CUDA | accelerated LIBSVM is a modification of the original LIBSVM | GPU library
kandi X-RAY | CUDA Summary
kandi X-RAY | CUDA Summary
GPU-accelerated LIBSVM is a modification of the original LIBSVM that exploits the CUDA framework to significantly reduce processing time while producing identical results. The functionality and interface of LIBSVM remains the same. The modifications were done in the kernel computation, that is now performed using the GPU.
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Lines of Code : 69License : Non-SPDX (Apache License 2.0)
Copydef 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",
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, 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
QUESTION
Code:
...ANSWER
Answered 2022-Jan-09 at 10:19The new version of OpenCV has some issues. Uninstall the newer version of OpenCV and install the older one using:
QUESTION
my computer has only 1 GPU.
Below is what I get the result by entering someone's code
...ANSWER
Answered 2021-Oct-12 at 08:52For the benefit of community providing solution here
This problem is because when keras run with gpu, it uses almost all
vram. So we needed to givememory_limitfor each notebook as shown below
QUESTION
Does it make sense to use Conda + Poetry for a Machine Learning project? Allow me to share my (novice) understanding and please correct or enlighten me:
As far as I understand, Conda and Poetry have different purposes but are largely redundant:
- Conda is primarily a environment manager (in fact not necessarily Python), but it can also manage packages and dependencies.
- Poetry is primarily a Python package manager (say, an upgrade of pip), but it can also create and manage Python environments (say, an upgrade of Pyenv).
My idea is to use both and compartmentalize their roles: let Conda be the environment manager and Poetry the package manager. My reasoning is that (it sounds like) Conda is best for managing environments and can be used for compiling and installing non-python packages, especially CUDA drivers (for GPU capability), while Poetry is more powerful than Conda as a Python package manager.
I've managed to make this work fairly easily by using Poetry within a Conda environment. The trick is to not use Poetry to manage the Python environment: I'm not using commands like poetry shell or poetry run, only poetry init, poetry install etc (after activating the Conda environment).
For full disclosure, my environment.yml file (for Conda) looks like this:
...ANSWER
Answered 2022-Feb-14 at 10:04As I wrote in the comment, I've been using a very similar Conda + Poetry setup in a data science project for the last year, for reasons similar to yours, and it's been working fine. The great majority of my dependencies are specified in pyproject.toml, but when there's something that's unavailable in PyPI, I add it to environment.yml.
Some additional tips:
- Add Poetry, possibly with a version number (if needed), as a dependency in
environment.yml, so that you get Poetry installed when you runconda env create, along with Python and other non-PyPI dependencies. - Consider adding
conda-lock, which gives you lock files for Conda dependencies, just like you havepoetry.lockfor Poetry dependencies.
QUESTION
Consider the following kernel, which reduces along the rows of a 2-D matrix
...ANSWER
Answered 2022-Jan-21 at 18:57Here is the code:
QUESTION
Hi guys I need a little help to understand why nvcc is not getting support to my gpu
I have a Nvidia RTX 3090 ti 24GB with this drivers
...ANSWER
Answered 2021-Dec-08 at 16:07In your posted system information, the last line
QUESTION
As I am working with my project, I noticed that when I run my app, inside the Application Output area I can see message:
...ANSWER
Answered 2021-Oct-17 at 10:27The problem is a problem in the Nvidia Driver 496.13. I had the same problem, and reverting to an older version fixed it
QUESTION
I've installed Windows 10 21H2 on both my desktop (AMD 5950X system with RTX3080) and my laptop (Dell XPS 9560 with i7-7700HQ and GTX1050) following the instructions on https://docs.nvidia.com/cuda/wsl-user-guide/index.html:
- Install CUDA-capable driver in Windows
- Update WSL2 kernel in PowerShell:
wsl --update - Install CUDA toolkit in Ubuntu 20.04 in WSL2 (Note that you don't install a CUDA driver in WSL2, the instructions explicitly tell that the CUDA driver should not be installed.):
ANSWER
Answered 2021-Nov-18 at 19:20Turns out that Windows 10 Update Assistant incorrectly reported it upgraded my OS to 21H2 on my laptop.
Checking Windows version by running winver reports that my OS is still 21H1.
Of course CUDA in WSL2 will not work in Windows 10 without 21H2.
After successfully installing 21H2 I can confirm CUDA works with WSL2 even for laptops with Optimus NVIDIA cards.
QUESTION
i have an import problem when executing my code:
...ANSWER
Answered 2021-Oct-06 at 20:27You're using outdated imports for tf.keras. Layers can now be imported directly from tensorflow.keras.layers:
QUESTION
I am trying to understand an example snippet that makes use of the PyTorch transposed convolution function, with documentation here, where in the docs the author writes:
"The padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input."
Consider the snippet below where a [1, 1, 4, 4] sample image of all ones is input to a ConvTranspose2D operation with arguments stride=2 and padding=1 with a weight matrix of shape (1, 1, 4, 4) that has entries from a range between 1 and 16 (in this case dilation=1 and added_padding = 1*(4-1)-1 = 2)
ANSWER
Answered 2021-Oct-31 at 10:39The output spatial dimensions of nn.ConvTranspose2d are given by:
QUESTION
I would like to make run an old N-body which uses OpenCL.
I have 2 cards NVIDIA A6000 with NVLink, a component which binds from an hardware (and maybe software ?) point of view these 2 GPU cards.
But at the execution, I get the following result:
Here is the kernel code used (I have put pragma that I estimate useful for NVIDIA cards):
...ANSWER
Answered 2021-Aug-07 at 12:36Your kernel code looks good and the cache tiling implementation is correct. Only make sure that the number of bodies is a multiple of local size, or alternatively limit the inner for loop to the global size additionally.
OpenCL allows usage of multiple devices in parallel. You need to make a thread with a queue for each device separately. You also need to take care of device-device communications and synchronization manually. Data transfer happens over PCIe (you also can do remote direct memory access); but you can't use NVLink with OpenCL. This should not be an issue in your case though as you need only little data transfer compared to the amount of arithmetic.
A few more remarks:
- In many cases N-body requires FP64 to sum up the forces and resolve positions at very different length scales. However on the A6000, FP64 performance is very poor, just like on GeForce Ampere. FP32 would be significantly (~64x) faster, but is likely insufficient in terms of accuracy here. For efficient FP64 you would need an A100 or MI100.
- Instead of 1.0/sqrt, use rsqrt. This is hardware supported and almost as fast as a multiplication.
- Make sure to use either FP32 float (1.0f) or FP64 double (1.0) literals consistently. Using double literals with float triggers double arithmetic and casting of the result back to float which is much slower.
EDIT: To help you out with the error message: Most probably the error at clCreateKernel (what value does status have after calling clCreateKernel?) hints that program is invalid. This might be because you give clBuildProgram a vector of 2 devices, but set the number of devices to only 1 and also have context only for 1 device. Try
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