Coding Guideline


C++ Coding Guideline

  • Follow Google style for C++, with two exceptions:

    • Each line of text may contain up to 100 characters.

    • The use of C++ exceptions is allowed.

  • Use C++17 features such as smart pointers, braced initializers, lambda functions, and std::thread.

  • Use Doxygen to document all the interface code.

  • We have some comments around symbols imported by headers, some of those are hinted by include-what-you-use. It’s not required.

  • We use clang-tidy and clang-format. You can check their configuration in the root directory of the XGBoost source tree.

  • We have a series of automatic checks to ensure that all of our codebase complies with the Google style. Before submitting your pull request, you are encouraged to run the style checks on your machine. See R Coding Guideline.

Python Coding Guideline

R Coding Guideline

Code Style

  • We follow Google’s C++ Style guide for C++ code.

    • This is mainly to be consistent with the rest of the project.

    • Another reason is we will be able to check style automatically with a linter.

  • When needed, you can disable the linter warning of certain line with // NOLINT(*) comments.

  • We use roxygen for documenting the R package.

Rmarkdown Vignettes

Rmarkdown vignettes are placed in R-package/vignettes. These Rmarkdown files are not compiled. We host the compiled version on doc/R-package.

The following steps are followed to add a new Rmarkdown vignettes:

  • Add the original rmarkdown to R-package/vignettes.

  • Modify doc/R-package/Makefile to add the markdown files to be build.

  • Clone the dmlc/web-data repo to folder doc.

  • Now type the following command on doc/R-package:

  • This will generate the markdown, as well as the figures in doc/web-data/xgboost/knitr.

  • Modify the doc/R-package/ to point to the generated markdown.

  • Add the generated figure to the dmlc/web-data repo.

    • If you already cloned the repo to doc, this means git add

  • Create PR for both the markdown and dmlc/web-data.

  • You can also build the document locally by typing the following command at the doc directory:

    make html

The reason we do this is to avoid exploded repo size due to generated images.

R package versioning

See XGBoost Release Policy.

Testing R package with different compilers

You can change the default compiler of R by changing the configuration file in home directory. For instance, if you want to test XGBoost built with clang++ instead of g++ on Linux, put the following in your ~/.R/Makevars file:


Be aware that the variable name should match with the name used by R CMD:

R CMD config CXX17

Registering native routines in R

According to R extension manual, it is good practice to register native routines and to disable symbol search. When any changes or additions are made to the C++ interface of the R package, please make corresponding changes in src/init.c as well.

Generating the Package and Running Tests

The source layout of XGBoost is a bit unusual to normal R packages as XGBoost is primarily written in C++ with multiple language bindings in mind. As a result, some special cares need to be taken to generate a standard R tarball. Most of the tests are being run on CI, and as a result, the best way to see how things work is by looking at the CI configuration files (GitHub action, at the time of writing). There are helper scripts in tests/ci_build and R-package/tests/helper_scripts for running various checks including linter and making the standard tarball.

Running Formatting Checks Locally

Once you submit a pull request to dmlc/xgboost, we perform two automatic checks to enforce coding style conventions. To expedite the code review process, you are encouraged to run the checks locally on your machine prior to submitting your pull request.


We use a combination of linters to enforce style convention and find potential errors. Linting is especially useful for scripting languages like Python, as we can catch many errors that would have otherwise occurred at run-time.

For Python scripts, pylint, black and isort are used for providing guidance on coding style, and mypy is required for type checking. For C++, cpplint is used along with clang-tidy. For R, lintr is used.

To run checks for Python locally, install the checkers mentioned previously and run:

cd /path/to/xgboost/
python ./tests/ci_build/ --fix

To run checks for R:

cd /path/to/xgboost/
Rscript tests/ci_build/lint_r.R $(pwd)

To run checks for cpplint locally:

cd /path/to/xgboost/
python ./tests/ci_build/

See next section for clang-tidy. For CMake scripts:

bash ./tests/ci_build/

Lastly, the linter for jvm-packages is integrated into the maven build process.


Clang-tidy is an advance linter for C++ code, made by the LLVM team. We use it to conform our C++ codebase to modern C++ practices and conventions.

To run this check locally, run the following command from the top level source tree:

cd /path/to/xgboost/
python3 tests/ci_build/

Also, the script accepts two optional integer arguments, namely --cpp and --cuda. By default they are both set to 1, meaning that both C++ and CUDA code will be checked. If the CUDA toolkit is not installed on your machine, you’ll encounter an error. To exclude CUDA source from linting, use:

cd /path/to/xgboost/
python3 tests/ci_build/ --cuda=0

Similarly, if you want to exclude C++ source from linting:

cd /path/to/xgboost/
python3 tests/ci_build/ --cpp=0

Guide for handling user input data

This is an in-comprehensive guide for handling user input data. XGBoost has wide verity of native supported data structures, mostly come from higher level language bindings. The inputs ranges from basic contiguous 1 dimension memory buffer to more sophisticated data structures like columnar data with validity mask. Raw input data can be used in 2 places, firstly it’s the construction of various DMatrix, secondly it’s the in-place prediction. For plain memory buffer, there’s not much to discuss since it’s just a pointer with a size. But for general n-dimension array and columnar data, there are many subtleties. XGBoost has 3 different data structures for handling optionally masked arrays (tensors), for consuming user inputs ArrayInterface should be chosen. There are many existing functions that accept only plain pointer due to legacy reasons (XGBoost started as a much simpler library and didn’t care about memory usage that much back then). The ArrayInterface is a in memory representation of __array_interface__ protocol defined by numpy or the __cuda_array_interface__ defined by numba. Following is a check list of things to have in mind when accepting related user inputs:

  • [ ] Is it strided? (identified by the strides field)

  • [ ] If it’s a vector, is it row vector or column vector? (Identified by both shape and strides).

  • [ ] Is the data type supported? Half type and 128 integer types should be converted before going into XGBoost.

  • [ ] Does it have higher than 1 dimension? (identified by shape field)

  • [ ] Are some of dimensions trivial? (shape[dim] <= 1)

  • [ ] Does it have mask? (identified by mask field)

  • [ ] Can the mask be broadcasted? (unsupported at the moment)

  • [ ] Is it on CUDA memory? (identified by data field, and optionally stream)

Most of the checks are handled by the ArrayInterface during construction, except for the data type issue since it doesn’t know how to cast such pointers with C builtin types. But for safety reason one should still try to write related tests for the all items. The data type issue should be taken care of in language binding for each of the specific data input. For single-chunk columnar format, it’s just a masked array for each column so it should be treated uniformly as normal array. For input predictor X, we have adapters for each type of input. Some are composition of the others. For instance, CSR matrix has 3 potentially strided arrays for indptr, indices and values. No assumption should be made to these components (all the check boxes should be considered). Slicing row of CSR matrix should calculate the offset of each field based on respective strides.

For meta info like labels, which is growing both in size and complexity, we accept only masked array at the moment (no specialized adapter). One should be careful about the input data shape. For base margin it can be 2 dim or higher if we have multiple targets in the future. The getters in DMatrix returns only 1 dimension flatten vectors at the moment, which can be improved in the future when it’s needed.