Comments (5)
You have the issue, possibly because of #49. Please try to build the .whl file on your own. Our team is very busy recently due to several paper deadlines, and will look into the issues at a later stage.
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You have the issue, possibly because of #49. Please try to build the .whl file on your own. Our team is very busy recently due to several paper deadlines, and will look into the issues at a later stage.
I am facing the same issue after git cloning, building and pip installing thundergbm on linux.
Whenever I run
from thundergbm import *,
I face the same error.
When I run
mkdir build && cd build && cmake .. && make -j
I face:
CMakeFiles/Makefile2:168: recipe for target 'src/thundergbm/CMakeFiles/thundergbm.dir/all' failed
make[1]: *** [src/thundergbm/CMakeFiles/thundergbm.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2
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Hi @Kunjal1999, thanks for the comments.
You couldn't import the library because you haven't built the library successfully. So you should check the library building process. Here are some suggestions that might help.
- Refer to the document as @zeyiwen mentioned.Make sure to initialize and update the submodules.
- Check the version of CUDA. If you are using CUDA > 11.0, you need to clone the
support_cuda_11 branch
. - We couldn't reproduce your result on a Unbuntu 16.0.4 with CUDA 11.0. So introducing your system configurations and uploading the entire error stack will be more appreciated.
Thanks.
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Hi @Kunjal1999, thanks for the comments.
You couldn't import the library because you haven't built the library successfully. So you should check the library building process. Here are some suggestions that might help.
- Refer to the document as @zeyiwen mentioned.Make sure to initialize and update the submodules.
- Check the version of CUDA. If you are using CUDA > 11.0, you need to clone the
support_cuda_11 branch
.- We couldn't reproduce your result on a Unbuntu 16.0.4 with CUDA 11.0. So introducing your system configurations and uploading the entire error stack will be more appreciated.
Thanks.
Hey, thanks for the reply, I was running the commands on Google Colab.
CUDA:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Wed_Jul_22_19:09:09_PDT_2020
Cuda compilation tools, release 11.0, V11.0.221
Build cuda_11.0_bu.TC445_37.28845127_0
from thundergbm.
Some bash commands to install ThunderGBM on Colab:
# clone and init submodules
!git clone -b support_cuda11 https://github.com/Xtra-Computing/thundergbm
!cd thundergbm && git submodule init && git submodule update && mkdir build
# build the library
!cd thundergbm/build && cmake .. && make -j 10
# test if built successfully
!cd thundergbm/build && ./bin/thundergbm-train data=../dataset/test_dataset.txt
# build wheel file and install it with pip
# clear the dist dir
!cd thundergbm/python && ls dist && rm -rf dist/* && ls dist
# generate the wheel file and install
!cd thundergbm/python && ls && python setup.py bdist_wheel && cd dist && ls && pip install `ls`
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Related Issues (20)
- any status update on issue #63? HOT 1
- ImportError: cannot import name 'TGBMClassifier' from 'thundergbm' (unknown location)
- FileNotFoundError: Could not find module 'c:\users\donald seger\miniconda3\envs\tensorflow\lib\site-packages\thundergbm-0.3.16-py3.8.egg\thundergbm\thundergbm.dll' (or one of its dependencies). Try using the full path with constructor syntax.
- Can you please answer issue #63?
- the Random Forest classifies everything to be 1 HOT 2
- Can you please answer issue #68?
- Is there a parameter to set "minimum samples per leaf" for Random Forest? HOT 1
- Why did you remove my issue? It is completely legit. HOT 1
- Please why are you removing my issue without any reason? HOT 1
- Debug Assertion Failed
- AttributeError: 'TGBMClassifier' object has no attribute 'save' HOT 1
- issue with saving and reloading model? HOT 2
- Please answer issue #75
- How to visualize the ThunderGBM? HOT 3
- random seed generator HOT 1
- Output of multi:softprob is probably wrong HOT 4
- Problems with random forest classifier when using more and deeper leaners
- Check failed: [max_elem + n_columns*(max_elem + 1) < 0x7fffffff] Max_values is too large to be transformed.请问这个问题怎么解决? HOT 2
- Can the Negative Weights be Input? HOT 2
- Cuda 12
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