Cross Compile For Mac Os On Linux

Clang/LLVM is a cross compiler by default and is now available on nearly every Linux distribution, so we just need a proper port of the cctools/ld64 and the macOS SDK. OSXCross includes a collection of scripts for preparing the SDK and building the cctools/ld64. 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. Cross compiling Rust on Mac OS for an ARM Linux router. Wanting to compile a small program I'd written in Rust to run on my home router, I found this guide to cross compilation of Rust code. The router is a Netgear R7000 with an ARM processor, running FreshTomato, a distribution of Linux for ARM and MIPS architecture consumer routers. @jcoffland No, the other way around. It's for building OS X applications on Linux. 'Cross-Compiling on Linux for Mac OS X 10.3 - 10.5' (emphasis added). Note that this answer is very old by now (almost 7 years old, yikes!) and the later answers below have more up to date information. Cross-compiling on windows for linux free download. Mpv-i686-cross-compiling-MinGW32-Doc This project aims at setting up a MinGW-w64 Toolchain on a pure Linux-32 Bits system to cross-compil. Mac OS X or Linux. Tor Browser enables you to use Tor on Windows.

1. Cross Compile For Osx On Linux
2. Cross Compile For Mac Os On Linux Virtualbox
3. Cross Compile For Mac Os On Linux Command
4. Cross Compile For Mac Os On Linux Mint
5. Cross Compile For Macos On Linux

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

• 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.

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.

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.

• your CUDA directory path is referred to as /usr/local/cuda/
• your cuDNN download path is referred to as

2.3.1. Installing From A Tar File

1. Navigate to your 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

1. Navigate to your 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

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.

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:

2.5. Upgrading From v7 To v8

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

• 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

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.

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

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.

Cross Compile For Osx On Linux

• Your CUDA directory path is referred to as C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.x
• Your cuDNN directory path is referred to as

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 cudabincudnn*.dll to C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.xbin.
   2. Copy cudaincludecudnn*.h to C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.xinclude.
   3. Copy 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 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

4.1. NVIDIA DRIVE OS Linux

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:

Cross Compile For Mac Os On Linux Virtualbox

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

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.

Notice

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.

Cross Compile For Mac Os On Linux Command

NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.

Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.

NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer ('Terms of Sale'). NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. No contractual obligations are formed either directly or indirectly by this document.

NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. Mathlab for mac. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customer's own risk.

NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer's sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform 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 document. NVIDIA accepts no 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 document or (ii) customer product designs.

No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA.

Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices.

THIS DOCUMENT AND 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. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIA's aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product.

VESA DisplayPort

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.

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

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.

Cross Compile For Osx On Linux

• Your CUDA directory path is referred to as C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.x
• Your cuDNN directory path is referred to as

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 cudabincudnn*.dll to C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.xbin.
   2. Copy cudaincludecudnn*.h to C:Program FilesNVIDIA GPU Computing ToolkitCUDAvx.xinclude.
   3. Copy 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 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

4.1. NVIDIA DRIVE OS Linux

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:

Cross Compile For Mac Os On Linux Virtualbox

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

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.

Notice

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.

Cross Compile For Mac Os On Linux Command

NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.

Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.

NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer ('Terms of Sale'). NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. No contractual obligations are formed either directly or indirectly by this document.

NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. Mathlab for mac. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customer's own risk.

NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer's sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform 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 document. NVIDIA accepts no 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 document or (ii) customer product designs.

No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA.

Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices.

THIS DOCUMENT AND 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. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIA's aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product.

VESA DisplayPort

DisplayPort and DisplayPort Compliance Logo, DisplayPort Compliance Logo for Dual-mode Sources, and DisplayPort Compliance Logo for Active Cables are trademarks owned by the Video Electronics Standards Association in the United States and other countries.

HDMI

HDMI, the HDMI logo, and High-Definition Multimedia Interface are trademarks or registered trademarks of HDMI Licensing LLC.

ARM

ARM, AMBA and ARM Powered are registered trademarks of ARM Limited. Cortex, MPCore and Mali are trademarks of ARM Limited. All other brands or product names are the property of their respective holders. 'ARM' is used to represent ARM Holdings plc; its operating company ARM Limited; and the regional subsidiaries ARM Inc.; ARM KK; ARM Korea Limited.; ARM Taiwan Limited; ARM France SAS; ARM Consulting (Shanghai) Co. Ltd.; ARM Germany GmbH; ARM Embedded Technologies Pvt. Ltd.; ARM Norway, AS and ARM Sweden AB.

OpenCL

OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.

Trademarks

NVIDIA, the NVIDIA logo, and cuBLAS, CUDA, CUDA Toolkit, cuDNN, DALI, DIGITS, DGX, DGX-1, DGX-2, DGX Station, DLProf, GPU, JetPack, Jetson, Kepler, Maxwell, NCCL, Nsight Compute, Nsight Systems, NVCaffe, NVIDIA Ampere GPU architecture, NVIDIA Deep Learning SDK, NVIDIA Developer Program, NVIDIA GPU Cloud, NVLink, NVSHMEM, PerfWorks, Pascal, SDK Manager, T4, Tegra, TensorRT, TensorRT Inference Server, Tesla, TF-TRT, Triton Inference Server, Turing, 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.

Cross Compile For Mac Os On Linux Mint

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Cross Compile For Macos On Linux

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