CUDA | learning cuda | GPU library

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kandi X-RAY | CUDA Summary

CUDA is a C library typically used in Hardware, GPU, Deep Learning, Pytorch, Numpy applications. CUDA has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

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Support

CUDA has a low active ecosystem.

It has 7 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

CUDA has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CUDA is current.

Quality

CUDA has no bugs reported.

Security

CUDA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

CUDA does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

CUDA releases are not available. You will need to build from source code and install.

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CUDA Key Features

No Key Features are available at this moment for CUDA.

CUDA Examples and Code Snippets

Find CUDA configuration .

python

Lines of Code : 69

License : Non-SPDX (Apache License 2.0)

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def find_cuda_config():
  """Returns a dictionary of CUDA library and header file paths."""
  libraries = [argv.lower() for argv in sys.argv[1:]]
  cuda_version = os.environ.get("TF_CUDA_VERSION", "")
  base_paths = _list_from_env("TF_CUDA_PATHS",

Return a dictionary of all CUDA configurations .

python

Lines of Code : 67

License : Non-SPDX (Apache License 2.0)

Copy

def get_all_configs():
  """Runs all functions for detecting user machine configurations.

  Returns:
    Tuple
      (List of all configurations found,
       List of all missing configurations,
       List of all configurations found with warnings,

Validate Cuda configuration .

python

Lines of Code : 65

License : Non-SPDX (Apache License 2.0)

Copy

def validate_cuda_config(environ_cp):
  """Run find_cuda_config.py and return cuda_toolkit_path, or None."""

  def maybe_encode_env(env):
    """Encodes unicode in env to str on Windows python 2.x."""
    if not is_windows() or sys.version_info[0] !

Community Discussions

Trending Discussions on CUDA

Parallelize histogram creation in c++ with futures: how to use a template function with future?

How can I find to access to GPUs via Tensorflow in PyCharm?

Using TensorFlow with GPU taking a long time for loading library related to CUDA

Dynamic Library error while using Tensorflow with GPU

calling a __host__ function from a __host__ __device__ functon is not allowed

xorshift and its variations give unrandom results in C

How to calculate the f1-score?

Force BERT transformer to use CUDA

UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach()

Pi estimation using sphere volume

QUESTION

Parallelize histogram creation in c++ with futures: how to use a template function with future?

Asked 2021-Jun-16 at 00:46

Giving a bit of context. I'm using c++17. I'm using pointer T* data because this will interop with cuda code. I'm trying write a parallel version (on CPU) of a histogram creator. The sequential version:

...

ANSWER

Answered 2021-Jun-16 at 00:46

The issue you are having has nothing to do with templates. You cannot invoke std::async() on a member function without binding it to an instance. Wrapping the call in a lambda does the trick.

Here's an example:

Source https://stackoverflow.com/questions/67994778

QUESTION

How can I find to access to GPUs via Tensorflow in PyCharm?

Asked 2021-Jun-15 at 14:43

I have a problem about not accessing GPU in PyCharm and I use NVIDIA as GPU.

I installed tensorflow-gpu in Python Interpreter of Setting part in Pycharm and then I run the code but I still cannot access it.

I wonder if I should use CUDA library? How can I fix it?

Here is my code snippet which is shown below.

...

ANSWER

Answered 2021-Jun-14 at 11:14

I fixed my issue.

Here are the steps of solving that issue.

1 ) Download CUDA from https://developer.nvidia.com/cuda-downloads

2 ) Download CUDNN from https://developer.nvidia.com/rdp/cudnn-download

3 ) Copy bin,include and lastly lib from CUDNN zip file and paste it C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA{version}

4 ) Then run the .py code in PyCharm and it perceives GPU at last.

Source https://stackoverflow.com/questions/67952801

QUESTION

Using TensorFlow with GPU taking a long time for loading library related to CUDA

Asked 2021-Jun-15 at 13:04

Machine Setting:

GPU: GeForce RTX 3060
Driver Version: 460.73.01
CUDA Driver Veresion: 11.2
Tensorflow: tensorflow-gpu 1.14.0
CUDA Runtime Version: 10.0
cudnn: 7.4.1

Note:

CUDA Runtime and cudnn version fits the guide from Tensorflow official documentation.
I've also tried for TensorFlow-gpu = 2.0, still the same problem.

Problem:

I am using Tensorflow for an objection detection task. My situation is that the program will stuck at

2021-06-05 12:16:54.099778: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10

for several minutes.

And then stuck at next loading process

2021-06-05 12:21:22.212818: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7

for even longer time. You may check log.txt for log details.

After waiting for around 30 mins, the program will start to running and WORK WELL.

However, whenever program invoke self.session.run(...), it will load the same two library related to cuda (libcublas and libcudnn) again, which is time-wasted and annoying.

I am confused that where the problem comes from and how to resolve it. Anyone could help?

Discussion Issue on Github

===================================

Update

After @talonmies 's help, the problem was resolved by resetting the environment with correct version matching among GPU, CUDA, cudnn and tensorflow. Now it works smoothly.

...

ANSWER

Answered 2021-Jun-15 at 13:04

Generally, if there are any incompatibility between TF, CUDA and cuDNN version you can observed this behavior.

For GeForce RTX 3060, support starts from CUDA 11.x. Once you upgrade to TF2.4 or TF2.5 your issue will be resolved.

For the benefit of community providing tested built configuration

CUDA Support Matrix

Source https://stackoverflow.com/questions/67847219

QUESTION

Dynamic Library error while using Tensorflow with GPU

Asked 2021-Jun-15 at 10:13

I am programming in Python 3.8 with Tensorflow installed along with my natural language processing project. When I want to begin the training phase, I get this message right before I begin...

...

ANSWER

Answered 2021-Mar-10 at 14:44

I would suggest you to use conda (Ananconda/Miniconda) to create a separate environment and install tensorflow-gpu, cudnn and cudatoolkit. Miniconda has a much smaller footprint than Anaconda. I would suggest you to install Miniconda if you do not have conda already.

Quick Installtion

Source https://stackoverflow.com/questions/66553987

QUESTION

calling a __host__ function from a __host__ __device__ functon is not allowed

Asked 2021-Jun-14 at 14:06

I am trying to use thrust with Opencv classes. The final code will be more complicated including using device memory but this simple example does not build successfully.

...

ANSWER

Answered 2021-Jun-14 at 14:06

As pointed out in the comments, for the code you have shown, you are getting a warning and this warning can be safely ignored.

For usage in CUDA device code:

For a C++ class to be usable in CUDA device code, any relevant member functions that will be used explicitly or implicitly in CUDA device code, must be marked with the __device__ decorator. (There are a few exceptions e.g. for defaulted constructors which don't apply here.)

The OpenCV class you are attempting to use (cv::KeyPoint), doesn't meet these requirements for use in device code. It won't be usable as-is.

There may be a few options:

Recast your work using cv::KeyPoint to use some class that provides similar functionality, that you write yourself, in such a way as to be properly designed and decorated.
Perhaps see if OpenCV built with CUDA has an alternate version here (properly designed/decorated) (my guess would be it probably doesn't)
Rewrite OpenCV itself, taking into account all necessary design changes to allow the cv::KeyPoint class to be usable in device code.
As a variant of suggestion 1, copy the relevant data .response to a separate set of classes or just a bare array, and do your selection work based on that. The selection work done there can be used to "filter" the original array.

Source https://stackoverflow.com/questions/67966538

QUESTION

xorshift and its variations give unrandom results in C

Asked 2021-Jun-14 at 09:54

I'm trying to create a pseudo-random generator API, but numbers generated by xorshift have unrandom nature. You can see the algorithm and tests here:

...

ANSWER

Answered 2021-Jun-14 at 09:54

You're looking at random numbers uniformly distributed between 0 and 18,446,744,073,709,551,615 (UINT64_MAX). All numbers between 10,000,000,000,000,000,000 and 18,446,744,073,709,551,615 start with a 1, so the skewed distribution is to be expected.

Source https://stackoverflow.com/questions/67967747

QUESTION

How to calculate the f1-score?

Asked 2021-Jun-14 at 07:07

I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.

My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall)) but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code? (Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)

...

ANSWER

Answered 2021-Jun-13 at 15:17

You can use sklearn to calculate f1_score

Source https://stackoverflow.com/questions/67959327

QUESTION

Force BERT transformer to use CUDA

Asked 2021-Jun-13 at 09:57

I want to force the Huggingface transformer (BERT) to make use of CUDA. nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0 or .to(cuda:0).

The code below is basically a customized part from german sentiment BERT working example

...

ANSWER

Answered 2021-Jun-12 at 16:19

You can make the entire class inherit torch.nn.Module like so:

Source https://stackoverflow.com/questions/67948945

QUESTION

UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach()

Asked 2021-Jun-12 at 23:00

I'm new on PyTorch and I'm trying to code with it so I have a function called OH which tack a number and return a vector like this

...

ANSWER

Answered 2021-Apr-30 at 23:19

the problem is that you are receiving a tensor on the act function on the Network and then save it as a tensor just remove the tensor in the action like this

Source https://stackoverflow.com/questions/67341208

QUESTION

Pi estimation using sphere volume

Asked 2021-Jun-12 at 14:46

My task is to calculate the approximate value of pi with an accuracy of at least 10^-6. The Monte Carlo algorithm does not provide the required accuracy. I need to use the calculation only through the volume of the sphere. What do you advise? I would be glad to see examples of code in CUDA or pure C++. Thank you.

...

ANSWER

Answered 2021-Jun-12 at 12:32

Taylor Series can be used to calculate the value of pi accurate up to 5 decimal places.

Source https://stackoverflow.com/questions/67948799

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

Vulnerabilities

No vulnerabilities reported

Install CUDA

You can download it from GitHub.

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