Cross Compile For Mac Os On Linux

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  1. Linux Mac Os
  2. Cross Compile For Mac Os On Linux Command
  3. Cross Compile Linux Kernel

Scratchbox is a toolkit for Linux cross-compilation to ARM and x86 targets; Grand Unified Builder (GUB) for Linux to cross-compile multiple architectures e.g.:Win32/Mac OS/FreeBSD/Linux used by GNU LilyPond; Crosstool is a helpful toolchain of scripts, which create a Linux cross-compile environment for the desired architecture, including. @IsAnton said in Cross compile Qt from Linux to Macos: And in Linux no such tool as xcodebuild, does it means that I can't build Qt from Lunux for Macos? Yes, Apple is the odd one out. You need a Mac to compile for iOS and/or MacOS. As far as I know, no way around it. Eclipse C Compiler. With Eclipse you get advance functionality for programming in C, C on an. Using these two, we can compile a program in a Mac that will run on that Linux like this: crystal build yourprogram.cr -cross-compile -target 'x8664-unknown-linux-gnu' This will generate a.o ( Object file ) and will print a line with a command to execute on the system we are trying to cross-compile to. We link to the system libusb on Mac OS X and Linux. On Windows depending on the bit depth we can link to libusb-1.0-32.dll.a or libusb-1.0-64.dll.a.Remember that.a-file can be renamed, but the application will still depend on libusb-1.0.dll.We take parameters for libusb through the system utility pkgconfig in Linux. We include all necessary system libraries and icons in addition to libusb.

Abstract

This cuDNN 8.0.4 Installation Guide provides step-by-step instructions on how to install and check for correct operation of cuDNN on Linux 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

Ensure you meet the following requirements before you install cuDNN.

  • For the latest compatibility software versions of the OS, CUDA, the CUDA driver, and the NVIDIA hardware, see the cuDNN Support Matrix.

2.1.1. Installing NVIDIA Graphics Drivers

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

Procedure

  1. Go to: NVIDIA download drivers
  2. Select the GPU and OS version from the drop-down menus.
  3. Download and install the NVIDIA graphics driver as indicated on that web page. For more information, select the 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.

Procedure

  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 16.04 and 18.04.

In the following sections:
  • your CUDA directory path is referred to as /usr/local/cuda/
  • your cuDNN download path is referred to as <cudnnpath>

2.3.1. Installing From A Tar File

Before issuing the following commands, you'll need to replace x.x and v8.x.x.x with your specific CUDA version and cuDNN version and package date.
  1. Navigate to your <cudnnpath> directory containing the cuDNN Tar file.
  2. Unzip the cuDNN package.

    or

  3. Copy the following files into the CUDA Toolkit directory, and change the file permissions.

2.3.2. Installing From A Debian File

Cross compile for mac os on linux mint
Before issuing the following commands, you'll need to replace x.x and 8.x.x.x with your specific CUDA version and cuDNN version and package date.

Procedure

  1. Navigate to your <cudnnpath> directory containing the cuDNN Debian file.
  2. Install the runtime library, for example:

    or

  3. Install the developer library, for example:

    or

  4. Install the code samples and the cuDNN library documentation, for example:

    or

2.3.3. Installing From An RPM File

Procedure

  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.

    or

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_v8 directory in the Debian file.

Procedure

  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.
    If cuDNN is properly installed and running on your Linux system, you will see a message similar to the following:

Linux Mac Os

2.5. Upgrading From v7 To v8

Since version 8 can coexist with previous versions of cuDNN, if the user has an older version of cuDNN such as v6 or v7, installing version 8 will not automatically delete an older revision. Therefore, if the user wants the latest version, install cuDNN version 8 by following the installation steps.
To upgrade from v7 to v8 for RHEL, run:

To switch between v7 and v8 installations, issue sudo update-alternatives --config libcudnn and choose the appropriate cuDNN version.

2.6. Troubleshooting

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

3. Installing cuDNN On Windows

3.1. Prerequisites

Ensure you meet the following requirements before you install cuDNN.
  • For the latest compatibility software versions of the OS, CUDA, the CUDA driver, and the NVIDIA hardware, see the cuDNN Support Matrix.

3.1.1. Installing NVIDIA Graphic Drivers

Install up-to-date NVIDIA graphics drivers on your Windows system.
  1. Go to: NVIDIA download drivers
  2. Select the GPU and OS version from the drop-down menus.
  3. Download and install the NVIDIA driver as indicated on that web page. For more information, select the 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 Windows

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

3.2. Downloading cuDNN For Windows

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

Procedure

  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.

3.3. Installing cuDNN On Windows

The following steps describe how to build a cuDNN dependent program.

Before issuing the following commands, you'll need to replace x.x and 8.x.x.x with your specific CUDA version and cuDNN version and package date.

In the following sections the CUDA v9.0 is used as example:
  • Your CUDA directory path is referred to as C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.x
  • Your cuDNN directory path is referred to as <installpath>
  1. Navigate to your <installpath> directory containing cuDNN.
  2. Unzip the cuDNN package. or
  3. Copy the following files into the CUDA Toolkit directory.
    1. Copy <installpath>cudabincudnn*.dll to C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.xbin.
    2. Copy <installpath>cudaincludecudnn*.h to C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.xinclude.
    3. Copy <installpath>cudalibx64cudnn*.lib to C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.xlibx64.
  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.

3.4. Upgrading From v7 To v8

Navigate to your <installpath> directory containing cuDNN and delete the old cuDNNlib and header files. Reinstall the latest cuDNN version by following the steps in Installing cuDNN On Windows.

3.5. Troubleshooting

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

4. Cross-compiling cuDNN Samples

This section describes how to cross-compile cuDNN samples.

4.1. NVIDIA DRIVE OS Linux

Cross compile linux kernel module

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

4.1.1. Installing The For DRIVE OS

Before issuing the following commands, you'll need to replace x-x with your specific version.

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

4.1.2. Installing For DRIVE OS

  1. Download the Ubuntu package for your preferred version: *libcudnn8-cross-aarch64_*.deb
  2. Download the cross compile package: libcudnn8-dev-cross-aarch64_*.deb
  3. Execute the following commands:

4.1.3. Cross-compiling Samples For DRIVE OS

Copy the cudnn_samples_v8 directory to your home directory:

4.2. QNX

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

4.2.1. Installing The For QNX

Before issuing the following commands, you'll need to replace x-x with your specific version.

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

4.2.2. Installing For QNX

  1. Download the Ubuntu package for your preferred version: *libcudnn8-cross-aarch64_*.deb
  2. Download the cross compile package: libcudnn8-devel-cross-aarch64_*.deb
  3. Execute the following commands:

4.2.3. Set The Environment Variables

To set the environment variables, issue the following commands:

4.2.4. Cross-compiling Samples For QNX

Copy the cudnn_samples_v8 directory to your home directory:

Before issuing the following commands, you'll need to replace 8.x.x with your specific version.

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Cross Compile For Mac Os On Linux Command

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