macOS incompatibility

[worstseed]

This issue has been reported by multiple users when they attempted to install and use JAHS-Bench-201 whose surrogates are based on XGBoost on macOS. Here are a few details:

MacBook Pro 15", 2018
MacOS 13.0 (Ventura) but happened on MacOS 12.* (Monterey) as well
MacBook Pro 13'', M2, 2022 (Monterey)

I created a new conda environment (conda 4.13.0) with Python 3.7.15 (but the same error with Python 3.10.) and installed JAHS-Bench-201 with pip install jahs-bench. Running python -m jahs_bench_examples.minimal leads to the following error:

[20:03:24] WARNING: /Users/runner/work/xgboost/xgboost/src/gbm/gbtree.cc:386: Loading from a raw memory buffer on CPU only machine.  Changing tree_method to hist.
Traceback (most recent call last):
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/jahs_bench_examples/minimal.py", line 13, in <module>
    run()
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/jahs_bench_examples/minimal.py", line 5, in run
    benchmark = jahs_bench.Benchmark(task="cifar10", kind="surrogate", download=True)
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/jahs_bench/api.py", line 135, in __init__
    loaders[kind]()
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/jahs_bench/api.py", line 148, in _load_surrogate
    self._surrogates[o] = XGBSurrogate.load(pth) if not self._lazy else \
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/jahs_bench/surrogate/model.py", line 462, in load
    model = joblib.load(outdir / cls.__model_filename)
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/joblib/numpy_pickle.py", line 587, in load
    obj = _unpickle(fobj, filename, mmap_mode)
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/joblib/numpy_pickle.py", line 506, in _unpickle
    obj = unpickler.load()
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/pickle.py", line 1088, in load
    dispatch[key[0]](self)
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/joblib/numpy_pickle.py", line 331, in load_build
    Unpickler.load_build(self)
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/pickle.py", line 1552, in load_build
    setstate(state)
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/xgboost/core.py", line 1452, in __setstate__
    _LIB.XGBoosterUnserializeFromBuffer(handle, ptr, length))
  File "/usr/local/Caskroom/miniconda/base/envs/jahs_bench_test/lib/python3.7/site-packages/xgboost/core.py", line 218, in _check_call
    raise XGBoostError(py_str(_LIB.XGBGetLastError()))
xgboost.core.XGBoostError: [20:03:24] /Users/runner/work/xgboost/xgboost/src/tree/tree_updater.cc:20: Unknown tree updater grow_gpu_hist
Stack trace:
  [bt] (0) 1   libxgboost.dylib                    0x000000012a9fa4a4 dmlc::LogMessageFatal::~LogMessageFatal() + 116
  [bt] (1) 2   libxgboost.dylib                    0x000000012ab43f39 xgboost::TreeUpdater::Create(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&, xgboost::GenericParameter const*) + 729
  [bt] (2) 3   libxgboost.dylib                    0x000000012aa85d8a xgboost::gbm::GBTree::LoadConfig(xgboost::Json const&) + 2634
  [bt] (3) 4   libxgboost.dylib                    0x000000012aaa4246 xgboost::LearnerConfiguration::LoadConfig(xgboost::Json const&) + 742
  [bt] (4) 5   libxgboost.dylib                    0x000000012aaa5827 xgboost::LearnerIO::Load(dmlc::Stream*) + 743
  [bt] (5) 6   libxgboost.dylib                    0x000000012a9f5101 XGBoosterUnserializeFromBuffer + 145
  [bt] (6) 7   libffi.8.dylib                      0x00000001075cda22 ffi_call_unix64 + 82
  [bt] (7) 8   ???                                 0x00007ff7b8e931e0 0x0 + 140701935940064

Details on versions of any packages installed in the conda environment (conda list):

ca-certificates           2022.10.11           hecd8cb5_0  
certifi                   2022.9.24        py37hecd8cb5_0  
charset-normalizer        2.1.1                    pypi_0    pypi
configspace               0.4.21                   pypi_0    pypi
cython                    0.29.32                  pypi_0    pypi
idna                      3.4                      pypi_0    pypi
jahs-bench                1.0.2                    pypi_0    pypi
joblib                    1.1.1                    pypi_0    pypi
libffi                    3.4.2                hecd8cb5_6  
ncurses                   6.3                  hca72f7f_3  
numpy                     1.21.6                   pypi_0    pypi
openssl                   1.1.1s               hca72f7f_0  
pandas                    1.3.5                    pypi_0    pypi
pip                       22.3.1           py37hecd8cb5_0  
pyparsing                 3.0.9                    pypi_0    pypi
python                    3.7.15               h218abb5_1  
python-dateutil           2.8.2                    pypi_0    pypi
pytz                      2022.6                   pypi_0    pypi
pyyaml                    6.0                      pypi_0    pypi
readline                  8.2                  hca72f7f_0  
requests                  2.28.1                   pypi_0    pypi
scikit-learn              1.0.2                    pypi_0    pypi
scipy                     1.7.3                    pypi_0    pypi
setuptools                65.5.0           py37hecd8cb5_0  
six                       1.16.0                   pypi_0    pypi
sqlite                    3.40.0               h880c91c_0  
threadpoolctl             3.1.0                    pypi_0    pypi
tk                        8.6.12               h5d9f67b_0  
urllib3                   1.26.13                  pypi_0    pypi
wheel                     0.37.1             pyhd3eb1b0_0  
xgboost                   1.5.2                    pypi_0    pypi
xz                        5.2.8                h6c40b1e_0  
yacs                      0.1.8                    pypi_0    pypi
zlib                      1.2.13               h4dc903c_0 

[trivialfis]

Hi, apologies for the slow response. This is actually a pickle compatibility issue, we recommend using save_model instead of Python pickle. The latter requires the pickled object to preserve the complete information including parameters like which device to use. Since the macos build is not CUDA-enabled, the GPU implementation is not available on that platform. save_model function can be used to discard all the hyper-parameters that are not related to the model itself. For more details, please see https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html .