Comments (8)
maybe it's better for the team to maintain the conda installation with higher versions.
from lightgbm.
It hungs at
bst = lgb.train(params, train_data, num_boost_round=10)
from lightgbm.
Thanks for using LightGBM.
If it hands on lgb.train()
, then I guess the code after that line is not necessary, correct? If so, please remove it from the example.
Can you please share any logs produced by lightgbm
prior to it "hanging"?
from lightgbm.
ok, thanks for your help. In fact, i had a model file and i just wanted to load it at beginning. It hanged at "lgb.Booster(model_file=model_path)" when loading, after that i tried the mini-case and it hanged at "lgb.train()".
By the way, what's the default log path by lightgbm
? I will try to find more information tonight~
from lightgbm.
By the way, what's the default log path by lightgbm?
By default, lightgbm
does not write logs to a file. It writes them directly to standard output (for example, printed in the terminal or the output below an executing Jupyter notebook cell).
from lightgbm.
it hangs at after print(1), at code _safe_call(_LIB.LGBM_BoosterCreateFromModelfile( _c_str(str(model_file)), ctypes.byref(out_num_iterations), ctypes.byref(self._handle)))
elif model_file is not None:
# Prediction task
out_num_iterations = ctypes.c_int(0)
print(1)
_safe_call(_LIB.LGBM_BoosterCreateFromModelfile(
_c_str(str(model_file)),
ctypes.byref(out_num_iterations),
ctypes.byref(self._handle)))
print(2)
out_num_class = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumClasses(
self._handle,
ctypes.byref(out_num_class)))
print(3)
self.__num_class = out_num_class.value
print(4)
self.pandas_categorical = _load_pandas_categorical(file_name=model_file)
print(5)
if params:
_log_warning('Ignoring params argument, using parameters from model file.')
params = self._get_loaded_param()
print(6)
from lightgbm.
ok, I find the problem. It's because of my numpy, scipy, pandas version is not compatible to the model file.
should make sure that these compute libs are compatible to the model files.
from lightgbm.
ok, I find the problem. It's because of my numpy, scipy, pandas version is not compatible to the model file.
Can you please provide specific details about this? I don't understand this statement.
You may want to see "How to create a reproducible example" (link) for some advice on reporting software problems in a way that others can help confirm and investigate them.
from lightgbm.
Related Issues (20)
- [python-package] [gpu] GPU install on Windows fails HOT 2
- [python-package] Training data partially available when loading exported lgb.Dataset HOT 9
- [CUDA] illegal memory access when using CUDA and large max_bin and large dataset HOT 1
- [python-package] How to pass additional attributes to custom eval or loss functions in regression task? HOT 8
- [python-package] ValueError when using scikit-learn API for multiclass binary classification HOT 2
- [RFC] Sync supported CUDA versions with a new support policy for CUDA Container Images HOT 1
- [R-package] CRAN warnings on gcc-14 HOT 1
- [GPU] GPU trainer takes more time than CPU's HOT 5
- [GPU] [performance] LightGBM Running Significantly Slower Than XGBoost on GPU for Classification Task HOT 6
- Extremely slow data loading on high dimensional dataset.
- [R-package], [GPU], Windows10, install error HOT 1
- `runtime error: member call on address 0x612000003ad8 which does not point to an object of type 'std::streambuf'` in macOS cpp_tests CI job HOT 2
- `OSError: [WinError 122] The data area passed to a system call is too small` in Windows bdist CI job HOT 4
- tests failed HOT 5
- [ci] [R-package] clang18 jobs failing: _mm_malloc compilation errors HOT 2
- [GPU][CUDA] Add support for linear tree with device=gpu, device=cuda
- Roadmap link does not work on the CONTRIBUTING.md file HOT 1
- Can't read old models saved with saveRDS.lgb.Booster on version 4.3.0 of lightgbm HOT 3
- After saving the datasets to disk, training with validation on the test set doesn't work (bins mapper issue) HOT 1
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 lightgbm.