* Make pip install xgboost*.tar.gz work by fixing build-python.sh
* Simplify install doc
* Add test
* Install Miniconda for Linux target too
* Build XGBoost only once in sdist
* Try importing xgboost after installation
* Don't set PYTHONPATH env var for sdist test
* Add OpenMP as CMake target
* Require CMake 3.12, to allow linking OpenMP target to objxgboost
* Specify OpenMP compiler flag for CUDA host compiler
* Require CMake 3.16+ if the OS is Mac OSX
* Use AppleClang in Mac tests.
* Update dmlc-core
- Install wget explicitly to match openssl.
- Install CMake explicitly.
- Use newer miniconda link.
- Reenable unittests.
- gcc@9 + xcode@10 for osx due to missing <_stdio.h>. Other versions of gcc should also work. But as homebrew pour gcc@9 after update by default, so I just stick with latest version.
- Disabled one external memory test for OSX. Not sure about the thread implementation in there and fixing external memory is beyond the scope of this PR.
- Use Python3 with conda in jvm package.
* All Linux tests are now in Jenkins CI
* Tests are now de-coupled from builds. We can now build XGBoost with one version of CUDA/JDK and test it with another version of CUDA/JDK
* Builds (compilation) are significantly faster because 1) They use C5 instances with faster CPU cores; and 2) build environment setup is cached using Docker containers
* Fix failing Travis CI on Mac
Use Homebrew Addon + latest Mac image
* Use long command for pytest
* Downgrade OSX image to xcode9.3, to use Java 8
* Install pytest in Python 2 environment
* Remove clang-tidy from Travis
* Fix#3402: wrong fid crashes distributed algorithm
The bug was introduced by the recent DMatrix refactor (#3301). It was partially
fixed by #3408 but the example in #3402 was still failing. The example in #3402
will succeed after this fix is applied.
* Explicitly specify "this" to prevent compile error
* Add regression test
* Add distributed test to Travis matrix
* Install kubernetes Python package as dependency of dmlc tracker
* Add Python dependencies
* Add compile step
* Reduce size of regression test case
* Further reduce size of test
