CUDA | Hough Transform Tracking using Thrust
kandi X-RAY | CUDA Summary
kandi X-RAY | CUDA Summary
Hough Transform Tracking using Thrust
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QUESTION
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 00:46The 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:
QUESTION
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 11:14I 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.
QUESTION
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?
===================================
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 13:04Generally, 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
QUESTION
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 14:44I 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.
QUESTION
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 14:06As 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.
QUESTION
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 09:54You'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.
QUESTION
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 15:17You can use sklearn to calculate f1_score
QUESTION
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 16:19You can make the entire class inherit torch.nn.Module
like so:
QUESTION
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 23:19the 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
QUESTION
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 12:32Taylor Series can be used to calculate the value of pi accurate up to 5 decimal places.
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