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92 lines
3.6 KiB
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92 lines
3.6 KiB
ReStructuredText
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Python Package Overview
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========
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This section provides an overview of the Python Package from a functionality perspective. If you wish to see examples check the `Python Simple & Advanced Examples section <python-examples.html>`_ all the classes and their respective functions you can find that in the `Python Class Reference Section <python-reference.html>`_.
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Below is a diagram that provides insights on the relationship between Vulkan Kompute objects and Vulkan resources, which primarily encompass ownership of either CPU and/or GPU memory.
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.. image:: ../images/kompute-architecture.jpg
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:width: 70%
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Package Installation
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^^^^^^^^^
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Make sure you have the following dependencies installed:
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* CMAKE v3.41+ (install in `Windows <https://tulip.labri.fr/TulipDrupal/?q=node/1081>`_, `Linux (Ubuntu) <https://vitux.com/how-to-install-cmake-on-ubuntu-18-04/>`_, `Mac <https://medium.com/r?url=https%3A%2F%2Fstackoverflow.com%2Fa%2F59825656%2F1889253>`_)
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* Vulkan SDK installed via `official website <https://vulkan.lunarg.com/sdk/home>`_
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* C++ compiler (eg. gcc for linux / mac, MSVC for Windows)
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Once you set up the package dependencies, you can install Kompute from ```Pypi``` using ```pip``` by running:
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.. code-block:: bash
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pip install kp
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You can also install from master branch using:
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.. code-block:: python
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pip install git+git://github.com/EthicalML/vulkan-kompute.git@master
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Core Python Components
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^^^^^^^^
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The Python package exposes three main classes:
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* :class:`kp.Manager` - Manages all high level Vulkan and Kompute resources created
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* :class:`kp.Sequence` - Contains a set of recorded operations that can be reused
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* :class:`kp.Tensor` - Core data component to manage GPU and host data used in operations
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One thing that you will notice is that the class :class:`kp::OpBase` and all its relevant operator subclasses are not exposed in Python.
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This is primarily because the way to interact with the operations are through the respective :class:`kp.Manager` and :class:`kp.Sequence` functions.
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More specifically, it can be through the following functions:
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* mgr.eval_<opname> - Runs operation under an existing named sequence
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* mgr.eval_<opname>_def - Runs operation under a new anonymous sequence
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* mgr.eval_async_<opname> - Runs operation asynchronously under an existing named sequence
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* mgr.eval_async_<opname>_def - Runs operation asynchronously under a new anonymous sequence
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* seq.record_<opname> - Records operation in sequence (requires sequence to be in recording mode)
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Log Level Configuration
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^^^^^^
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Logging inside the C++ uses the PyBind logging, which allows for all the std::cout to be passed to a python logger.
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All python output is logged to the logger with the name `kp`. You can interact with the logger similar to any python logging as per the example below:
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.. code-block:: python
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:linenos:
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>>> import kp
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>>> import logging
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>>>
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>>> kp_logger = logging.getLogger("kp")
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>>> kp_logger.setLevel(logging.INFO)
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>>>
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>>> kp.Manager()
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INFO:kp:Using physical device index {} found {}
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<kp.Manager object at 0x7f2ac075ca30>
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>>> kp_logger.setLevel(logging.DEBUG)
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>>>
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>>> kp.Manager()
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DEBUG:kp:Kompute Manager creating instance
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DEBUG:kp:Kompute Manager Instance Created
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DEBUG:kp:Kompute Manager creating Device
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INFO:kp:Using physical device index {} found {}
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DEBUG:kp:Kompute Manager device created
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DEBUG:kp:Kompute Manager compute queue obtained
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DEBUG:kp:Kompute Manager Destructor started
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INFO:kp:Destroying device
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DEBUG:kp:Kompute Manager Destroyed Device
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DEBUG:kp:Kompute Manager Destroyed Instance
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<kp.Manager object at 0x7f2af6109e30>
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