Comments (3)
sdaulton
commented on June 21, 2024
+1. I also was unable to install on MacOS
from jahs_bench_201.
nilskober
commented on June 21, 2024
Same issue on my machine with MacOS. Here a few details:
MacBook Pro 15" 2018
MacOS 13.0 (Ventura) but happened on MacOS 12.* (Monterey) as well
I created a new conda environment (conda 4.13.0) with Python 3.7.15 (but same error with Python 3.10.) and installed jahsbench 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
from jahs_bench_201.
NeoChaos12
commented on June 21, 2024
As per the response of the authors of XGBoost, the correct way to handle this issue on our end will require a very significant change to the way surrogate models are saved and loaded, possibly requiring a complete rewrite of the interface between jahs_bench.surrogate.model.XGBSurrogate and sklearn.pipeline.Pipeline. Thus, I will defer this to a future update to the repo.
from jahs_bench_201.
Related Issues (14)
- Incompatible checksums error HOT 8
- Provide a fixed tag version once stable HOT 2
- Add a `py.typed` to export type information
- Export the ability to create the `joint_config_space` as a function HOT 1
- Issue with config and trajectory HOT 1
- pip install error HOT 1
- Ambiguity in the query HOT 3
- Termination in colorectal_histology due to memory overflow HOT 3
- Bug in argument check (metrics) HOT 1
- Wrong path when loading tabular data HOT 2
- Moving away from pickles
- Issue with evaluating multiobjective benchmarks HOT 4
- Data download
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from jahs_bench_201.