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AidLearning-FrameWork | powerful mobile development platform, AidLearning builds | GPU library

 by   aidlearning Python Version: v0.92 License: Non-SPDX

 by   aidlearning Python Version: v0.92 License: Non-SPDX

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kandi X-RAY | AidLearning-FrameWork Summary

AidLearning-FrameWork is a Python library typically used in Telecommunications, Media, Telecom, Hardware, GPU applications. AidLearning-FrameWork has no bugs, it has no vulnerabilities and it has medium support. However AidLearning-FrameWork build file is not available and it has a Non-SPDX License. You can download it from GitHub.
🔥🔥AidLearning is a powerful mobile development platform, AidLearning builds a linux env supporting GUI, deep learning and visual IDE on Android...Now Aid supports OpenCL (GPU+NPU) for high performance acceleration...Linux on Android or HarmonyOS
Support
Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • AidLearning-FrameWork has a medium active ecosystem.
  • It has 4468 star(s) with 658 fork(s). There are 206 watchers for this library.
  • There were 3 major release(s) in the last 12 months.
  • There are 10 open issues and 176 have been closed. On average issues are closed in 24 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of AidLearning-FrameWork is v0.92
This Library - Support
Best in #GPU
Average in #GPU
This Library - Support
Best in #GPU
Average in #GPU

quality kandi Quality

  • AidLearning-FrameWork has 0 bugs and 0 code smells.
This Library - Quality
Best in #GPU
Average in #GPU
This Library - Quality
Best in #GPU
Average in #GPU

securitySecurity

  • AidLearning-FrameWork has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • AidLearning-FrameWork code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
This Library - Security
Best in #GPU
Average in #GPU
This Library - Security
Best in #GPU
Average in #GPU

license License

  • AidLearning-FrameWork has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
This Library - License
Best in #GPU
Average in #GPU
This Library - License
Best in #GPU
Average in #GPU

buildReuse

  • AidLearning-FrameWork releases are available to install and integrate.
  • AidLearning-FrameWork has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • AidLearning-FrameWork saves you 876 person hours of effort in developing the same functionality from scratch.
  • It has 1735 lines of code, 132 functions and 35 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #GPU
Average in #GPU
This Library - Reuse
Best in #GPU
Average in #GPU
Top functions reviewed by kandi - BETA

kandi has reviewed AidLearning-FrameWork and discovered the below as its top functions. This is intended to give you an instant insight into AidLearning-FrameWork implemented functionality, and help decide if they suit your requirements.

  • Convert a pre - trained model .
  • Builds a network .
  • Decode multiple positions .
  • Convert a label map to a list of categories .
  • Decodes a polynomial from the root image .
  • Draw a box on an image .
  • Create dialog box .
  • Draws a keypoint and kp .
  • Convert a convolution layer to a list of output blocks .
  • Loads weights from a given checkpoint .

AidLearning-FrameWork Key Features

🔥🔥AidLearning is a powerful mobile development platform, AidLearning builds a linux env supporting GUI, deep learning and visual IDE on Android...Now Aid supports OpenCL (GPU+NPU) for high performance acceleration...Linux on Android or HarmonyOS

AidLearning-FrameWork Examples and Code Snippets

  • 低AI开发门槛, 快速、简单、极致性能

低AI开发门槛, 快速、简单、极致性能

aid install examples-gpu 
#进入案例目录 
cd /home/examples-gpu 

Community Discussions

Trending Discussions on GPU
  • Vulkan : How could queues support different features? / VkQueue implementation
  • OpenCL local memory exists on Mali/Adreno GPU
  • How to force gpu usage with JavaFX?
  • GPU's not showing up on GKE Node even though they show up in GKE NodePool
  • "Attempting to perform BLAS operation using StreamExecutor without BLAS support" error occurs
  • SSBO CPU mapping returning correct data, but data is 'different' to the SSBO on GPU
  • Julia CUDA - Reduce matrix columns
  • Use of tf.GradientTape() exhausts all the gpu memory, without it it doesn't matter
  • Why does nvidia-smi return "GPU access blocked by the operating system" in WSL2 under Windows 10 21H2
  • How to run Pytorch on Macbook pro (M1) GPU?
Trending Discussions on GPU

QUESTION

Vulkan : How could queues support different features? / VkQueue implementation

Asked 2022-Apr-03 at 21:56

In my understanding, VkPhysicalDevice represents an implementation of Vulkan, which could be represented as a GPU and its drivers. We are supposed to record commands with VkCommandBuffers and send them through queues to, potentially, multithread the work we send to the gpu. That is why I understand the fact there can be multiple queues. I understand as well that QueueFamilies groups queues depending on the features they can do (the extensions available for them e.g. presentation, as well as graphics computations, transfer, etc).

However, if a GPU is able to do Graphics work, why are there queues unable to do so? I heard that using queues with less features could be faster, but why? What is a queue concretely? Is it only tied to vulkan implementation? Or is it related to hardware specific things?

I just don't understand why queues with different features exist, and even after searching through the Vulkan doc, StackOverflow, vulkan-tutorial and vkguide, the only thing I found was "Queues in Vulkan are an “execution port” for GPUs.", which I don't really understand and on which I can't find anything on google.

Thank you in advance for your help!

ANSWER

Answered 2022-Apr-03 at 21:56

A queue is a thing that consumes and executes commands, such that each queue (theoretically) executes separately from every other queue. You can think of a queue as a mouth, with commands as food.

Queues within a queue family typically execute commands using the same underlying hardware to process them. This would be like a creature with multiple mouths but all of them connect to the same digestive tract. How much food they can eat is separate from how much food they can digest. Food eaten by one mouth may have to wait for food previously eaten by another to pass through the digestive tract.

Queues from different families may (or may not) have distinct underlying execution hardware. This would be like a creature with multiple mouths and multiple digestive tracts. If a mouth eats, that food need not wait for food from a different mouth to digest.

Of course, distinct underlying execution hardware is typically distinct for a reason. Several GPUs have specialized DMA hardware for doing copies to/from device-local memory. Such hardware will typically expose a queue family that only allows transfer operations, and those transfer operations may be restricted in their byte alignment compared to transfers done on graphics-capable queues.

Note that these are general rules. Sometimes queues within a family do execute on different hardware, and sometimes queues between families use much of the same hardware. The API and implementations don't always make this clear, so you may have to benchmark different circumstances.

Source https://stackoverflow.com/questions/71729064

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install AidLearning-FrameWork

You can download it from GitHub.
You can use AidLearning-FrameWork like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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