gpu | Dissecting the M1 's GPU for 3D acceleration | GPU library

 by   AsahiLinux C Version: Current License: No License

kandi X-RAY | gpu Summary

kandi X-RAY | gpu Summary

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

Research for an open source graphics stack for Apple M1.

            kandi-support Support

              gpu has a low active ecosystem.
              It has 662 star(s) with 18 fork(s). There are 58 watchers for this library.
              It had no major release in the last 6 months.
              gpu has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of gpu is current.

            kandi-Quality Quality

              gpu has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              gpu 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.

            kandi-Reuse Reuse

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

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            gpu Key Features

            No Key Features are available at this moment for gpu.

            gpu Examples and Code Snippets

            pip installation: GPU (CUDA)
            pypidot img1Lines of Code : 13dot img1no licencesLicense : No License
            copy iconCopy
            pip install --upgrade pip
            # Installs the wheel compatible with CUDA 11 and cuDNN 8.2 or newer.
            # Note: wheels only available on linux.
            pip install --upgrade "jax[cuda]" -f
            pip insta  
            Computes GPU options .
            pythondot img2Lines of Code : 57dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _compute_gpu_options(self):
                """Build the GPUOptions proto."""
                visible_device_list = []
                virtual_devices = []
                gpu_index = -1
                memory_growths = set()
                gpu_devices = self.list_physical_devices("GPU")
                pluggable_devices = self  
            Creates a map of GPU capabilities from match_list .
            pythondot img3Lines of Code : 52dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def create_gpu_capa_map(match_list,
              """Generates a map between GPU types and corresponding compute capability.
              This method is used for retrieving  
            Get GPU type .
            pythondot img4Lines of Code : 36dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def get_gpu_type():
              """Retrieves GPU type.
                String that is the name of the detected NVIDIA GPU.
                  e.g. 'Tesla K80'
                'unknown' will be returned if detected GPU type is an unknown name.
                  Unknown name refers to any GPU name  

            Community Discussions


            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.



            Answered 2021-Jun-14 at 11:14

            I fixed my issue.

            Here are the steps of solving that issue.

            1 ) Download CUDA from

            2 ) Download CUDNN from

            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.



            Crash on a protocol witness related issue
            Asked 2021-Jun-15 at 13:26

            In my iOS app "Progression" there is rarely a crash (1 crash in ~1000+ Sessions) I am currently not able to fix. The message is

            Progression: protocol witness for TrainingSetSessionManager.update(object:weight:reps:) in conformance TrainingSetSessionDataManager + 40

            This crash points me to the following method:



            Answered 2021-Jun-15 at 13:26

            While editing my initial question to add more context as Jay proposed I think it found the issue.

            What probably happens? The view where the crash is, contains a table view. Each cell will be configured before being presented. I use a flag which holds the information, if the amount of weight for this cell (it is a strength workout app) has been initially set or is a change. When prepareForReuse is being called, this flag has not been reset. And that now means scrolling through the table view triggers a DB write for each reused cell, that leads to unnecessary writes to the db. Unnecessary, because the exact same number is already saved in the db.

            My speculation: Scrolling fast could maybe lead to a race condition (I have read something about that issue with realm) and that maybe causes this weird crash, because there are multiple single writes initiated in a short time.

            Solution: I now reset the flag on prepareForReuse to its initial value to prevent this misbehaviour.

            The crash only happens when the cell is set up and the described behaviour happens. Therefor I'm quite confident I fixed the issue finally. Let's see. -- I was not able to reproduce the issue, but it also only happens pretty rare.



            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


            1. CUDA Runtime and cudnn version fits the guide from Tensorflow official documentation.
            2. I've also tried for TensorFlow-gpu = 2.0, still the same 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/] Successfully opened dynamic library

            for several minutes.

            And then stuck at next loading process

            2021-06-05 12:21:22.212818: I tensorflow/stream_executor/platform/default/] Successfully opened dynamic library

            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, 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



            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.



            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



            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...



            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



            vulkan - why a transition command needs an old layout information
            Asked 2021-Jun-15 at 10:01

            Why an old layout information is needed for setting a new layout for an image.

            As far as I understand, when setting an image layout, it became in specific memory arrangement tend for optimal need. So the new layout is not dependent on what was before. A memory layout for transfer reading (VK_IMAGE_LAYOUT_TRANSFER_SRC_OPTIMAL) is always the same and it doesn't matter what layout was before, isn't it?

            But even if the old layout information is needed for transition operation (because of some reason) - still, GPU hardware/driver knows the electronics circuits condition (memory layout in this case), so why do we need to give it an information it knows ?



            Answered 2021-Jun-15 at 10:01

            A memory layout for transfer reading (VK_IMAGE_LAYOUT_TRANSFER_SRC_OPTIMAL) is always the same and it doesn't matter what layout was before, isn't it?

            It does, if you want to convert it from the previous layout without losing the data. Otherwisely you indeed can use oldLayout=VK_IMAGE_LAYOUT_UNDEFINED.

            still, GPU hardware/driver knows the electronics circuits condition (memory layout in this case)

            It's not "electronic circuit". Potentially it's just a haystack of bits in RAM.

            Anyway. One paradigm of Vulkan is that it tries not to enforce memoization. Specifically it will often not remember state that is not part of vkCreate*. I think there is some functional programming influence...



            Some websites dont fully load/render in selenium headless mode
            Asked 2021-Jun-15 at 07:34

            So I have a problem that I have been noticing with selenium when I run it headless where some pages don't totally load/render some elements. I don't exactly know what's happening not to load 100%; maybe JS not running?

            My code:



            Answered 2021-Mar-13 at 11:51
            from selenium import webdriver
            from time import sleep
            options = webdriver.ChromeOptions()
                "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36")
            browser = webdriver.Chrome(options=options)



            Should I trust conda or pip when checking libraries installed?
            Asked 2021-Jun-15 at 00:54

            While I am in a conda environment, the 'conda list' and 'pip freeze' show different number of libraries. For example, 'tensorflow-gpu' is listed in 'pip freeze', but not in 'conda list'. If I want to use tensorflow-gpu in this environment, should I run pip install tensorflow-gpu to install it again, or not necessary?



            Answered 2021-Jun-15 at 00:54

            I think when you are using the conda environment. The conda list is going to show all the general packages that shared by the same conda environment. And the reason why 'tensorflow-gpu' is listed in 'pip freeze', but not in 'conda list', is because you used pip install to installed 'tensorflow-gpu'(could be you or the IDE). In this case, 'tensorflow-gpu' is only exists under this python project I believe. Actually, there is an official document about this topic.

            Issues may arise when using pip and conda together. When combining conda and pip, it is best to use an isolated conda environment. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software. If modifications are needed to the environment, it is best to create a new environment rather than running conda after pip. When appropriate, conda and pip requirements should be stored in text files.

            Use pip only after conda Install as many requirements as possible with conda then use pip.

            Pip should be run with --upgrade-strategy only-if-needed (the default).

            Do not use pip with the --user argument, avoid all users installs.

            And here is the link.



            I could not click licence agreement 'Accept' button in Hololens 2 Emulator
            Asked 2021-Jun-14 at 18:48

            I just launched the emulator App by double clicking it. It loaded (loading time is 10 to 15 min) with an audio to accept Microsoft licence agreement and login with current Microsoft id. It shows the licence agreement window as shown in below image:

            Hololens Emulator Licence Agreement

            I could not click the Accept button. So I could not proceed further. I used alt+mouse drag to bring the hand, but either the hand does not appear or sometimes even if it appears and moves, no raycast to point on the button. I tried toggling the Use mouse, use keyboard for simulation check boxes.

            Emulator version: 10.0.20346.1002

            My device sepc:

            Processor Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz 2.80 GHz, Installed RAM 16.0 GB (15.9 GB usable), System type 64-bit operating system, x64-based processor

            Windows Spec:

            Edition Windows 10 Enterprise, Version 21H1, OS build 19043.1023, Experience Windows Feature Experience Pack 120.2212.2020.0

            Windows SDK version - 10.0.20348.1

            GPU: Nvidia Geforce GTX 1070, DirectX version: 12, Driver model: WDDM 2.7



            Answered 2021-Jun-14 at 18:48

            Thanks Hernando - MSFT for helping me. The Accept button in the Licence agreemnet window can be clicked by moving back and forth the hand using W,A,S,D keys.

            Previously i thought that i should click using a raycast pointer. It would be nice if i can click using raycast because the current approach is very difficult for any new users.

            Additionally, sometimes mouse and keyboard inputs doesn't worked for me. This is because the 'Use Mouse' and 'Use Keyboard' check-boxes under the Simulation Panel is disabled. We have to ensure they are enabled for using mouse and keyboard for simulation inputs.



            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)



            Answered 2021-Jun-13 at 15:17

            You can use sklearn to calculate f1_score



            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



            Answered 2021-Jun-12 at 16:19

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


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


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