Updated readme to reflect python example
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Alejandro Saucedo
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README.md
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README.md
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@ -15,7 +15,7 @@
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<td>
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<h1>Vulkan Kompute</h1>
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<h3>The General Purpose Vulkan Compute Framework.</h3>
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<h3>The General Purpose Vulkan Compute Framework for C++ and Python.</h3>
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</td>
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@ -29,10 +29,10 @@
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## Principles & Features
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* [Single header](#setup) library for simple import to your project
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* [Documentation](https://kompute.cc) leveraging doxygen and sphinx
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* [Asynchronous & parallel processing](#asynchronous-and-parallel-operations) capabilities with multi-queue command submission
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* [Non-Vulkan naming conventions](#architectural-overview) to disambiguate Vulkan vs Kompute components
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* [Single header](#setup) for simple import with flexible build-system configuration
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* Multi-language support with C++ as core SDK as well as [optimized Python bindings](#python-package)
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* [Asynchronous & parallel processing](#asynchronous-and-parallel-operations) support through GPU family queues
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* [Mobile enabled](#mobile-enabled) with examples in Android studio across several architectures
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* BYOV: [Bring-your-own-Vulkan design](#motivations) to play nice with existing Vulkan applications
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* Explicit relationships for GPU and host [memory ownership and memory management](https://kompute.cc/overview/memory-management.html)
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* [Short code examples](#simple-examples) showing the core features
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@ -118,7 +118,7 @@ int main() {
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mgr.evalOpAwaitDefault();
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// 5. Create managed sequence to submit batch operations to the CPU
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std::shared_ptr<kp::Sequence> sq = mgr.getOrCreateManagedSequence("seq").lock();
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std::shared_ptr<kp::Sequence> sq = mgr.getOrCreateManagedSequence("seq");
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// 5.1. Explicitly begin recording batch commands
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sq->begin();
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@ -255,13 +255,69 @@ You can also access the <a href="https://github.com/EthicalML/vulkan-kompute/tre
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</tr>
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</table>
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## Motivations
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## Python Package
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This project started after seeing that a lot of new and renowned ML & DL projects like Pytorch, Tensorflow, Alibaba DNN, Tencent NCNN - among others - have either integrated or are looking to integrate the Vulkan SDK to add mobile (and cross-vendor) GPU support.
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Besides the C++ core SDK you can also use the Python package of Kompute, which exposes the same core functionality.
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The Vulkan SDK offers a great low level interface that enables for highly specialized optimizations - however it comes at a cost of highly verbose code which requires 500-2000 lines of code to even begin writing application code. This has resulted in each of these projects having to implement the same baseline to abstract the non-compute related features of Vulkan. This large amount of non-standardised boiler-plate can result in limited knowledge transfer, higher chance of unique framework implementation bugs being introduced, etc.
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You can install from the repository by running:
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We are currently developing Vulkan Kompute not to hide the Vulkan SDK interface (as it's incredibly well designed) but to augment it with a direct focus on Vulkan's GPU computing capabilities. [This article](https://towardsdatascience.com/machine-learning-and-data-processing-in-the-gpu-with-vulkan-kompute-c9350e5e5d3a) provides a high level overview of the motivations of Kompute, together with a set of hands on examples that introduce both GPU computing as well as the core Vulkan Kompute architecture.
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```
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pip install .
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```
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Then you can interact with it from your interpreter. Below is the same sample as above "Your First Kompute (Simple Version)" but in Python:
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```python
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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mgr = Manager()
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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shaderFilePath = "shaders/glsl/opmult.comp"
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mgr.eval_async_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shaderFilePath)
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# Alternatively can pass raw string/bytes:
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# shaderFileData = """ shader code here... """
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# mgr.eval_algo_data_def([tensor_in_a, tensor_in_b, tensor_out], list(shaderFileData))
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mgr.eval_await_def()
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mgr.eval_tensor_sync_local_def([tensor_out])
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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```
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Similarly you can find the same extended example as above:
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```python
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mgr = Manager(0, [2])
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = Tensor([0, 0, 0])
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shaderFilePath = "../../shaders/glsl/opmult.comp"
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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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seq = mgr.create_sequence("op")
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mgr.eval_async_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shaderFilePath)
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mgr.eval_await_def()
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seq.begin()
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seq.record_tensor_sync_local([tensor_in_a])
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seq.record_tensor_sync_local([tensor_in_b])
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seq.record_tensor_sync_local([tensor_out])
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seq.end()
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seq.eval()
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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```
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## More examples
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@ -281,6 +337,7 @@ We are currently developing Vulkan Kompute not to hide the Vulkan SDK interface
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* [Android NDK Mobile Kompute ML Application](https://towardsdatascience.com/gpu-accelerated-machine-learning-in-your-mobile-applications-using-the-android-ndk-vulkan-kompute-1e9da37b7617)
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* [Game Development Kompute ML in Godot Engine](https://towardsdatascience.com/supercharging-game-development-with-gpu-accelerated-ml-using-vulkan-kompute-the-godot-game-engine-4e75a84ea9f0)
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## Build Overview
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The build system provided uses `cmake`, which allows for cross platform builds.
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@ -344,3 +401,11 @@ make mk_cmake MK_BUILD_TYPE="Release"
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make mk_run_tests
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```
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## Motivations
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This project started after seeing that a lot of new and renowned ML & DL projects like Pytorch, Tensorflow, Alibaba DNN, Tencent NCNN - among others - have either integrated or are looking to integrate the Vulkan SDK to add mobile (and cross-vendor) GPU support.
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The Vulkan SDK offers a great low level interface that enables for highly specialized optimizations - however it comes at a cost of highly verbose code which requires 500-2000 lines of code to even begin writing application code. This has resulted in each of these projects having to implement the same baseline to abstract the non-compute related features of Vulkan. This large amount of non-standardised boiler-plate can result in limited knowledge transfer, higher chance of unique framework implementation bugs being introduced, etc.
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We are currently developing Vulkan Kompute not to hide the Vulkan SDK interface (as it's incredibly well designed) but to augment it with a direct focus on Vulkan's GPU computing capabilities. [This article](https://towardsdatascience.com/machine-learning-and-data-processing-in-the-gpu-with-vulkan-kompute-c9350e5e5d3a) provides a high level overview of the motivations of Kompute, together with a set of hands on examples that introduce both GPU computing as well as the core Vulkan Kompute architecture.
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