. Tutorial - Ultralytics YOLOv8 quantization: https://github.com/ENOT-AutoDL/ENOT_Tutorials/blob/%ENOT_VERSION%/3.%20Tutorial%20-%20Ultralytics%20YOLOv8%20quantization.ipynb

This notebook shows how to apply enot-autodl framework for automatic network quantization of Ultralytics YOLOv8.


.. _E4 ref:

`4. Tutorial - ENOT baseline optimizer`_
=================================================
.. _4. Tutorial - ENOT baseline optimizer: https://github.com/ENOT-AutoDL/ENOT_Tutorials/blob/%ENOT_VERSION%/4.%20Tutorial%20-%20ENOT%20baseline%20optimizer.ipynb

This notebook describes how to use ENOT optimizer.


.. _E5 ref:

`5. Tutorial - label selector starting points`_
================================================
.. _5. Tutorial - label selector starting points: https://github.com/ENOT-AutoDL/ENOT_Tutorials/blob/%ENOT_VERSION%/5.%20Tutorial%20-%20label%20selector%20starting%20points.ipynb

This notebook demonstrates how to add additional starting points to
:class:`~enot.pruning.OptimalPruningLabelSelector`.


.. _Distributed (multi-gpu / multi-node) pretrain example reference:

`Distributed (multi-gpu / multi-node) pretrain example`_
========================================================
.. _Distributed (multi-gpu / multi-node) pretrain example: https://github.com/ENOT-AutoDL/ENOT_Tutorials/tree/%ENOT_VERSION%/multigpu_pretrain
.. _.sh script: https://github.com/ENOT-AutoDL/ENOT_Tutorials/blob/%ENOT_VERSION%/multigpu_pretrain/run_multigpu_pretrain.sh
.. _.py script: https://github.com/ENOT-AutoDL/ENOT_Tutorials/blob/%ENOT_VERSION%/multigpu_pretrain/multigpu_pretrain.py

In this folder you can find `.sh script`_ for running multi-gpu pretrain. You
can change it's configuration to run on single GPU, on multiple GPU within a
single node, or on multiple compute nodes with multiple GPUs.

The second file in this folder is a `.py script`_ which is launched by .sh
script. This script uses functions from other tutorials, but is adapted to run
in distributed manner. This script should be viewed as a reference point for
user-defined distributed pretrain scripts.

Distributed search is not recommended as it is under development. Moreover, the
search procedure is usually relatively fast. At the tuning stage, you will have
a regular model without any of the ENOT specifics, so it is your responsibility
to write correct distributed code (probably by wrapping found model with
`DistributedDataParallel module <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`_).