The NVIDIA HPC SDK includes instructions for developing, profiling, and deploying software using the HPC Container Maker to simplify the creation of container images. The NVIDIA Container Runtime enables seamless GPU support in virtually all container frameworks, including Docker and Singularity. DOWNLOAD: NVIDIA GeForce 461.72 WHQL. Dec 29th 2020 A Christmas Miracle: 500,000 NVIDIA RTX 3080 Cards Found in Lost Shipping Container (103). NVIDIA® Nsight™ Systems is a system-wide performance analysis tool designed to visualize an application’s algorithms, help you identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs; from large server to our smallest SoC. Download Now Overview NVIDIA Nsight Systems is a low overhead performance analysis tool designed to. Now install the NVIDIA Container Toolkit (previously known as nvidia-docker2). WSL 2 support is available starting with nvidia-docker2 v2.3 and the underlying runtime library ( libnvidia-container = 1.2.0-rc.1). NVIDIA V100 TENSOR CORE GPU The World’s Most Powerful GPU The NVIDIA® V100 Tensor Core GPU is the world’s most powerful accelerator for deep learning, machine learning, high-performance computing (HPC), and graphics. Powered by NVIDIA Volta™, a single V100 Tensor Core GPU offers the performance of nearly.
NVIDIA® Nsight™ Systems is a system-wide performance analysis tool designed to visualize an application’s algorithms, help you identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs; from large server to our smallest SoC.
Overview
NVIDIA Nsight Systems is a low overhead performance analysis tool designed to provide nsights developers need to optimize their software. Unbiased activity data is visualized within the tool to help users investigate bottlenecks, avoid inferring false-positives, and pursue optimizations with higher probability of performance gains. Users will be able to identify issues, such as GPU starvation, unnecessary GPU synchronization, insufficient CPU parallelizing, and even unexpectedly expensive algorithms across the CPUs and GPUs of their target platform. It is designed to scale across a wide range of NVIDIA platforms such as: large Tesla multi-GPU x86 servers, Quadro workstations, Optimus enabled laptops, DRIVE devices with Tegra+dGPU multi-OS, and Jetson. NVIDIA Nsight Systems can even provide valuable insight into the behaviors and load of deep learning frameworks such as PyTorch and TensorFlow; allowing users to tune their models and parameters to increase overall single or multi-GPU utilization.
Platforms
Learn about Nsight Systems on your platform:
Release Highlights
2021.1 - Announcement Post
- Support for top ray tracing titles on Vulkan
- UX and performance improvements
2020.5 - Announcement Post
- NVIDIA Ampere Architecture
- CUDA memory allocation trace
- NCCL trace
- UX improvements
- Improved selection highlights
- Support for hi-DPI displays
2020.4 - Announcement Post
- NVIDIA Ampere Architecture
- CUDA 11.1
- CUDA memory allocation trace
- Labeled and color coded UVM transfers
- Launch Nsight Compute to profile kernel selected from within Nsight Systems
- Vulkan mGPU and device groups
- Timeline improvements
- Unified OpenGL workloads
- Frame duration statistics
- System wall clock allowing to compare multiple reports
- CLI on Windows
- UX improvements
2020.3 - Announcement Post
- NVIDIA Ampere Architecture
- CUDA 11.0
- CUDA Graph correlation
- OptiX
- Vulkan KHR ray tracing extension
- OpenMP
- CLI improvements
- UX improvements
Downloads
Available for profiling directly on Linux workstations and servers, including the NVIDIA DGX line, or remotely from a variety of hosts: Windows, Linux, or MacOSX.
Learn about other target platforms.
Documentation
Support
To provide feedback, request additional features, or report support issues, please use the Developer Forums.
System Requirements
Supported target operating systems for data collection:
- Ubuntu 16.04, 18.04 and 20.04*
- CentOS 7+*
- Red Hat Enterprise Linux 7+* * For older OS versions, please use Nsight Systems 2020.3
Supported target hardware
- GPU: Pascal or newer
- CPU: x86-64, Arm Server Base System Architecture and Power9 processors* * Intel Haswell architecture or newer is required for LBR sampling backtraces
Supported target software
- 64 bit applications only
- CUDA 10.0+ for CUDA tracing
- Requires driver r418 or newer
Supported host operating systems for data visualization:
- Windows 10+
- macOS X 10.9+
- Ubuntu 16.04, 18.04 and 20.04
Release Highlights
2021.1 - Announcement Post
- Support for top ray tracing titles on DirectX and Vulkan
- Stats on Windows CLI
- UX and performance improvements
2020.5 - Announcement Post
- NVIDIA Ampere Architecture
- UX improvements
- Improved selection highlights
- Support for hi-DPI displays
2020.4 - Announcement Post
- NVIDIA Ampere Architecture
- CUDA 11.1
- CUDA memory allocation trace
- Labeled and color coded UVM transfers
- Launch Nsight Compute to profile kernel selected from within Nsight Systems
- Vulkan mGPU and device groups
- Timeline improvements
- Unified OpenGL workloads
- Frame duration statistics
- System wall clock allowing to compare multiple reports
- CLI on Windows
- UX improvements
2020.3 - Announcement Post
- NVIDIA Ampere Architecture
- CUDA 11.0
- CUDA Graph correlation
- OptiX
- Vulkan KHR ray tracing extension
- DirectX Raytracing(DXR) Tier 1.1
- UX improvements
Downloads
Available for profiling directly on Linux workstations and servers, including the NVIDIA DGX line, or remotely from a variety of hosts: Windows, Linux, or MacOSX.
Visual Studio Integration*requires Nsights Sytems to be installed
Learn about other target platforms.
Documentation
Support
To provide feedback, request additional features, or report support issues, please use the Developer Forums.
System Requirements
Supported operating systems
- Windows 10
Supported target hardware
- GPU: Pascal or newer
- CPU: x86-64 processors
Supported target software
- 64 bit applications only
- CUDA 10.0+ for CUDA tracing
- Requires driver r418 or newer
Release Highlights
2019.4
- Ftrace collection on Linux
- Event table - alternative view of timeline data
- Improved CUDA memory transfer color scheme
- Android 9 support
- Expanded export capabilities
- New data sources: thread information, cuDNN, cuBLAS
2019.3
- QNX OS runtime backtraces for long blocking functions
- Exporters for SQLite & JSON
- NVTX, CUDA, OS Runtime Trace(OSRT)
Downloads
Nsight Systems is bundled as part of the following product development suites:
Jetson via NVIDIA SDK ManagerDocumentation
Support
To provide feedback, request additional features, or report support issues, please use the Developer Forums.
System Requirements
Supported Target Hardware
- ShieldTV
- Jetson AGX Xavier, Jetson TX2, Jetson TX1
- DRIVE AGX Pegasus, DRIVE AGX Xavier, DRIVE PX Parker AutoChauffeur, DRIVE PX Parker AutoCruise
Supported target operating systems for data collection:
- QNX
- Linux
- Android
Supported host operating systems for data visualization:
- Ubuntu 16.04, and 18.04
Features
Learn about feature support per target platform group
Workstations and Servers | Workstations and Gaming PCs | Autonomous Machines | Autonomous Vehicles | |
|---|---|---|---|---|
| View system-wide application behavior across CPUs and GPUs | ||||
| CPU cores utilization, process, & thread activities | ||||
| CPU thread periodic sampling backtraces | ||||
| CPU thread blocked state backtraces | ||||
| CPU performance counter sampling | ||||
| GPU workload trace | ||||
| GPU context switch trace | ||||
| SOC hypervisor trace | ||||
| SOC memory bandwidth sampling | ||||
| SOC Accelerators trace | ||||
| OS Event Trace | ||||
| Investigate CPU-GPU interactions and bubbles | ||||
| User annotations API trace NVIDIA Tools Extension API (NVTX) | ||||
| CUDA API | ||||
| CUDA libraries trace (cuBLAS, cuDNN & TensorRT) | ||||
| OpenGL API trace | ||||
| Vulkan API trace | ||||
| Direct3D12, Direct3D11, DXR, & PIX APIs | ||||
| OptiX | ||||
| Bidirectional correlation of API and GPU workload | ||||
| Identify GPU idle and sparse usage | ||||
| Multi-GPU Graphics trace | ||||
| Ready for big data | ||||
| Fast GUI capable of visualizing in excess of 10 million events on laptops | ||||
| Additional command line collection tool | ||||
| NV-Docker container support | ||||
| NVIDIA GPU Cloud support | ||||
| Minimum user privilege level |
* On Intel Haswell and newer CPU architecture
** Only with OS runtime trace enabled. Some syscalls such as handcrafted assembly may be missed. Backtraces may only appear if time threasholds are exceeded.
What Users Are Saying
AWS
Deepset achieves a 3.9x speedup and 12.8x cost reduction for training NLP models by working with AWS and NVIDIA
Tracxpoint
We noticed that our new Quadro P6000 server was ‘starved’ during training and we needed experts for supporting us. NVIDIA Nsight Systems helped us to achieve over 90 percent GPU utilization. A deep learning model that previously took 600 minutes to train, now takes only 90.
Felix Goldberg, Chief AI Scientist, TracepointNVIDIA
I used Nsight Systems to analyze our internal system and built a plan for optimizing both CPU and GPU usage, with significant performance and resource gains ultimately achieved to both. Overall, there is no alternative tool like Nsight which helps me to extract only, and exactly what I need to understand resource usage.
Sang Hun Lee, System Software Engineer, NVIDIANIH Center for Macromolecular Modeling and Bioinformatics at University of Illinois at Urbana-Champaign
Watch John Stone, present how he achieved over a 3x performance increase in VMD; a popular tool for analyzing large biomolecular systems.
Related Media
Direct3D11 Feature SpotlightThe 2019.6 release aims to provide a more detailed data collection, exploration, and collection control for all markets ranging from high performance computing to visual effects. 2019.6 introduces new data sources, improved visual data navigation, expanded CLI capabilities, extended export coverage and statistics.
Command Line Sessions Feature SpotlightNVIDIA Nsight Systems 2020.1 release adds CLI support for Power9 architecture. The ability to run multiple recording sessions simultaneously in CLI. UX improvements and stats export options in the GUI and CLI.
OpenMP Feature spotlightIn the 2020.3 release, Nsight Systems adds ability to analyze applications parallelized using OpenMP.
Statistics Driven ProfilingIn the 2019.3 release, Nsight Systems adds the ability to analyze reports using statistics to identify opportunities for improving your GPU-accelerated application.
2019.4 Release SpotlightThe 2019.4 release aims to provide a more detailed data collection, exploration, and collection control for all markets ranging from high performance computing to visual effects. 2019.4 introduces new data sources, improved visual data navigation, expanded CLI capabilities, extended export coverage and statistics.
Vulkan TraceIn the 2019.3 release, Nsight Systems adds the ability to trace vulkan on Windows and Linux targets; allowing you to inspect the CPU/GPU relationship and solve complicated frame stuttering issues in your Vulkan application.
Optimizing HPC simulation and visualization codeWatch John Stone, of the NIH Center for Macromolecular Modeling and Bioinformatics at University of Illinois at Urbana-Champaign, discuss how he achieved over a 3x performance increase of VMD, a popular tool for analyzing large biomolecular systems.
NVIDIA Jetson Partner Stories: StereolabsIn the drone industry, the weight and size of the main board is critical. With the ZED stereo camera by Stereolabs, developers can capture the world in 3D and map 3D models of indoor and outdoor scenes up to 20 meters. The small form factor of the Jetson TX1 enables Stereolabs to bring advanced computer vision capabilities to smaller and smaller systems. See what is possible when these two technologies come together in drones to power the latest virtual reality applications.
NVIDIA System Profiler - IntroductionAn introduction to the latest NVIDIA System Profiler. Includes an UI workthrough and setup details for NVIDIA System Profiler on the NVIDIA Jetson Embedded Platform. Download and learn more here.
Analyzing NCCL Usage with NVIDIA Nsight SystemsNVIDIA Nsight Systems now includes support for tracing NCCL (NVIDIA Collective Communications Library) usage in your CUDA application. Download and learn more here.
Nsight Systems Feature Spotlight: OpenMPNVIDIA® Nsight™ Systems is an indispensable system-wide performance analysis tool, designed to help developers tune and scale software across CPUs and GPUs. Download and learn more here.
CUDA on WSL User Guide
The guide for using NVIDIA CUDA on Windows Subsystem for Linux.
Introduction
Windows Subsystem for Linux (WSL) is a Windows 10 feature that enables users to run native Linux command-line tools directly on Windows. WSL is a containerized environment within which users can run Linux native applications from the command line of the Windows 10 shell without requiring the complexity of a dual boot environment. Internally, WSL is tightly integrated with the Microsoft Windows operating system, which allows it to run Linux applications alongside traditional Windows desktop and modern store apps.
With WSL 2 and GPU paravirtualization technology, Microsoft enables developers to run GPU accelerated applications on Windows.
The following document describes a workflow for getting started with running CUDA applications or containers in a WSL 2 environment.Getting Started
Getting started with running CUDA on WSL requires you to complete these steps in order:
Installing Microsoft Windows Insider Program Builds
Install the latest builds from the Microsoft Windows Insider Program
Register for the Microsoft Windows Insider Program.
Install the latest build from the Dev Channel.
Note:Ensure that you install Build version 20145 or higher. We recommend being on WIP OS 21332 and higher with Linux Kernel 5.4.91+ for the best performance.
You can check your build version number by running winver via the Windows Run command.
Installing NVIDIA Drivers
Download the NVIDIA Driver from the download section on the CUDA on WSL page. Choose the appropriate driver depending on the type of NVIDIA GPU in your system - GeForce and Quadro.
Install the driver using the executable. This is the only driver you need to install.
The DirectX WSL driver is installed automatically along with other driver components so no additional action is needed for installation. This driver enables graphics on WSL2.0 by supporting DX12 APIs. TensorFlow with DirectML support on WSL will get NV GPU hardware acceleration for training and inference workloads. There are no present capabilities in WSL, hence the driver is oriented towards compute/machine learning tasks. For some helpful examples, see https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-tensorflow-wsl.
Do not install any Linux display driver in WSL. The Windows Display Driver will install both the regular driver components for native Windows and for WSL support.
Note:NVIDIA is aware of a specific installation issue reported on mobile platforms with the WIP driver 465.12 posted on 11/16/2020. A known workaround will be to disable and reenable the GPU adapter from device manager at system start. We are working on a fix for this issue and will have an updated driver soon.
As an alternative, users may opt to roll back to an earlier driver from device manager driver updates.
Installing WSL 2
This section includes details about installing WSL 2, including setting up a Linux distribution of your choice from the Microsoft Store.
- Install WSL 2 by following the instructions in the Microsoft documentation available here.
- Ensure you have the latest kernel by clicking “Check for updates” in the “Windows Update” section of the Settings app. If the right update with the kernel 4.19.121+ is installed, you should be able to see it in the Windows Update history. Alternatively, you can check the version number by running the following command in PowerShell:
- If you don’t see this update, then in the Windows Update Advanced options, make sure to enable recommended Microsoft updates and run the check again:
- If you don’t have the last WSL kernel updated, you will see the following blocking warning upon trying to launch a Linux distribution within WSL 2.
- Launch the Linux distribution and make sure it runs in WSL 2 mode using the following command:
Setting up CUDA Toolkit
It is recommended to use the Linux package manager to install the CUDA for the Linux distributions supported under WSL 2. Follow these instructions to install the CUDA Toolkit.
First, set up the CUDA network repository. The instructions shown here are for Ubuntu 18.04. See the CUDA Linux Installation Guide for more information on other distributions.
Now install CUDA. Note that for WSL 2, you should use the cuda-toolkit-<version> meta-package to avoid installing the NVIDIA driver that is typically bundled with the toolkit. You can also install other components of the toolkit by choosing the right meta-package.
Do not choose the cuda, cuda-11-0, or cuda-drivers meta-packages under WSL 2 since these packages will result in an attempt to install the Linux NVIDIA driver under WSL 2.
Running CUDA Applications
Just run your CUDA app as you would run it under Linux! Once the driver is installed there is nothing more to do to run existing CUDA applications that were built on Linux.
A snippet of running the BlackScholes Linux application from the CUDA samples is shown below.
Build the CUDA samples available under /usr/local/cuda/samples from your installation of the CUDA Toolkit in the previous section. The BlackScholes application is located under /usr/local/cuda/samples/4_Finance/BlackScholes. Alternatively, you can transfer a binary built on Linux to WSL 2!
Setting up to Run Containers
This chapter describes the workflow for setting up the NVIDIA Container Toolkit in preparation for running GPU accelerated containers.
Install Docker
Use the Docker installation script to install Docker for your choice of WSL 2 Linux distribution. Note that NVIDIA Container Toolkit does not yet support Docker Desktop WSL 2 backend.
Install NVIDIA Container Toolkit
Now install the NVIDIA Container Toolkit (previously known as nvidia-docker2). WSL 2 support is available starting with nvidia-docker2 v2.3 and the underlying runtime library (libnvidia-container >= 1.2.0-rc.1).
For brevity, the installation instructions provided here are for Ubuntu 18.04 LTS.
Setup the stable and experimental repositories and the GPG key. The changes to the runtime to support WSL 2 are available in the experimental repository.
Install the NVIDIA runtime packages (and their dependencies) after updating the package listing.
Open a separate WSL 2 window and start the Docker daemon again using the following commands to complete the installation.
Running CUDA Containers
In this section, we will walk through some examples of running GPU containers in a WSL 2 environment.
Simple CUDA Containers
In this example, let’s run an N-body simulation CUDA sample. This example has already been containerized and available from NGC.
From the console, you should see an output as shown below.
Jupyter Notebooks
In this example, let’s run Jupyter notebook.
After the container starts, you can see the following output on the console.
After the URL is available from the console output, input the URL into your browser to start developing with the Jupyter notebook. Ensure that you replace 127.0.0.1 with localhost in the URL when connecting to the Jupyter notebook from the browser.
If you navigate to the Cell menu and select the Run All item, then check the log within the Jupyter notebook WSL 2 container to see the work accelerated by the GPU of your Windows PC.
Deep Learning Framework Containers
In this example, let’s run a TensorFlow container to do a ResNet-50 training run using GPUs using the 20.03 container from NGC. This is done by launching the container and then running the training script from the nvidia-examples directory.
Let's look at another example from Lesson 15 of the Learning TensorFlow tutorial. In this example, the code creates a random matrix with a given size as input and then does a element wise operation on the input tensor.
The example also allows you to observe the speedup when the code is run on the GPU. The source code is shown below.
Save the code as matmul.py on the host's C drive, which is mapped as /mnt/c in WSL 2. Run the code using the same 20.03 TensorFlow container in the previous example. The results of running this script, launched from the mounted drive C, on a GPU and a CPU are shown below. For simplicity the output is reduced.
The same example is now run on the CPU.
Get started quickly with AI training using pre-trained models available from NVIDIA and the NGC catalog. Follow the instructions in this post for more details.
Changelog
Nvidia Telemetry Container Download
- 3/23/2021: Resolved issues with nvidia-smi crashing on some systems.
- NVIDIA Driver for Windows 10: 470.14
- WIP build: 21332, WSL Linux kernel 5.4.91
- 3/9/2021
- WIP OS 21313+ and Linux kernel 5.4.91
- Do not use WIP OS 21327
- 1/28/2021: CUDA Toolkit 11.2 support, nvidia-smi, NVML support and critical performance improvements. The following software versions are supported with this preview release for WSL 2:
- NVIDIA Driver for Windows 10: 465.42
- Recommended WIP build: 21292
- 12/16/2020: Support for 3060Ti. Fix for installation problems observed in notebooks. The following software versions are supported with this preview release for WSL 2:
- NVIDIA Driver for Windows 10: 465.21
- 11/16/2020: The following software versions are supported with this preview release for WSL 2:
- NVIDIA Driver for Windows 10: 465.12
- 9/23/2020: The following software versions are supported with this preview release for WSL 2:
- 9/2/2020: The following software versions are supported with this preview release for WSL 2:
- NVIDIA Driver for Windows 10: 460.15
6/19/2020: Updated driver release to address cache coherency issues on some CPU systems, including AMD Ryzen.
The following software versions are supported with this preview release for WSL 2:- NVIDIA Driver for Windows 10: 455.41
6/17/2020: Initial Version.
The following software versions are supported with this preview release for WSL 2:- NVIDIA Driver for Windows 10: 455.38
- NVIDIA Container Toolkit: nvidia-docker2 (2.3) and libnvidia-container (>= 1.2.0-rc.1)
7.1. New Features
The following new features are included in this release:
- CUDA Toolkit 11.2 support
- Critical performance improvements
- nvidia-smi packaged
- Full NVML support
- IPC support
7.2. Resolved Issues
The following issues are resolved in this release:- Fixed nvidia-smi issues on some systems.
7.3. Known Limitations
The following features are not supported in this release:
Nvidia Display Container Download
- Note that NVIDIA Container Toolkit does not yet support Docker Desktop WSL 2 backend. Use Docker-CE for Linux instead inside your WSL 2 Linux distribution.
- nvidia-smi is now supported but in order to use it, please copy it to /usr/bin and set appropriate permissions with the below commands: We will soon provide a better user experience when there is OS support.
- CUDA debugging or profiling tools are not supported in WSL 2. This capability will be added in a future release.
- cumemmap IPC with fd is now supported. Other Legacy IPC APIs are not yet supported.
- Unified Memory is limited to the same feature set as on native Windows systems.
- With the NVIDIA Container Toolkit for Docker 19.03, only --gpus all is supported. This means that on multi-GPU systems it is not possible to filter for specific GPU devices by using specific index numbers to enumerate GPUs.
- When installing CUDA using the package manager, do not use the cuda, cuda-11-0, or cuda-drivers meta-packages under WSL 2. These packages have dependencies on the NVIDIA driver and the package manager will attempt to install the NVIDIA Linux driver which may result in issues. The NVIDIA Windows 10 driver should be the only driver present in the system.
- When running the NGC Deep Learning (DL) Framework GPU containers in WSL 2, you may encounter a message: Note that this message is an incorrect warning for WSL 2 and will be fixed in future releases of the DL Framework containers to correctly detect the NVIDIA GPUs. The DL Framework containers will still continue to be accelerated using CUDA on WSL 2.
7.4. Known Issues
The following are known issues in this release:
- CUDA on WSL2 is not to be used with the latest Microsoft Windows 10 Insider Preview Build 20226 due to known issues. Please use newer builds >= 20236 or revert to the older build 20221 to use CUDA on WSL2.
- Warning: Please do not update to WIP OS 21327 if you would like to use WSL with GPU support.
Troubleshooting
Here is a collection of potential errors that you may encounter when using CUDA on WSL 2:
Container Runtime Initialization Errors
- In some cases, when running a Docker container, you may encounter nvidia-container-cli : initialization error:
- This usually indicates that the right Microsoft Windows Insider Preview Builds, WSL 2, NVIDIA drivers and NVIDIA Container Toolkit may not be installed correctly. Use the dxdiag tools from the Run dialog and provide the diagnostic logs to NVIDIA. You can also use the CUDA on WSL 2 Forums to get in touch with NVIDIA product and engineering teams for help.
Notices
Notice
This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.
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