question-mark
Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

From where to learn to use cudnn(python) within cupy? Please share some links to documentation or projects.

See original GitHub issue

Thank you for using and contributing to CuPy!

If your issue is a request for support in using CuPy, please post it on Stack Overflow or Google Groups (en / ja). Developer team members are monitoring questions on these channels.

If it is a bug report, please include the following information:

  • Conditions (you can just paste the output of python -c 'import cupy; cupy.show_config()')
    • CuPy version
    • OS/Platform
    • CUDA version
    • cuDNN/NCCL version (if applicable)
  • Code to reproduce
  • Error messages, stack traces, or logs

Issue Analytics

  • State:closed
  • Created 3 years ago
  • Comments:5 (3 by maintainers)

github_iconTop GitHub Comments

2reactions
emcastillocommented, May 27, 2020

@leofang dude, you are the best

1reaction
leofangcommented, May 27, 2020

Hi @LK54345

Can you recommend any example cupy code on using texture memory?

You can take a look at how texture/surface memory is tested: https://github.com/cupy/cupy/blob/master/tests/cupy_tests/cuda_tests/test_texture.py. The tests should be close enough to real-world use cases.

Documentation is a bit tricky.

Right, admittedly it is, but it’s mainly for easy migration from CUDA C or PyCUDA to CuPy, in which users already have a validated running code utilizing texture memory. For those cases, it is straightforward to translate to our API, but there’d be a learning curve, regardless whether you’re using CUDA C, PyCUDA, or CuPy, if you are going to build the code from scratch. Texture memory works very differently, after all.

Just FYI, for these inquiries please feel free to ask on StackOverflow (and tag your post with “cupy”, which would be monitored by the team’s bot) or on CuPy’s mailing list: https://groups.google.com/forum/#!forum/cupy.

Read more comments on GitHub >

github_iconTop Results From Across the Web

CuPy: NumPy & SciPy for GPU
CuPy is an open-source array library for GPU-accelerated computing with Python. CuPy utilizes CUDA Toolkit libraries including cuBLAS, cuRAND, cuSOLVER, ...
Read more >
CuPy Documentation - Read the Docs
CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in.
Read more >
NVIDIA Deep Learning cuDNN Documentation
This cuDNN 8.7.0 Installation Guide provides step-by-step instructions on how to install and check for correct operation of NVIDIA cuDNN on ...
Read more >
Build and install OpenCV from source with CUDA and cuDNN ...
IMPORTANT --------- Please add OPENCV_GENERATE_PKGCONFIG=1 flag when configuring to create the opencv.pc so other applications can find ...
Read more >
How to install CUDA Toolkit and cuDNN for deep learning
The cuDNN library: A GPU-accelerated library of primitives for deep neural networks. Using the cuDNN package, you can increase training speeds ...
Read more >

github_iconTop Related Medium Post

No results found

github_iconTop Related StackOverflow Question

No results found

github_iconTroubleshoot Live Code

Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free

github_iconTop Related Reddit Thread

No results found

github_iconTop Related Hackernoon Post

No results found

github_iconTop Related Tweet

No results found

github_iconTop Related Dev.to Post

No results found

github_iconTop Related Hashnode Post

No results found