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pyinn | CuPy fused PyTorch neural networks ops | GPU library

 by   szagoruyko Python Version: Current License: MIT

 by   szagoruyko Python Version: Current License: MIT

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kandi X-RAY | pyinn Summary

pyinn is a Python library typically used in Hardware, GPU, Deep Learning, Pytorch applications. pyinn has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
CuPy implementations of fused PyTorch ops. PyTorch version of [imagine-nn](https://github.com/szagoruyko/imagine-nn). The purpose of this package is to contain CUDA ops written in Python with CuPy, which is not a PyTorch dependency. An alternative to CuPy would be https://github.com/pytorch/extension-ffi, but it requires a lot of wrapping code like https://github.com/sniklaus/pytorch-extension, so doesn’t really work with quick prototyping. Another advantage of CuPy over C code is that dimensions of each op are known at JIT-ing time, and compiled kernels potentially can be faster. Also, the first version of the package was in PyCUDA, but it can’t work with PyTorch multi-GPU. ~~On Maxwell Titan X pyinn.conv2d_depthwise MobileNets are ~2.6x faster than F.conv2d~~ [benchmark.py](test/benchmark.py).
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Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • pyinn has a low active ecosystem.
  • It has 268 star(s) with 35 fork(s). There are 11 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 14 open issues and 5 have been closed. On average issues are closed in 19 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of pyinn is current.
pyinn Support
Best in #GPU
Average in #GPU
pyinn Support
Best in #GPU
Average in #GPU

quality kandi Quality

  • pyinn has 0 bugs and 44 code smells.
pyinn Quality
Best in #GPU
Average in #GPU
pyinn Quality
Best in #GPU
Average in #GPU

securitySecurity

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

license License

  • pyinn is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
pyinn License
Best in #GPU
Average in #GPU
pyinn License
Best in #GPU
Average in #GPU

buildReuse

  • pyinn releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • pyinn saves you 350 person hours of effort in developing the same functionality from scratch.
  • It has 838 lines of code, 56 functions and 11 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
pyinn Reuse
Best in #GPU
Average in #GPU
pyinn Reuse
Best in #GPU
Average in #GPU
Top functions reviewed by kandi - BETA

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

  • Calculate a batch of input gradients
    • Convert a column of data into an image
    • Compute the shape of a col2im2 image
    • Perform col2im transformation
  • Compute the convolutional op
    • Convert input image to col
    • Convert an image into colormap
    • Calculate the shape of an image
  • Backward computation
    • Returns the dtype of a tensor
    • Load a CUDA kernel
  • Compute the convolutional layer
    • Concatenate input image

      Get all kandi verified functions for this library.

      Get all kandi verified functions for this library.

      pyinn Key Features

      CuPy fused PyTorch neural networks ops

      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 pyinn

      You can download it from GitHub.
      You can use pyinn 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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