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Web25/09/ · Disclaimer. Building and using a custom kernel will make it very difficult to get support for your system. While it is a learning experience to compile your own kernel, you will not be allowed to file bugs on the custom-built kernel (if you do, they will be Rejected without further explanation) Web16/02/ · The content of this tutorial is mainly based on the excellent books “Hands-on machine learning with scikit-learn, keras and tensorflow” from Aurélien Géron () and “Tidy Modeling with R” from Max Kuhn and Julia Silge () Another option is to use ggpairs, converts our factor column ocean_proximity into numeric binary (0 WebUndergraduate Courses Lower Division Tentative Schedule Upper Division Tentative Schedule PIC Tentative Schedule CCLE Course Sites course descriptions for Mathematics Lower & Upper Division, and PIC Classes All pre-major & major course requirements must be taken for letter grade only! mathematics courses Math 1: Precalculus General Course WebWelcome to the Yocto Project! The Yocto Project is an open-source collaboration project focused on embedded Linux developers. Among other things, the Yocto Project uses a build system based on the OpenEmbedded (OE) project, which uses the BitBake tool, to construct complete Linux images. The BitBake and OE components are combined together to form WebWe would like to show you a description here but the site won’t allow us ... read more

Contents Disclaimer Reasons for compiling a custom kernel Reasons for NOT compiling a custom kernel Tools you'll need Get the kernel source Option A Use git Option B Download the source archive Option C Download the source package Modify the source for your needs Build the Kernel s Build Method A: Build the kernel when source is from git repository, or from apt-get source Alternate Build Method B : The Old-Fashioned Debian Way Install the new kernel Rebuilding ''linux-restricted-modules'' Speeding Up the Build More documentation Comments External information Disclaimer Building and using a custom kernel will make it very difficult to get support for your system.

While it is a learning experience to compile your own kernel, you will not be allowed to file bugs on the custom-built kernel if you do, they will be Rejected without further explanation. Note: This page would need significant cleaning. Also note that this page describes how to do things for the Edgy 2. Until this kernel source, we did not have any mechanisms in place that would allow people to build their own kernels easily.

This was intentional. This page does NOT describe how to build upstream kernels from kernel. This is how to rebuild the actual Ubuntu kernel starting from source. Reasons for compiling a custom kernel You are a kernel developer. You need the kernel compiled in a special way, that the official kernel is not compiled in for example, with some experimental feature enabled.

You are attempting to debug a problem in the stock Ubuntu kernel for which you have filed or will file a bug report. You have hardware the stock Ubuntu kernel does not support. Reasons for NOT compiling a custom kernel You merely need to compile a special driver. For this, you only need to install the linux-headers packages. You have no idea what you are doing, and if you break something, you'll need help fixing it.

Depending on what you do wrong, you might end up having to reinstall your system from scratch. You got to this page by mistake, and checked it out because it looked interesting, but you don't really want to learn a lot about kernels. If you want to install a new kernel without compilation, you can use Synaptic , search for linux-image and select the kernel version you want to install. Tools you'll need To start, you will need to install a few packages.

Use a following command line to install precisely the packages needed for the release you are using: Hardy 8. Lucid Note that this will almost always be out of date compared to the latest development source, so you should use git option A if you need the latest patches.

Use a follow command to install the build dependencies and extract the source to the current directory : Ubuntu Hardy 8. The Ubuntu supplied modules may not be compatible with a PAE enabled kernel. Ubuntu Karmic Koala 9. gz , and. dsc and a sub-directory. For instance, if uname -r returns 2. dsc and the sub-directory linux Raring Modify the source for your needs For most people, simply modifying the configs is enough.

If you need to install a patch, read the instructions from the patch provider to learn how to apply it. In this directory there are several files. The config file is the base for all targets in that architecture. Then there are several config.

FLAVOUR files that contain options specific to that target. For example, here are the files for 2. generic -rw-r--r-- 1 root root config. server -rw-r--r-- 1 root root config. server-bigiron -rw-r--r-- 1 root root 8 lowlatency -rw-r--r-- 1 root root vars. env of your kernel source directory. If you need to change a config option, simply modify the file that contains the option.

If you modify just the config file, it will affect all targets for this architecture. If you modify one of the target files, it only affects that target. After applying a patch, or adjusting the configs, it is always best to regenerate the config files to ensure they are consistent.

There is a helper command for this. Depending on your needs, you may want to build all the kernel targets, or just one specific to your system. However, you also want to make sure that you do not clash with the stock kernels. In our example, the goal is to build a classification model to predict the type of median housing prices in districts in California.

In particular, the model should learn from California census data and be able to predict wether the median house price in a district population of to people is below or above a certain threshold, given some predictor variables.

Hence, we face a supervised learning situation and should use a classification model to predict the categorical outcomes below or above the preice. Furthermore, we use the F1-Score as a performance measure for our classification problem.

Note that in our classification example we again use the dataset from the previous regession tutorial. Therefore, we first need to create our categorical dependent variable from the numeric variable median house value. We will do this in the phase data understanding during the creation of new variables. Afterwards, we will remove the numeric variable median house value from our data. This downstream system will determine whether it is worth investing in a given area or not.

Since there could be multiple wrong entries of the same type, we apply our corrections to all of the rows of the corresponding variable:. However, in a real data science project, data cleaning is usually a very time consuming process.

Numeric variables should be formatted as integers int or double precision floating point numbers dbl. Categorical nominal and ordinal variables should usually be formatted as factors fct and not characters chr.

We choose to format the variables as dbl , since the values could be floating-point numbers. Note that it is usually a good idea to first take care of the numerical variables. Afterwards, we can easily convert all remaining character variables to factors using the function across from the dplyr package which is part of the tidyverse.

We arrange the data by columns with most missingness:. We have a missing rate of 0. This can cause problems for some algorithms. We will take care of this issue during our data preparation phase. One very important thing you may want to do at the beginning of your data science project is to create new variable combinations. For example:. What you really want is the number of rooms per household. Similarly, the total number of bedrooms by itself is not very useful: you probably want to compare it to the number of rooms.

And the population per household also seems like an interesting attribute combination to look at. Furthermore, in our example we need to create our dependent variable and drop the original numeric variable.

Therefore we drop it. Take a look at our dependent variable and create a table with the package gt. After we took care of our data issues, we can obtain a data summary of all numerical and categorical attributes using a function from the package skimr :. The sd column shows the standard deviation, which measures how dispersed the values are. The p0, p25, p50, p75 and p columns show the corresponding percentiles: a percentile indicates the value below which a given percentage of observations in a group of observations fall.

These are often called the 25th percentile or first quartile , the median, and the 75th percentile. Further note that the median income attribute does not look like it is expressed in US dollars USD.

Actually the data has been scaled and capped at 15 actually, The numbers represent roughly tens of thousands of dollars e. Another quick way to get an overview of the type of data you are dealing with is to plot a histogram for each numerical attribute.

A histogram shows the number of instances on the vertical axis that have a given value range on the horizontal axis. You can either plot this one attribute at a time, or you can use ggscatmat from the package GGally on the whole dataset as shown in the following code example , and it will plot a histogram for each numerical attribute as well as correlation coefficients Pearson is the default. We just select the most promising variabels for our plot:.

Note that our attributes have very different scales. We will take care of this issue later in data preparation, when we use feature scaling data normalization. Finally, many histograms are tail-heavy: they extend much farther to the right of the median than to the left. This may make it a bit harder for some Machine Learning algorithms to detect patterns.

We will transform these attributes later on to have more bell-shaped distributions. For our right-skewed data i. The training data will be used to fit models, and the testing set will be used to measure model performance. We perform data exploration only on the training data. A training dataset is a dataset of examples used during the learning process and is used to fit the models. A test dataset is a dataset that is independent of the training dataset and is used to evaluate the performance of the final model.

If a model fit to the training dataset also fits the test dataset well, minimal overfitting has taken place. A better fitting of the training dataset as opposed to the test dataset usually points to overfitting.

In our data split, we want to ensure that the training and test set is representative of the categories of our dependent variable.

A stratum plural strata refers to a subset part of the whole data from which is being sampled. We only have two categories in our data. The point of data exploration is to gain insights that will help you select important variables for your model and to get ideas for feature engineering in the data preparation phase.

Ususally, data exploration is an iterative process: once you get a prototype model up and running, you can analyze its output to gain more insights and come back to this exploration step.

It is important to note that we perform data exploration only with our training data. Next, we take a closer look at the relationships between our variables.

Since our data includes information about longitude and latitude , we start our data exploration with the creation of a geographical scatterplot of the data to get some first insights:.

Figure 2. This image tells you that the housing prices are very much related to the location e. We can use boxplots to check, if we actually find differences in our numeric variables for the different levels of our dependent categorical variable :.

Additionally, we can use the function ggscatmat to create plots with our dependent variable as color column:. The histograms are tail-heavy: they extend much farther to the right of the median than to the left. We start with a simple count. We can observe that most districts with a median house price above , have an ocean proximity below 1 hour. On the other hand, districts below that threshold are typically inland. Hence, ocean proximity is indeed a good predictor for our two different median house value categories.

We mainly use the tidymodels packages recipes and workflows for this steps. Recipes are built as a series of optional data preparation steps, such as:. Data cleaning : Fix or remove outliers, fill in missing values e. Feature engineering : Discretize continuous features, decompose features e. or aggregate features into promising new features like we already did.

Content Cleanup Required This article should be cleaned-up to follow the content standards in the Wiki Guide. More info Needs Updating This article needs updating to include the latest versions of Ubuntu. Contents Disclaimer Reasons for compiling a custom kernel Reasons for NOT compiling a custom kernel Tools you'll need Get the kernel source Option A Use git Option B Download the source archive Option C Download the source package Modify the source for your needs Build the Kernel s Build Method A: Build the kernel when source is from git repository, or from apt-get source Alternate Build Method B : The Old-Fashioned Debian Way Install the new kernel Rebuilding ''linux-restricted-modules'' Speeding Up the Build More documentation Comments External information Disclaimer Building and using a custom kernel will make it very difficult to get support for your system.

While it is a learning experience to compile your own kernel, you will not be allowed to file bugs on the custom-built kernel if you do, they will be Rejected without further explanation. Note: This page would need significant cleaning. Also note that this page describes how to do things for the Edgy 2.

Until this kernel source, we did not have any mechanisms in place that would allow people to build their own kernels easily. This was intentional. This page does NOT describe how to build upstream kernels from kernel. This is how to rebuild the actual Ubuntu kernel starting from source. Reasons for compiling a custom kernel You are a kernel developer. You need the kernel compiled in a special way, that the official kernel is not compiled in for example, with some experimental feature enabled.

You are attempting to debug a problem in the stock Ubuntu kernel for which you have filed or will file a bug report. You have hardware the stock Ubuntu kernel does not support. Reasons for NOT compiling a custom kernel You merely need to compile a special driver.

For this, you only need to install the linux-headers packages. You have no idea what you are doing, and if you break something, you'll need help fixing it.

Depending on what you do wrong, you might end up having to reinstall your system from scratch. You got to this page by mistake, and checked it out because it looked interesting, but you don't really want to learn a lot about kernels. If you want to install a new kernel without compilation, you can use Synaptic , search for linux-image and select the kernel version you want to install.

Tools you'll need To start, you will need to install a few packages. Use a following command line to install precisely the packages needed for the release you are using: Hardy 8. Lucid Note that this will almost always be out of date compared to the latest development source, so you should use git option A if you need the latest patches.

Use a follow command to install the build dependencies and extract the source to the current directory : Ubuntu Hardy 8. The Ubuntu supplied modules may not be compatible with a PAE enabled kernel. Ubuntu Karmic Koala 9. gz , and. dsc and a sub-directory. For instance, if uname -r returns 2.

dsc and the sub-directory linux Raring Modify the source for your needs For most people, simply modifying the configs is enough. If you need to install a patch, read the instructions from the patch provider to learn how to apply it. In this directory there are several files. The config file is the base for all targets in that architecture. Then there are several config. FLAVOUR files that contain options specific to that target.

For example, here are the files for 2. generic -rw-r--r-- 1 root root config. server -rw-r--r-- 1 root root config. server-bigiron -rw-r--r-- 1 root root 8 lowlatency -rw-r--r-- 1 root root vars. env of your kernel source directory. If you need to change a config option, simply modify the file that contains the option. If you modify just the config file, it will affect all targets for this architecture. If you modify one of the target files, it only affects that target.

After applying a patch, or adjusting the configs, it is always best to regenerate the config files to ensure they are consistent. There is a helper command for this. Depending on your needs, you may want to build all the kernel targets, or just one specific to your system.

However, you also want to make sure that you do not clash with the stock kernels. Note: Though these outside instructions include making a separate and unique branch of the kernel, unlike here, they include thorough explanations of all necessary steps from start to finish. Oneiric It is necessary in git trees following git commit 3ebdce35bb9a72bef "UBUNTU: [Config] Abstract the debian directory". The AUTOBUILD environment variable triggers special features in the kernel build.

First, it skips normal ABI checks ABI is the binary compatibility. It can do this because it also creates a unique ABI ID. If you used a git repo, this unique ID is generated from the git HEAD SHA.

Your packages will be named using this ID. If you have a more than one processor or more than one core, you can speed things up by running concurrent compile commands.

stamp-build-server for the server flavour, etc. The debs are placed in your the parent directory of the kernel source directory. If needed, the Ubuntu modules source for Hardy 8. cd linux-ubuntu-modules config make prepare scripts Alternate Build Method B : The Old-Fashioned Debian Way The new Ubuntu build system is great for developers, for people who need the absolute latest bleeding-edge kernel, and people who need to build a diverse set of kernels several "flavours".

However it can be a little complex for ordinary users. If you don't need the latest development sources, there is a simpler way to compile your kernel from the linux-source package.

Before building the kernel, you must configure it. config Before you run make menuconfig or make xconfig which is what the next step tells you to do , make sure you have the necessary packages: sudo apt-get install qt3-dev-tools libqt3-mt-dev if you plan to use 'make xconfig' sudo apt-get install libncurses5 libncurses5-dev if you plan to use 'make menuconfig' If you would like to see what is different between your original kernel config and the new one and decide whether you want any of the new features , you can run: make oldconfig Since the 2.

ko files much larger than they would otherwise be. Now you can compile the kernel and create the packages: make clean only needed if you want to do a "clean" build make deb-pkg You can enable parallel make use make -j. deb sudo dpkg -i linux-headers deb Similarly, if you have built the Ubuntu module for Hardy 8. deb sudo dpkg -i linux-headers-lum deb If you use modules from linux-restricted-modules , you will need to recompile this against your new linux-headers package.

Note: In response to the various comments in the remainder of this section: On Ubuntu Precise After installing the package my new kernel booted just fine without following any of the methods below. Someone please correct me if I'm mistaken. Since Ubuntu Lucid Instead, there are example scripts provided that will perform the task. These scripts will work for official kernel images as well.

It is real solution. The make-kpkg option is --overlay-dir. Rebuilding ''linux-restricted-modules'' The linux-restricted-modules l-r-m package contains a number of non-DFSG-free drivers as well as some firmware and the ipw wireless networking daemon which, in a perfect world, wouldn't have to be packaged separately, but which unfortunately are not available under a GPL-compatible license.

If you use any of the hardware supported by the l-r-m package, you will likely find that your system does not work as well after switching to a custom kernel. In this case you should try to compile the l-r-m package. See CustomRestrictedModules on how to rebuild l-r-m if you use nVidia or ATI binary drivers, you do. Note: you will need around 8 hours of compilation time and around 10 Gb of hard drive space to compile all kernel flavours and restricted modules.

Further note: There are no l-r-m or linux-restricted-modules packages in Lucid. Speeding Up the Build Use distcc and, if you're rebuilding often, ccache. If you have AMD64 machines available on your local area network, they can still participate in building bit code; distcc seems to handle that automatically. However, with distcc taking over all compiles by default, you will need to set HOSTCC so that when kernel builds want to use the compiler on the host itself, they don't end up distributing jobs to the bit server.

If you fail to do that, you'll get link-compatibility failures between bit and bit code. Partners Support Community Ubuntu. Ubuntu Documentation Official Documentation Community Help Wiki Contribute. Page History Login to edit. Contents Disclaimer Reasons for compiling a custom kernel Reasons for NOT compiling a custom kernel Tools you'll need Get the kernel source Option A Use git Option B Download the source archive Option C Download the source package Modify the source for your needs Build the Kernel s Build Method A: Build the kernel when source is from git repository, or from apt-get source Alternate Build Method B : The Old-Fashioned Debian Way Install the new kernel Rebuilding ''linux-restricted-modules'' Speeding Up the Build More documentation Comments External information.

,Reasons for compiling a custom kernel

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The config file is the base for all targets in that architecture. Rings play a central role in many areas of mathematics, e. Facilitate development of professional mathematical and pedagogical understandings required to teach California? Global bifurcations of cycles. Selected topics in differential equations. Note that this step can not be performed on negative numbers. We will transform these attributes later on to have more bell-shaped distributions.

dsc and the sub-directory linux In particular, the model should learn from California census data and be able to predict wether the median house price in a district population of to people is below or above a certain threshold, given some predictor variables. Introduction to probability through applications and examples, binary option crashcourse tutorial. Otherwise, you might be interested in reading the early chapters of the Yocto Project Development Manual. You can learn more about downloading a Yocto Project kernel in the " Yocto Project Kernel " bulleted item in the Yocto Project Development Manual. deb Similarly, if you have built the Ubuntu module binary option crashcourse tutorial Hardy 8.

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