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Установка cuda for mac os

Установка cuda for mac os

Производитель графических процессоров NVIDIA отказывается от поддержки CUDA для macOS. Платформа для параллельных вычислений, использующаяся для аппаратного ускорения рендеринга во многих продуктах, «в последний раз» представлена в выпуске CUDA Toolkit 10.2, как сказано в логе изменений.

Новость нельзя назвать большим сюрпризом: Apple не комплектует свои продукты видеокартами NVIDIA последние несколько лет. Для потребительской линейки компания использует интегрированные карты от Intel, а для профессиональных станций, например, Mac Pro, iMac Pro или 16-дюймового MacBook Pro, — карты от AMD.

Однако даже подключить NVIDIA как внешнюю видеокарту (eGPU) стало проблемой, начиная с выхода macOS Mojave (10.14) в прошлом году: для последних карт просто нет драйверов. NVIDIA опубликовали официальное обращение пользователям, где сказали, что ничего с этим не могут сделать:

«Apple полностью контролирует драйверы для Mac OS. К сожалению, на данный момент NVIDIA не может выпустить драйвер без официального разрешения Apple

Разработчики Apple дали анонимный комментарий изданию Appleinsider, почему купертинская компания приняла такое решение:

«Речь не идет о том, что в Metal 2 нельзя добавить поддержку NVIDIA — у них отличные инженеры, и с производительностью проблем не будет. Просто кто-то наверху этого не хочет.»

Где получить аппаратное ускорение без CUDA?

Большинство коммерческих движков, включая V-Ray GPU, OctaneRender и Redshift, сейчас работают на CUDA. Фреймворк OptiX от NVIDIA, поддержку которого разработчики сейчас активно добавляют в свои продукты (например, последние версии Blender или V-Ray Next), тоже требует CUDA Toolkit.

Другие рендеры, например, Blender Cycles, поддерживают карты AMD с помощью OpenCL, но Apple прекратили поддержку и этого API, заменив OpenCL на собственный Metal 2.

Получается, что на момент выхода этой новости практически единственный вариант для пользователей Mac, которые хотят продолжать пользоваться аппаратным ускорением, — это AMD с Radeon ProRender. В будущем поддержка Metal API может также появится и в Redshift и OctaneRender.

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Установка cuda for mac os

The installation instructions for the CUDA Toolkit on Mac OS X.

1. Introduction

CUDA В® is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

This guide will show you how to install and check the correct operation of the CUDA development tools.

1.1. System Requirements

Table 1. Mac Operating System Support in CUDA 10.2

Toolchain Mac OSX Version (native x86_64)
Xcode Apple LLVM 10.13.6 (17G2307)
10.2 (10B61) 10.0.1 YES

(*) As specific minor versions of Mac OSX are released, the corresponding CUDA drivers can be downloaded from here.

Before installing the CUDA Toolkit, you should read the Release Notes, as they provide important details on installation and software functionality.

1.2. About This Document

This document is intended for readers familiar with the Mac OS X environment and the compilation of C programs from the command line. You do not need previous experience with CUDA or experience with parallel computation.

2. Prerequisites

2.1. CUDA-capable GPU

To verify that your system is CUDA-capable, under the Apple menu select About This Mac , click the More Info … button, and then select Graphics/Displays under the Hardware list. There you will find the vendor name and model of your graphics card. If it is an NVIDIA card that is listed on the CUDA-supported GPUs page, your GPU is CUDA-capable.

The Release Notes for the CUDA Toolkit also contain a list of supported products.

2.2. Mac OS X Version

The CUDA Development Tools require an Intel-based Mac running Mac OSX v. 10.13. To check which version you have, go to the Apple menu on the desktop and select About This Mac .

Xcode Version

A supported version of Xcode must be installed on your system. The list of supported Xcode versions can be found in the System Requirements section. The latest version of Xcode can be installed from the Mac App Store.

Older versions of Xcode can be downloaded from the Apple Developer Download Page. Once downloaded, the Xcode.app folder should be copied to a version-specific folder within /Applications . For example, Xcode 6.2 could be copied to /Applications/Xcode_6.2.app .

Command-Line Tools

The CUDA Toolkit requires that the native command-line tools are already installed on the system. Xcode must be installed before these command-line tools can be installed.

Note: It is recommended to re-run the above command if Xcode is upgraded, or an older version of Xcode is selected.

3. Installation

Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.

3.1. Download

Once you have verified that you have a supported NVIDIA GPU, a supported version the MAC OS, and clang, you need to download the NVIDIA CUDA Toolkit.

The download can be verified by comparing the posted MD5 checksum with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.

3.2. Install

Use the following procedure to successfully install the CUDA driver and the CUDA toolkit. The CUDA driver and the CUDA toolkit must be installed for CUDA to function. If you have not installed a stand-alone driver, install the driver provided with the CUDA Toolkit.

: Specifies a package to install. Can be used multiple times. Options are «cuda-toolkit», «cuda-samples», and «cuda-driver».
—log-file=

: Specify a file to log the installation to. Default is /var/log/cuda_installer.log.

In order to modify, compile, and run the samples, the samples must also be installed with write permissions. A convenience installation script is prov >cuda-install-samples- 10.2 .sh . This script is installed with the cuda-samples- 10 — 2 package.

3.3. Uninstall

The CUDA Driver, Toolkit and Samples can be uninstalled by executing the uninstall script provided with each package:

Table 2. Mac Uninstall Script Locations

Package Location
CUDA Driver /usr/local/bin/uninstall_cuda_drv.pl
CUDA Toolkit /Developer/NV >10.2 /bin/uninstall_cuda_ 10.2 .pl
CUDA Samples /Developer/NV >10.2 /bin/uninstall_cuda_ 10.2 .pl

All packages which share an uninstall script will be uninstalled unless the —manifest= flag is used. Uninstall manifest files are located in the same directory as the uninstall script, and have filenames matching .

4. Verification

Before continuing, it is important to verify that the CUDA toolkit can find and communicate correctly with the CUDA-capable hardware. To do this, you need to compile and run some of the included sample programs.

4.1. Driver

4.2. Compiler

The installation of the compiler is first checked by running nvcc -V in a terminal window. The nvcc command runs the compiler driver that compiles CUDA programs. It calls the host compiler for C code and the NVIDIA PTX compiler for the CUDA code.

4.3. Runtime

After compilation, go to bin/x86_64/darwin/release and run deviceQuery . If the CUDA software is installed and configured correctly, the output for deviceQuery should look similar to that shown in Figure 1.

Note that the parameters for your CUDA device will vary. The key lines are the first and second ones that confirm a device was found and what model it is. Also, the next-to-last line, as indicated, should show that the test passed.

Running the bandwidthTest sample ensures that the system and the CUDA-capable device are able to communicate correctly. Its output is shown in Figure 2

Note that the measurements for your CUDA-capable device description will vary from system to system. The important point is that you obtain measurements, and that the second-to-last line (in Figure 2) confirms that all necessary tests passed.

Should the tests not pass, make sure you have a CUDA-capable NVIDIA GPU on your system and make sure it is properly installed.

If you run into difficulties with the link step (such as libraries not being found), consult the Release Notes found in the doc folder in the CUDA Samples directory.

To see a graphical representation of what CUDA can do, run the particles executable.

5. Additional Cons >

Now that you have CUDA-capable hardware and the NVIDIA CUDA Toolkit installed, you can examine and enjoy the numerous included programs. To begin using CUDA to accelerate the performance of your own applications, consult the CUDA C++ Programming Guide.

A number of helpful development tools are included in the CUDA Toolkit to assist you as you develop your CUDA programs, such as NVIDIA В® Nsightв„ў Eclipse Edition, NVIDIA Visual Profiler, cuda-gdb, and cuda-memcheck.

For technical support on programming questions, consult and participate in the Developer Forums.

Notices

Notice

ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, «MATERIALS») ARE BEING PROVIDED «AS IS.» NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.

Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication of otherwise under any patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. NVIDIA Corporation products are not authorized as critical components in life support devices or systems without express written approval of NVIDIA Corporation.

Trademarks

NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.

Copyright

В© 2009 — 2019 NVIDIA Corporation. All rights reserved.

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Apple и NV >10

Apple не привыкать ссориться с поставщиками. Известно несколько случаев, когда отношения между компанией и её партнёрами ухудшались настолько, что в Купертино были вынуждены искать альтернативного поставщика или даже начать разрабатывать нужные запчасти или ПО самостоятельно. Иногда это шло Apple на пользу, позволяя ей начать развиваться более активно, как в случае с Apple Maps или собственными процессорами A-серии, которые устанавливаются в мобильные устройства компании и по сей день являются самыми лучшими. Но, к сожалению, чаще всего, как в случаях с Qualcomm или NVIDIA конфликт выходил Apple и — самое главное — её пользователям боком.

Поддержка CUDA на macOS

NVIDIA прекращает поддержку архитектуры CUDA для macOS, начиная с версии 10.2, которая станет последней совместимой сборкой, говорится на сайте компании. Получается, что все будущие релизы CUDA, которые будут выходить после 10.2, не будут совместимы с операционной системой Apple, по сути, лишая изрядную долю профессиональных пользователей, а также пользователей хакинтошей возможности работать с дискретной графикой NVIDIA. Поэтому единственными ускорителями, которые будут актуальны на маках, отныне будут только решения AMD.

CUDA – программно-аппаратная архитектура параллельных вычислений, которая позволяет существенно увеличить вычислительный потенциал ряда функций при условии использования графических процессоров NVIDIA. Благодаря своей эффективности CUDA широко используется в таких областях, как астрофизика, вычислительная биология и химия, моделирование динамики жидкостей, электромагнитных воздействий, компьютерная томография, сейсмоанализ, а также всевозможных графических редакторах и даже играх.

Несмотря на то что для многих ни сам термин CUDA, ни его предназначение до сих пор остаются неизвестными, факт того, что NVIDIA отказывается от поддержки macOS, говорит о многом. Скорее всего, это терминальная стадия негативных отношений между двумя компаниями, восстановить которые будет весьма проблематично. Ведь если скандал с участием Qualcomm был довольно скоротечным и, по сути, строился на стремлении одной стороны продемонстрировать свою силу другой, то в случае с NVIDIA дела обстояли несколько иначе.

Почему графики NVIDIA нет на Mac

Конфликт между Apple и NVIDIA начался ещё десять лет назад, когда Apple лишилась большого количества заказов на MacBook Pro из-за неудачного графического решения. Тогда в Купертино приняли решение, что полагаться на одного поставщика ускорителей, пусть и имеющего отличную репутацию в отрасли, будет нерационально, и обратили внимание на AMD. С тех пор, несмотря на то, что решения последней уступали решениям NVIDIA, отношения между Apple и AMD шли в гору. При этом сама Apple не спешила отказываться от поддержки графики NVIDIA, позволяя владельцам фирменных компьютеров менять один ускоритель на другой по своему желанию.

Читайте также: Скоро мы можем увидеть 14-дюймовый MacBook Pro

Однако то, что произошло теперь, выглядит как настоящий разрыв и прекращение сотрудничества. Как это отразится на Apple, сказать сложно. С одной стороны, в Купертино и так уже давно не используют графику от NVIDIA в своих компьютерах, а значит, обеспечивать их полную работоспособность в компании и не обязаны вовсе. Но, с другой стороны, это может отпугнуть некоторых пользователей — особенно профессиональных, — для которых важна и графика от NVIDIA, и ускоритель CUDA. А если так пойдёт и дальше, Apple рискует потерять часть платёжеспособной аудитории, что для неё очень нежелательно.

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Установка cuda for mac os

This cuDNN 7.6.5 Installation Guide provides step-by-step instructions on how to install and check for correct operation of cuDNN on Linux, Mac OS X, and Microsoft Windows systems.

For previously released cuDNN installation documentation, see cuDNN Archives.

1. Overview

The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN is part of the NVIDIA Deep Learning SDK.

Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN accelerates widely used deep learning frameworks and is freely available to members of the NVIDIA Developer Program.

2. Installing cuDNN On Linux

2.1. Prerequisites

A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see: CUDA GPUs.

2.1.1. Installing NV >

Install up-to-date NVIDIA graphics drivers on your Linux system.

  1. Go to: NVIDIA download drivers
  2. Select the GPU and OS version from the drop down menus.
  3. Download and install NV >ADDITIONAL INFORMATION tab for step-by-step instructions for installing a driver.
  4. Restart your system to ensure the graphics driver takes effect.

2.1.2. Installing The CUDA Toolkit For Linux

Refer to the following instructions for installing CUDA on Linux, including the CUDA driver and toolkit: NVIDIA CUDA Installation Guide for Linux.

2.2. Downloading cuDNN For Linux

In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

  1. Go to: NVIDIA cuDNN home page.
  2. Click Download .
  3. Complete the short survey and click Submit .
  4. Accept the Terms and Conditions. A list of available download versions of cuDNN displays.
  5. Select the cuDNN version you want to install. A list of available resources displays.

2.3. Installing cuDNN On Linux

The following steps describe how to build a cuDNN dependent program. Choose the installation method that meets your environment needs. For example, the tar file installation applies to all Linux platforms, and the debian installation package applies to Ubuntu 14.04,16.04, and 18.04.

2.3.1. Installing From A Tar File

  1. Navigate to your directory containing the cuDNN Tar file.
  2. Unzip the cuDNN package.
  3. Copy the following files into the CUDA Toolkit directory, and change the file permissions.

2.3.2. Installing From A Debian File

  1. Navigate to your directory containing cuDNN Debian file.
  2. Install the runtime library, for example:
  3. Install the developer library, for example:
  4. Install the code samples and the cuDNN Library User Guide, for example:

2.3.3. Installing From An RPM File

  1. Download the rpm package libcudnn*.rpm to the local path.
  2. Install the rpm package from the local path. This will install the cuDNN libraries.

2.4. Verifying The cuDNN Install On Linux

To verify that cuDNN is installed and is running properly, compile the mnistCUDNN sample located in the /usr/src/cudnn_samples_v7 directory in the debian file.

  1. Copy the cuDNN sample to a writable path.
  2. Go to the writable path.
  3. Compile the mnistCUDNN sample.
  4. Run the mnistCUDNN sample.

2.5. Upgrading From v6 To v7

cuDNN v7 can coexist with previous versions of cuDNN, such as v5 or v6.

2.6. Troubleshooting

Join the NVIDIA Developer Forum to post questions and follow discussions.

3. Installing cuDNN On Mac OS X

3.1. Prerequisites

3.1.1. Installing NV >

Install up-to-date NVIDIA graphics drivers on your Mac OS X system.

  1. Go to: NVIDIA download drivers
  2. Select the GPU and OS version from the drop down menus.
  3. Download and install NV >ADDITIONAL INFORMATION tab for step-by-step instructions for installing a driver.
  4. Restart your system to ensure the graphics driver takes effect.

3.1.2. Installing The CUDA Toolkit For Mac OS X

Refer to the following instructions for installing CUDA on Mac OS X, including the CUDA driver and toolkit: NVIDIA CUDA Installation Guide for Mac OS X.

3.2. Downloading cuDNN For Mac OS X

In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

  1. Go to: NVIDIA cuDNN home page.
  2. Click Download .
  3. Complete the short survey and click Submit .
  4. Accept the Terms and Conditions. A list of available download versions of cuDNN displays.
  5. Select the cuDNN version to want to install. A list of available resources displays.
  6. Extract the cuDNN archive to a directory of your choice.

4. Installing cuDNN On Windows

4.1. Prerequisites

4.1.1. Installing NV >

  1. Go to: NVIDIA download drivers
  2. Select the GPU and OS version from the drop down menus.
  3. Download and install NV >ADDITIONAL INFORMATION tab for step-by-step instructions for installing a driver.
  4. Restart your system to ensure the graphics driver takes effect.

4.1.2. Installing The CUDA Toolkit For Windows

Refer to the following instructions for installing CUDA on Windows, including the CUDA driver and toolkit: NVIDIA CUDA Installation Guide for Windows.

4.2. Downloading cuDNN For Windows

In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

  1. Go to: NVIDIA cuDNN home page.
  2. Click Download .
  3. Complete the short survey and click Submit .
  4. Accept the Terms and Conditions. A list of available download versions of cuDNN displays.
  5. Select the cuDNN version to want to install. A list of available resources displays.
  6. Extract the cuDNN archive to a directory of your choice.

4.3. Installing cuDNN On Windows

  1. Navigate to your directory containing cuDNN.
  2. Unzip the cuDNN package. or
  3. Copy the following files into the CUDA Toolkit directory.
    1. Copy \cuda\bin\cudnn64_7.6.5.32.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin .
    2. Copy \cuda\ include\cudnn.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include .
    3. Copy \cuda\lib\x64\cudnn.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\lib\x64 .
  4. Set the following environment variables to point to where cuDNN is located. To access the value of the $(CUDA_PATH) environment variable, perform the following steps:
    1. Open a command prompt from the Start menu.
    2. Type Run and hit Enter .
    3. Issue the control sysdm.cpl command.
    4. Select the Advanced tab at the top of the window.
    5. Click Environment Variables at the bottom of the window.
    6. Ensure the following values are set:
  5. Include cudnn.lib in your Visual Studio project.
    1. Open the Visual Studio project and right-click on the project name.
    2. Click Linker > Input > Additional Dependencies .
    3. Add cudnn.lib and click OK .

4.4. Upgrading From v6 To v7

cuDNN v7 can coexist with previous versions of cuDNN, such as v5 or v6.

4.5. Troubleshooting

Join the NVIDIA Developer Forum to post questions and follow discussions.

5. Cross-compiling cuDNN Samples

5.1. NV >

Follow the below steps to cross-compile cuDNN samples on NVIDIA DRIVE OS Linux.

5.1.1. Installing The CUDA Toolkit For DRIVE OS

  1. Download the CUDA for Ubuntu package: cuda*ubuntu*_amd64.deb
  2. Download the cross compile package: cuda*-cross-aarch64*_all.deb
  3. Execute the following commands:

5.1.2. Installing cuDNN For DRIVE OS

  1. Download the cuDNN Ubuntu package for your preferred CUDA Toolkit version: *libcudnn7-cross-aarch64_*.deb
  2. Download the cross compile package: libcudnn7-devel-cross-aarch64_*.deb
  3. Execute the following commands:

5.1.3. Cross-compiling cuDNN Samples For DRIVE OS

Copy the cudnn_samples_v7 directory to your home directory:

5.2. QNX

Follow the below steps to cross-compile cuDNN samples on QNX:

5.2.1. Installing The CUDA Toolkit For QNX

  1. Download the CUDA for Ubuntu package: cuda*ubuntu*_amd64.deb
  2. Download the cross compile package: cuda*-cross-aarch64*_all.deb
  3. Execute the following commands:

5.2.2. Installing cuDNN For QNX

  1. Download the cuDNN Ubuntu package for your preferred CUDA Toolkit version: *libcudnn7-cross-aarch64_*.deb
  2. Download the cross compile package: libcudnn7-devel-cross-aarch64_*.deb
  3. Execute the following commands:

5.2.3. Set The Environment Variables

5.2.4. Cross-compiling cuDNN Samples For QNX

Copy the cudnn_samples_v7 directory to your home directory:

6. Package Manager Installation

When using RPM or Deb, the downloaded archive is a repository. This repository does not contain the actual installation package, and only contains the information about where to find the actual installation packages online. The package manager uses this information to download the installation packages and install them.

If the actual installation packages are available online, then the package manager will automatically download them and install them. Otherwise, the package manager installs a local repository containing the installation packages on the system.

Whether the repository is available online or installed locally, the installation procedure is identical and made of multiple steps. See below.

6.1. Network Installation

6.1.1. Ubuntu

The above will install the repository containing information about appropriate cuDNN libraries online. Execute the steps below to install cuDNN library.

Execute the steps below to install cuDNN library:

6.1.2. RHEL

  1. Download and install the repository:
  2. Install the cuDNN package:
    1. For the latest version:
    2. For other versions:

    Notices

    Notice

    THE INFORMATION IN THIS GUIDE AND ALL OTHER INFORMATION CONTAINED IN NVIDIA DOCUMENTATION REFERENCED IN THIS GUIDE IS PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE INFORMATION FOR THE PRODUCT, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIA’s aggregate and cumulative liability towards customer for the product described in this guide shall be limited in accordance with the NVIDIA terms and conditions of sale for the product.

    THE NVIDIA PRODUCT DESCRIBED IN THIS GUIDE IS NOT FAULT TOLERANT AND IS NOT DESIGNED, MANUFACTURED OR INTENDED FOR USE IN CONNECTION WITH THE DESIGN, CONSTRUCTION, MAINTENANCE, AND/OR OPERATION OF ANY SYSTEM WHERE THE USE OR A FAILURE OF SUCH SYSTEM COULD RESULT IN A SITUATION THAT THREATENS THE SAFETY OF HUMAN LIFE OR SEVERE PHYSICAL HARM OR PROPERTY DAMAGE (INCLUDING, FOR EXAMPLE, USE IN CONNECTION WITH ANY NUCLEAR, AVIONICS, LIFE SUPPORT OR OTHER LIFE CRITICAL APPLICATION). NVIDIA EXPRESSLY DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR SUCH HIGH RISK USES. NVIDIA SHALL NOT BE LIABLE TO CUSTOMER OR ANY THIRD PARTY, IN WHOLE OR IN PART, FOR ANY CLAIMS OR DAMAGES ARISING FROM SUCH HIGH RISK USES.

    NVIDIA makes no representation or warranty that the product described in this guide will be suitable for any specified use without further testing or modification. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to ensure the product is suitable and fit for the application planned by customer and to do the necessary testing for the application in order to avoid a default of the application or the product. Weaknesses in customer’s product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this guide. NVIDIA does not accept any liability related to any default, damage, costs or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this guide, or (ii) customer product designs.

    Other than the right for customer to use the information in this guide with the product, no other license, either expressed or implied, is hereby granted by NVIDIA under this guide. Reproduction of information in this guide is permissible only if reproduction is approved by NVIDIA in writing, is reproduced without alteration, and is accompanied by all associated conditions, limitations, and notices.

    Trademarks

    NVIDIA, the NVIDIA logo, and cuBLAS, CUDA, cuDNN, DALI, DIGITS, DGX, DGX-1, DGX-2, DGX Station, DLProf, Jetson, Kepler, Maxwell, NCCL, Nsight Compute, Nsight Systems, NvCaffe, PerfWorks, Pascal, SDK Manager, Tegra, TensorRT, TensorRT Inference Server, Tesla, TF-TRT, and Volta are trademarks and/or registered trademarks of NVIDIA Corporation in the United States and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.

    Copyright

    В© 2019 NVIDIA Corporation. All rights reserved.

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