nvsmi | An nvidia-smi -like interface for R | GPU library

by cur-dev C Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | nvsmi Summary

nvsmi is a C library typically used in Hardware, GPU applications. nvsmi has no bugs, it has no vulnerabilities and it has low support. However nvsmi has a Non-SPDX License. You can download it from GitHub.

An nvidia-smi-like interface for R. This works via NVML, and does not actually require the nvidia-smi utility to be installed or in your $PATH (although it probably will be anyway if NVML is installed). Currently the package has most (all?) of the useful NVML "get" functions available, as well as a few high-level interfaces (see the API section below). More NVML wrappers will be added over time (if you want one, feel free to ask for it, or better yet, submit a PR). At this time I have no plans to add the "set" functions, because these require root and I think it's a monstrously bad idea to give an R process root.

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Quality

Security

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Support

nvsmi has a low active ecosystem.

It has 7 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of nvsmi is current.

Quality

nvsmi has 0 bugs and 0 code smells.

Security

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

nvsmi code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

nvsmi has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

Installation instructions, examples and code snippets are available.

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nvsmi Key Features

No Key Features are available at this moment for nvsmi.

nvsmi Examples and Code Snippets

No Code Snippets are available at this moment for nvsmi.

Community Discussions

Trending Discussions on nvsmi

Why does nvidia-smi return "GPU access blocked by the operating system" in WSL2 under Windows 10 21H2

What is the correct version of CUDNN for CUDA 11.0

Runtimeerror: Cuda out of memory - problem in code or gpu?

QUESTION

Why does nvidia-smi return "GPU access blocked by the operating system" in WSL2 under Windows 10 21H2

Asked 2021-Nov-18 at 19:20

Installing CUDA on WSL2

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.):

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ANSWER

Answered 2021-Nov-18 at 19:20

Turns 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.

Source https://stackoverflow.com/questions/70011494

QUESTION

What is the correct version of CUDNN for CUDA 11.0

Asked 2020-Dec-13 at 01:22

I want to start using tensorflow-gpu, and I looked some stuff up, and found out that I need to ensure that I have both CUDA and CUDNN. So, I opened up the command prompt and ran the command nvidia-smi to check my CUDA version:

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ANSWER

Answered 2020-Dec-13 at 01:22

What nvidia-smi shows is not the CUDA version that you have installed, but the maximum CUDA version that your driver supports.

CUDA 11.0 has been announced but not released yet (as of June 2nd 2020), so you should use CUDA 10.2 as it's the latest available version.

Source https://stackoverflow.com/questions/62149833

QUESTION

Runtimeerror: Cuda out of memory - problem in code or gpu?

Asked 2020-Sep-14 at 02:35

I am currently working on a computer vision project. I keep getting a runtime error that says "CUDA out of memory". I have tried all possible ways like reducing batch size and image resolution, clearing the cache, deleting variables after training starts, reducing image data and so on... Unfortunately, this error doesn't stop. I have a Nvidia Geforce 940MX graphics card on my HP Pavilion laptop. I have installed cuda 10.2 and cudNN from the pytorch installation page. My aim was to create a flask website out of this model but I am stuck with this issue. Any suggestions to this problem will be helpful.

This is my code

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ANSWER

Answered 2020-Sep-14 at 02:35

I ran your model on Kaggle with a batch_size = 48 and attached a screenshot of the requirements. An epoch takes around 30-40 mins to complete. I would say you could easily train your model with the 30+ hrs Kaggle gives.

I also tested inference with batch_size=1 and set num_workers=0 in your dataloader, the GPU Usage is 1.3GB.

I would recommend you to train your model on Kaggle/Colab and download the weights onto your local machine. Later, you could run inference on your machine with batch size = 1. Inference, usually happens faster.

Source https://stackoverflow.com/questions/63871643

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

Vulnerabilities

No vulnerabilities reported

Install nvsmi

The development version is maintained on GitHub:. You will need to have NVIDIA's NVML library installed to build the package. NVML is bundled with CUDA, which you can download from the NVIDIA website. The package needs to be able to find nvml.h and libnvidia-ml.so. We try looking in several locations for these files, but you can manually specify the paths for the header and library with the configure-args --with-nvml-include and --with-nvml-lib, respectively. We also recommend setting /usr/local/cuda/ as a link to your latest CUDA installation, which is an option when you install CUDA via the runfile (and should automatically happen if you use the .deb or .rpm). If you have trouble building the package, please open an issue with an output of the package configure (what you see when you run ./configure), as well as the locations of nvml.h and libnvidia-ml.so on your system. I have literally no idea how to get this to work on Windows at this time.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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