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Making A Kernel Zip \/\/TOP\\\\


After a successful build, you will find the kernel image ( Image ) in arch/arm64/boot/ directory. The device tree file for ESPRESSObin ( armada-3720-community.dtb ) has been generated in arch/arm64/boot/dts/marvell/ directory.




Making A Kernel Zip



A series of patches needs to be applied for kernel version 4.4.52, branches linux-4.4.52-armada-17.06 and onward. These patches address the changes required for ESPRESSObin v7 hardware revision, as well as ensure backward compatibility with previous ESPRESSObin revisions (v5 and older).


After a successful build, you will find the kernel image ( Image ) in arch/arm64/boot/ directory. The device tree files for different ESPRESSObin hardware revisions have been generated in arch/arm64/boot/dts/marvell/ directory.


This section will show how to download, configure and build mainline kernel 4.14 for your ESPRESSObin board. Make sure that all the requirements listed in Build system requirements are installed on your build machine.


I've been given a .zip file containing source for a proprietary kernel module. Once unzip'd, there is an install script that needs to be run. The install script untar's the actual source and builds the kernel module. It requires kernel headers to compile against.Here is my Buildroot .mk file:


Why is the kernel source not visible to this build? I thought that, since Buildroot is building the kernel as part of the overall process, the header files would be available for subsequent kernel module compiles. Am I missing a setting? I feel that I'm not understanding the Buildroot process in a basic way, even after referring to the manual many times.


Regarding the build issue: it is impossible to help without studying the specific build system of this kernel module. Very likely you will need to pass some environment variables to tell the build system where your kernel source code is located, and possibly other things. But without looking at the specific details, it's impossible to help you.


You can have a look at how standard out of tree kernel modules are handled by looking at the package/pkg-kernel-module.mk code. However, that will not be directly useful to a package like yours that uses a custom installation script.


Kali NetHunter is available for un-rooted devices (NetHunter Rootless), for rooted devices that have a custom recovery (NetHunter Lite), and for rooted devices with custom recovery for which a NetHunter specific kernel is available (NetHunter).


Both rooted editions provide additional tools & services.A custom kernel can extend that functionality by adding additional network and USB gadget drivers as well as wifi injection support for selected wifi chips.


NetHunter Lite can be installed on all Android devices that are rooted and have a custom recovery.The full NetHunter experience requires a devices specific kernel that has been purpose built for Kali NetHunter.The NetHunter GitLab repository contains over 164 kernels for over 65 devices. Kali Linux publishes over 25 images for the most popular devices on the NetHunter download page.The following live reports are generated automatically by GitLab CI:


The Kernel Density tool calculates the magnitude per unit area from point and line features using the kernel function. This function spreads the point location value outwards to each cell within the search radius. This creates a surface with the highest value at the point location and the lowest value at the search radius boundary. Refer to ArcMap: How Kernel Density works for more information.


  • ArcMap: Differences between point, line, and kernel density

  • FAQ: Can a heat map be created in ArcMap?

  • FAQ: What tools are used to create heat maps, hot spots, and interpolation surfaces in ArcMap?

  • FAQ: Is it possible to draw a heat map on top of other feature layers in ArcGIS Online?

  • FAQ: Is it possible to symbolize date fields by quantity in ArcMap?

  • How To: Create heat maps in ArcGIS Pro using the Density toolset



Amazon SageMaker is a fully managed service that allows you to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. In August 2019, Amazon SageMaker announced the availability of the pre-installed R kernel in all Regions. This capability is available out-of-the-box and comes with the reticulate library pre-installed. This library offers an R interface for the Amazon SageMaker Python SDK, which enables you to invoke Python modules from within an R script.


This post discusses how to create a custom R environment (kernel) in Amazon SageMaker on top of the built-in R kernel and how to persist that between sessions. The post explains how to install a new package in the R environment, how this new environment can be saved on Amazon Simple Storage Service (Amazon S3), and how you can use it to create new Amazon SageMaker instances using the Amazon SageMaker lifecycle configuration. The post also includes bash scripts that you can use for lifecycle configurations when creating or starting an Amazon SageMaker notebook instance.


By default, Amazon SageMaker launches the base R kernel every time you stop and start an Amazon SageMaker instance. Any additional packages you install are lost when you stop the instance, and you have to reinstall the packages when you start the instance again. This is time-consuming and cumbersome. The solution is to save the environment on the EBS storage of the instance and link it to a custom R kernel upon startup using the Amazon SageMaker lifecycle configuration script. For more information, see Customize a Notebook Instance Using a Lifecycle Configuration Script.


This script creates a symbolic link between the ./evns/ folder (which contains the custom R environment) and the anaconda custom-r environment. This allows the environment to be listed under the kernels in Amazon SageMaker.


You now have a notebook with your custom R environment. When in your notebook, you should see the R logo in the upper right corner corner of the Juypyter environment, which indicates the kernel is an R kernel, and the name of your kernel should be conda_r_custom-r. To test the environment, import one of the libraries that you included in the custom environment (for example, rJava).


This post walked you through creating a custom, persistent R environment for Amazon SageMaker notebook instances. For example notebooks for R on Amazon SageMaker, see the Amazon SageMaker examples GitHub repository. For more details about creating an Amazon SageMaker notebook instance with R kernel, visit the coding with R on Amazon SageMaker notebook instances blog post. You can visit R User Guide to Amazon SageMaker on the developer guide for more details on ways of leveraging Amazon SageMaker features using R. In addition, for more resources to further your experience with Amazon SageMaker, see the AWS Machine Learning Blog.


Both of these routes are covered below, however, you are strongly recommended to follow the cross-compilation route. The low processing power of the Raspberry Pi means that a local compile will take many hours. A compilation of the latest kernel and modules took about 752 minutes (12h30m)!


This section serves to hold a new user's hand just a bit more than some of the other more generic information below in the document. To get more information on the steps in the roadmap, search this page for additional details. It assumes you can navigate filesystems, move files across systems, and have a general understanding of compiling linux kernels, filesystems, partitions, and block devices.


This series of steps yielded a successful custom/updated hardfp kernel to a stock Raspbian installation, cross compiled from an AMD 64-bit Debian system without regression on any kernel configuration options or requiring modified boot parameters. Be aware that in the worst case, you may need to overlay a stock set of kernel/modules/firmware on the Raspberry Pi if something fails. If you do not know how to do this, then a reimage of the SD card may be necessary. Assuming this is not an issue for your configuration, continue onward:


The kernel source should be downloaded from the Raspberry Pi Linux section on GitHub. Although you could just compile the vanilla kernel from Kernel.org, it will not have the necessary drivers and modules for the Broadcom SoC on the Raspberry Pi. You can however apply patches from the vanilla kernel to the Raspberry Pi one - be prepared for potential compiler grumbles though!


Detailed openSUSE Raspberry Pi 12.3 Image 20130407 + 3.8.8 kernel hack tutorial witten (2013-04-22 updated) see: =87&t=40664&p=331953#p331953The kernel compile takes about 22 hours on Raspberry Pi Model B due massive module compiles. Include all IP_VS, ARPD, Fuse-zfs, Zram and more :-)


The kernel source requires a case-sensitive filesystem. If you do not have a HFS+ case-sensitive partition that can be used, create a disk image with the appropriate format.Ensure the latest versin of Xcode and command line tools are installed from Apple Developer ConnectionInstall macports


The fully built kernel will be arch/arm/boot/Image. Copy your new kernel file into the Raspberry Pi boot partition, though preferably as a new file (such as kernel_new.img) just in case it doesn't work. If you're building on the Raspberry Pi, just copy the file to /boot. If you use a different filename, edit config.txt change the kernel line:


NOTE: If you have rebuilt the new kernel with exactly the same version as the one that's running, you'll need to remove the old modules first. Ideally this should be done offline by mounting the SD card on another system.


NOTE: The lib directory will have symlinks back to the kernel sources (lib/modules//source and lib/modules//build). If you have limited space on the SD card and don't intend to compile modules on the Raspberry Pi itself, you will probably want to remove those links before you transfer the lib directory. The size difference can be many hundreds of MB. 350c69d7ab


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