nvtop - NVIDIA GPUs htop like monitoring tool

  •        577

Nvtop stands for NVidia TOP, a (h)top like task monitor for NVIDIA GPUs. It can handle multiple GPUs and print information about them in a htop familiar way. NVTOP has a builtin setup utility that provides a way to specialize the interface to your needs. Simply press F2 and select the options that are the best for you.




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gputil - A Python module for getting the GPU status from NVIDA GPUs using nvidia-smi programmically in Python

  •    Python

GPUtil is a Python module for getting the GPU status from NVIDA GPUs using nvidia-smi. GPUtil locates all GPUs on the computer, determines their availablity and returns a ordered list of available GPUs. Availablity is based upon the current memory consumption and load of each GPU. The module is written with GPU selection for Deep Learning in mind, but it is not task/library specific and it can be applied to any task, where it may be useful to identify available GPUs. NVIDIA GPU with latest NVIDIA driver installed. GPUtil uses the program nvidia-smi to get the GPU status of all available NVIDIA GPUs. nvidia-smi should be installed automatically, when you install your NVIDIA driver.

TinyNvidiaUpdateChecker - Check for NVIDIA GPU driver updates!

  •    CSharp

This application has a simple concept, when launched it will check for new driver updates for your NVIDIA gpu! With this you no longer need waste your time searching if there's something new to get. HTML Agility Pack will automatically install when attempting to debug the project (make sure you're running the latest version of VS2017), or you may manually install it by doing the following: Open up your Package Manager Console and type in Install-Package HtmlAgilityPack.

coriander - Build NVIDIA® CUDA™ code for OpenCL™ 1.2 devices

  •    LLVM

Build applications written in NVIDIA® CUDA™ code for OpenCL™ 1.2 devices. Other systems should work too, ideally. You will need at a minimum at least one OpenCL-enabled GPU, and appropriate OpenCL drivers installed, for the GPU. Both linux and Mac systems stand a reasonable chance of working ok.

NVIDIA PerfGraph

  •    C++

A simple, cross platform performance monitoring application specifically designed to be used with nVidia's instrumented driver and the NVPerfSDK to give a graphical representation of internal GPU counters. Support for non-GPU counters is also available.

nvptx - How to: Run Rust code on your NVIDIA GPU

  •    Rust

Since 2016-12-31, rustc can compile Rust code to PTX (Parallel Thread Execution) code, which is like GPU assembly, via --emit=asm and the right --target argument. This PTX code can then be loaded and executed on a GPU. However, a few days later 128-bit integer support landed in rustc and broke compilation of the core crate for NVPTX targets (LLVM assertions). Furthermore, there was no nightly release between these two events so it was not possible to use the NVPTX backend with a nightly compiler.

CudaSift - A CUDA implementation of SIFT for NVidia GPUs (1.6 ms on a GTX 1060)

  •    Cuda

This is the fourth version of a SIFT (Scale Invariant Feature Transform) implementation using CUDA for GPUs from NVidia. The first version is from 2007 and GPUs have evolved since then. This version is slightly more precise and considerably faster than the previous versions and has been optimized for Kepler and later generations of GPUs. On a GTX 1060 GPU the code takes about 1.6 ms on a 1280x960 pixel image and 2.4 ms on a 1920x1080 pixel image. There is also code for brute-force matching of features that takes about 2.2 ms for two sets of around 1900 SIFT features each.



Before you begin, you may need to disable the opensource ubuntu NVIDIA driver called nouveau. If nouveau driver(s) are still loaded do not proceed with the installation guide and troubleshoot why it's still loaded.

nvidia-docker - Build and run Docker containers leveraging NVIDIA GPUs

  •    Makefile

The full documentation and frequently asked questions are available on the repository wiki. An introduction to the NVIDIA Container Runtime is also covered in our blog post.

xmrig-nvidia - Monero (XMR) NVIDIA miner

  •    C++

⚠️ You must update miners to version 2.5 before April 6 due Monero PoW change. XMRig is high performance Monero (XMR) NVIDIA miner, with the official full Windows support.

atop - System and process monitor for Linux

  •    C

Atop is an ASCII full-screen performance monitor for Linux that is capable of reporting the activity of all processes (even if processes have finished during the interval), daily logging of system and process activity for long-term analysis, highlighting overloaded system resources by using colors, etcetera. At regular intervals, it shows system-level activity related to the CPU, memory, swap, disks (including LVM) and network layers, and for every process (and thread) it shows e.g. the CPU utilization, memory growth, disk utilization, priority, username, state, and exit code. In combination with the optional kernel module netatop, it even shows network activity per process/thread. In combination with the optional daemon atopgpud, it also shows GPU activity on system level and process level. Resource consumption by all processes. It shows the resource consumption by all processes that were active during the interval, so also the resource consumption by those processes that have finished during the interval.

gpu-rest-engine - A REST API for Caffe using Docker and Go

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This repository shows how to implement a REST server for low-latency image classification (inference) using NVIDIA GPUs. This is an initial demonstration of the GRE (GPU REST Engine) software that will allow you to build your own accelerated microservices. This repository is a demo, it is not intended to be a generic solution that can accept any trained model. Code customization will be required for your use cases.

bi-att-flow - Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization

  •    Python

The model has ~2.5M parameters. The model was trained with NVidia Titan X (Pascal Architecture, 2016). The model requires at least 12GB of GPU RAM. If your GPU RAM is smaller than 12GB, you can either decrease batch size (performance might degrade), or you can use multi GPU (see below). The training converges at ~18k steps, and it took ~4s per step (i.e. ~20 hours). You can still omit them, but training will be much slower.

cuda-api-wrappers - Thin C++-flavored wrappers for the CUDA Runtime API

  •    C++

nVIDIA's Runtime API for CUDA is intended for use both in C and C++ code. As such, it uses a C-style API, the lowest common denominator (with a few notable exceptions of templated function overloads). This library of wrappers around the Runtime API is intended to allow us to embrace many of the features of C++ (including some C++11) for using the runtime API - but without reducing expressivity or increasing the level of abstraction (as in, e.g., the Thrust library). Using cuda-api-wrappers, you still have your devices, streams, events and so on - but they will be more convenient to work with in more C++-idiomatic ways.

MangoHud - A Vulkan and OpenGL overlay for monitoring FPS, temperatures, CPU/GPU load and more

  •    C

A Vulkan and OpenGL overlay for monitoring FPS, temperatures, CPU/GPU load and more. Once done, proceed to the installation.

nvvl - A library that uses hardware acceleration to load sequences of video frames to facilitate machine learning training

  •    C++

NVVL (NVIDIA Video Loader) is a library to load random sequences of video frames from compressed video files to facilitate machine learning training. It uses FFmpeg's libraries to parse and read the compressed packets from video files and the video decoding hardware available on NVIDIA GPUs to off-load and accelerate the decoding of those packets, providing a ready-for-training tensor in GPU device memory. NVVL can additionally perform data augmentation while loading the frames. Frames can be scaled, cropped, and flipped horizontally using the GPUs dedicated texture mapping units. Output can be in RGB or YCbCr color space, normalized to [0, 1] or [0, 255], and in float, half, or uint8 tensors. Using compressed video files instead of individual frame image files significantly reduces the demands on the storage and I/O systems during training. Storing video datasets as video files consumes an order of magnitude less disk space, allowing for larger datasets to both fit in system RAM as well as local SSDs for fast access. During loading fewer bytes must be read from disk. Fitting on smaller, faster storage and reading fewer bytes at load time allievates the bottleneck of retrieving data from disks, which will only get worse as GPUs get faster. For the dataset used in our example project, H.264 compressed .mp4 files were nearly 40x smaller than storing frames as .png files.

kaolin - A PyTorch Library for Accelerating 3D Deep Learning Research

  •    Python

NVIDIA Kaolin library provides a PyTorch API for working with a variety of 3D representations and includes a growing collection of GPU-optimized operations such as modular differentiable rendering, fast conversions between representations, data loading, 3D checkpoints and more. Kaolin library is part of a larger suite of tools for 3D deep learning research. For example, the Omniverse Kaolin App will allow interactive visualization of 3D checkpoints. To find out more about the Kaolin ecosystem, visit the NVIDIA Kaolin Dev Zone page.

aind2-cnn - AIND Term 2 -- Lesson on Convolutional Neural Networks

  •    Jupyter

(Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment.

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