How To Compile A Driver For Mac

This (compiling) is something that folks new to Linux can find difficult, just as a heads up. Instructions on how to do this can be found here, although I'm not sure if it will work for Kali. I did find a Kali thread concerning this (on Ubuntu's forum] here, although I doubt that it will be of any help. @Smith@@ and Yum this is what you need to put in your config.plist - it this is a modified EDID (detailed timing section) but has the same product/vendor.

1. How To Compile C Code
2. How To Compile C Files
3. How To Compile Java Code

Advertisement Along with The sheer number of different ways in which Linux can be run is astounding, as there are plenty of choices to go around. While there are plenty of distributions which rely on either the.deb., compiling your own Linux kernel is one of those things that’s considered very geeky and technical. We’ve already Linux systems are so ridiculously flexible and configurable, it's downright crazy. A huge part of why Linux is so flexible is because of its modular structure. Every single system component and program is split up. Why you might want to do this. Before we walk you through the steps, here’s a brief overview on some important concepts.

What Is a Kernel? In short, it’s a vital part of your operating system, if not the most important. An operating system is responsible for letting your programs function, by allowing them access to your hardware.

However, it’s the There is only one de facto thing that Linux distributions have in common: the Linux kernel. But while it's often talked about, a lot of people don't really know exactly what it does. Which actually carries all these jobs out.

The Linux kernel is special in that it can be tailor made to fit the hardware you own. Contrast this with the Windows NT kernel, which will be generally the same across every computer.

To do this, the kernel must go through a process called compiling. What Is Compiling?

Generally, programs are written in a human readable format. This makes it easier for people to create software. For example, the Linux kernel is written mainly in a programming language called C. However, computers can’t understand this. They only recognize a complicated language called machine code.

How To Compile C Code

To do so, the lines of text need to be using a special piece of software called a compiler. Different compilers are used to convert different programming languages. For example, GCC is a compiler which translates C code into machine code (among others). But that’s not all they do — compilers can also optimize the translated code for specific machines. Along with this, they can also remove unwanted features from programs whilst doing so.

Usually, this process is already done for lots of software. Instead, people download the converted binary instructions, which work out of the box. For example, the Using the 'wrong' browser can lead to a lot of unnecessary headaches, wasted productivity, and even lost data. So which browser is the best for your Linux computer? Let's find out. You’re running right now is already compiled. This is because compiling something can be a little bit complicated and time consuming.

Think of it like translating a large book into a foreign language. In short, compiling the kernel means converting its code into something that will actually run on your computer. Obtaining Kernel Sources You can’t make a fire without fuel. Likewise, you can’t compile programs if you don’t have the code for it in the first place. The method is generally the same for most Linux operating systems with a few exceptions. Many of them supply their own custom kernel sources. You should try and use that if you can.

For example, if you’re using a Debian-based operating system such as Ubuntu, you should install the sources using these commands: sudo apt-get update sudo apt-get install linux-source You can also install different versions of this package (e.g. Linux-source-4.8.0) if you like. You’ll find the installed files in the /usr/src/ directory, so navigate there, and view the contents. Cd /usr/src/ ls You’ll see a file that ends with.tar.bz2.

This is the source you’ll be using. Extract it with this command: sudo tar xjvf linux-source-4.4.0.tar.bz2 Make sure you replace the file with the correct name, of course. The file will be quite big, so the process will take a little while! Alternative Method If you like, you may obtain your sources from the. I recommend this only if your Linux operating system doesn’t provide them.

Select the version you want and download away. If you go through this route, you should download it to an empty folder.

This way, if you want to compile more kernels in the future, you’ll have a nice folder to keep them organized in. Prerequisites Before you proceed, you must install some programs that will let you customize and compile it. On Debian-based systems, you need the build-essential package, among others: sudo apt-get install build-essential gcc libncurses5-dev libssl-dev The libncurses5-dev package will make it easier to configure the kernel, so make sure to install it.

Other Linux operating systems will have a similar program, but this may be under a different name. For example, if you’re using Fedora isn't as well known as Ubuntu, and has a reputation for being hard to use. But if this is true, why do so many people continue using Fedora?, it’s called ncurses. If it’s not installed already, you’ll also need the GNU Compiler Collection (GCC). This is the tool responsible for translating the raw source code into something understandable to computers.

That is to say, compiling. Having installed these programs, open up your terminal so that you’re inside the extracted Linux source folder.

For example, if you used the linux-source method, you’d do this: cd /usr/src/linux-source-4.4.0 Kernel Configuration File Before compiling the kernel, you need to tell GCC how you want it done. You do this using a command line tool called make. Type in this command: sudo make localmodconfig This creates a file called.config, which tells GCC what to compile from the kernel sources. The make localmodconfig command detects currently running kernel components, and marks them for compilation. If the kernel is inside a folder you own, you won’t need sudo for it to work. You may come across some messages like the one below. Just press the Enter key to skip them — they’re usually just new kernel features.

However, localmodconfig is not perfect! If you’re not currently using some parts of your computer, it may not detect all the things it supports. As such, you need to enable them manually. Alternatively, you can skip this step entirely and use the next command instead. This will compile the new kernel with the same options as the one you’re currently using. If you’d prefer that, but used the localmodconifg command before, do this: sudo make clean This will give you a clean build.

You should also use this command if you’ve compiled a kernel with these sources before. Further Configuration Inside the kernel source folder, there’s a file called.config. This is what GCC will use to choose what to compile. Instead of editing it manually (not recommended), you’ll instead use a few terminal tools.

This will make selecting compile options much easier. Having done this, fine tune it further: sudo make nconfig You’ll see a colorful menu pop up. This is the kernel configuration menu. To navigate around, use the arrow keys. You can press the right arrow key to expand entries with a — sign next to them. Navigate out of these sub-menus by pressing the left arrow key. You can toggle menu entries that have the or sign next to them by pressing the space bar.

This will cycle through the different menu options. If you see a. or M inside, that specific kernel component will be compiled. The only difference between them is that the M option will be loaded when it’s needed. This can be useful if you’re compiling a driver for example, that won’t be used often. If you want to know more about what a specific switch does, press F2 over it. You’ll see a helpful description of what you’re compiling.

Once you’re all done, press the F9 button to save and exit. Compiling & Installing the Kernel Now that you’ve created a custom make file, you’ll need to the kernel. Type in this command: sudo make -j$(nproc -all) You will not need sudo if you downloaded the kernel sources off the web. The second part of the command helps speed up the kernel compile time, by taking advantage of all your Every computer has a processor, whether it's a small efficiency pro or a large performance powerhouse, or else it wouldn't be able to function. Of course, the processor, also called the CPU or Central Processing. You may get rid of this, or change the number to something else (e.g.j2), if you want to use your computer for other tasks without too many hiccups.

However, this will also make the compiling slower! This process can and will take a very long time. The less you chose to compile, the shorter it will be. Even so, you’ll probably need to be patient.

Afterwards, type this command in to finish compiling the rest of the kernel: sudo make modulesinstall To actually use this kernel, you’ll need to enter this command: sudo make install This will automatically copy the kernel to your /boot folder and generate the appropriate files to make it work. Switching Kernels Using GRUB If you reboot after installing your new kernel, your system might not work!

This is usually due to an improperly compiled kernel (that is, not supporting your computer well enough). Case in point, my own laptop. Just in case this happens, I recommend so that it can switch back to older kernels. To do this, use this command: sudo nano /etc/default/grub Place a # sign in front of the GRUBHIDDENTIMEOUT and GRUBHIDDENTIMEOUTQUIET lines.

Afterwards, save these changes by running this command: sudo grub-mkconfig -o /boot/grub/grub.cfg Now if you reboot your computer, you’ll be able to switch to an older kernel if your new one doesn’t work. Simply go to the Advanced options menu item and select the kernel you want to boot. If everything works out fine, congratulations! You’ll be using the kernel you compiled by yourself.

Are you interested in compiling more kernels? Other programs? Do you find it worthwhile?

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Ubuntu sudo apt install python-dev python-pip # or python3-dev python3-pip mac OS Requires Xcode 8.3 or later. Install using the package manager: /usr/bin/ruby -e '$(curl -fsSL export PATH='/usr/local/bin:/usr/local/sbin:$PATH' brew install python@2 # or python (Python 3) Install the TensorFlow pip package dependencies (if using a virtual environment, omit the -user argument): pip install -U -user pip six numpy wheel mock pip install -U -user kerasapplications1.0.5 -no-deps pip install -U -user keraspreprocessing1.0.3 -no-deps The dependencies are listed in the file under REQUIREDPACKAGES. Install Bazel, the build tool used to compile TensorFlow. Add the location of the Bazel executable to your PATH environment variable.

Install GPU support (optional, Linux only) There is no GPU support for macOS. Read the guide to install the drivers and additional software required to run TensorFlow on a GPU. Note: It is easier to set up one of TensorFlow's GPU-enabled. Download the TensorFlow source code Use to clone the: git clone cd tensorflow The repo defaults to the master development branch. You can also checkout a to build: git checkout branchname # r1.9, r1.10, etc. To test your copy of the source tree, run the following test for versions r1.12 and before (this may take a while): bazel test -c opt - //tensorflow/.

For versions after r1.12 (like master), run the following: bazel test -c opt - //tensorflow/. Key Point: If you're having build problems on the latest development branch, try a release branch that is known to work. Configure the build Configure your system build by running the following at the root of your TensorFlow source tree:./configure This script prompts you for the location of TensorFlow dependencies and asks for additional build configuration options (compiler flags, for example). The following shows a sample run of./configure (your session may differ): View sample configuration session./configure You have bazel 0.15.0 installed. Please specify the location of python. Default is /usr/bin/python: /usr/bin/python2.7 Found possible Python library paths: /usr/local/lib/python2.7/dist-packages /usr/lib/python2.7/dist-packages Please input the desired Python library path to use.

Default is /usr/lib/python2.7/dist-packages Do you wish to build TensorFlow with jemalloc as malloc support? Y/n: jemalloc as malloc support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Google Cloud Platform support? Y/n: Google Cloud Platform support will be enabled for TensorFlow. Do you wish to build TensorFlow with Hadoop File System support? Y/n: Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon AWS Platform support? Y/n: Amazon AWS Platform support will be enabled for TensorFlow. Do you wish to build TensorFlow with Apache Kafka Platform support? Y/n: Apache Kafka Platform support will be enabled for TensorFlow. Do you wish to build TensorFlow with XLA JIT support?

y/N: No XLA JIT support will be enabled for TensorFlow. Do you wish to build TensorFlow with GDR support? y/N: No GDR support will be enabled for TensorFlow. Do you wish to build TensorFlow with VERBS support? y/N: No VERBS support will be enabled for TensorFlow. Do you wish to build TensorFlow with OpenCL SYCL support?

y/N: No OpenCL SYCL support will be enabled for TensorFlow. Do you wish to build TensorFlow with CUDA support? y/N: Y CUDA support will be enabled for TensorFlow. Please specify the CUDA SDK version you want to use.

Leave empty to default to CUDA 9.0: 9.0 Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. Default is /usr/local/cuda: Please specify the cuDNN version you want to use. Leave empty to default to cuDNN 7.0: 7.0 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. Default is /usr/local/cuda: Do you wish to build TensorFlow with TensorRT support? y/N: No TensorRT support will be enabled for TensorFlow.

Please specify the NCCL version you want to use. If NCLL 2.2 is not installed, then you can use version 1.3 that can be fetched automatically but it may have worse performance with multiple GPUs. Default is 2.2: 1.3 Please specify a list of comma-separated Cuda compute capabilities you want to build with. You can find the compute capability of your device at: Please note that each additional compute capability significantly increases your build time and binary size. Default is: 3.5,7.0 6.1 Do you want to use clang as CUDA compiler? y/N: nvcc will be used as CUDA compiler.

How To Compile C Files

Please specify which gcc should be used by nvcc as the host compiler. Default is /usr/bin/gcc: Do you wish to build TensorFlow with MPI support?

y/N: No MPI support will be enabled for TensorFlow. Please specify optimization flags to use during compilation when bazel option '-config=opt' is specified Default is -march=native: Would you like to interactively configure./WORKSPACE for Android builds? y/N: Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding '-config=' to your build command. See tools/bazel.rc for more details.config=mkl # Build with MKL support.config=monolithic # Config for mostly static monolithic build. Configuration finished Configuration options For, specify the versions of CUDA and cuDNN.

If your system has multiple versions of CUDA or cuDNN installed, explicitly set the version instead of relying on the default./configure creates symbolic links to your system's CUDA libraries—so if you update your CUDA library paths, this configuration step must be run again before building. For compilation optimization flags, the default ( -march=native) optimizes the generated code for your machine's CPU type. However, if building TensorFlow for a different CPU type, consider a more specific optimization flag. See the for examples. There are some preconfigured build configs available that can be added to the bazel build command, for example:.config=mkl —Support for the.config=monolithic —Configuration for a mostly static, monolithic build. Note: Starting with TensorFlow 1.6, binaries use AVX instructions which may not run on older CPUs.

How To Compile Java Code

Build the pip package Bazel build CPU-only Use bazel to make the TensorFlow package builder with CPU-only support: bazel build -config=opt //tensorflow/tools/pippackage:buildpippackage GPU support To make the TensorFlow package builder with GPU support: bazel build -config=opt -config=cuda //tensorflow/tools/pippackage:buildpippackage Bazel build options Building TensorFlow from source can use a lot of RAM. If your system is memory-constrained, limit Bazel's RAM usage with: -localresources 2048.5,1.0. The are built with GCC 4 and use the older ABI.

For GCC 5 and later, make your build compatible with the older ABI using: -cxxopt='-DGLIBCXXUSECXX11ABI=0'. ABI compatibility ensures that custom ops built against the official TensorFlow package continue to work with the GCC 5 built package. Build the package The bazel build command creates an executable named buildpippackage—this is the program that builds the pip package. For example, the following builds a.whl package in the /tmp/tensorflowpkg directory:./bazel-bin/tensorflow/tools/pippackage/buildpippackage /tmp/tensorflowpkg Although it is possible to build both CUDA and non-CUDA configurations under the same source tree, it's recommended to run bazel clean when switching between these two configurations in the same source tree.

Install the package The filename of the generated.whl file depends on the TensorFlow version and your platform. Use pip install to install the package, for example: pip install /tmp/tensorflowpkg/tensorflow- version- tags.whl Success: TensorFlow is now installed. Docker Linux builds TensorFlow's Docker development images are an easy way to set up an environment to build Linux packages from source. These images already contain the source code and dependencies required to build TensorFlow. See the TensorFlow for installation and the. CPU-only The following example uses the:nightly-devel image to build a CPU-only Python 2 package from the latest TensorFlow source code. See the for available TensorFlow -devel tags.

Download the latest development image and start a Docker container that we'll use to build the pip package: docker pull tensorflow/tensorflow:nightly-devel docker run -it -w /tensorflow -v $PWD:/mnt -e HOSTPERMS='$(id -u):$(id -g)' tensorflow/tensorflow:nightly-devel bash git pull # within the container, download the latest source code The above docker run command starts a shell in the /tensorflow directory—the root of the source tree. It mounts the host's current directory in the container's /mnt directory, and passes the host user's information to the container through an environmental variable (used to set permissions—Docker can make this tricky). Alternatively, to build a host copy of TensorFlow within a container, mount the host source tree at the container's /tensorflow directory: docker run -it -w /tensorflow -v /path/to/tensorflow:/tensorflow -v $PWD:/mnt -e HOSTPERMS='$(id -u):$(id -g)' tensorflow/tensorflow: nightly-devel bash With the source tree set up, build the TensorFlow package within the container's virtual environment:.

Configure the build—this prompts the user to answer build configuration questions. Build the tool used to create the pip package. Run the tool to create the pip package.