Import sklearn.metrics.f1_score after import torch causes an error

1

Hello,

I find that if I run import sklearn.metrics.f1_score after import torch, it will raise an error. Could anyone help me?

I have tried installing libgcc but I found it has already been in my environment.

Success run

$ python3
Python 3.8.15 (default, Nov 24 2022, 15:19:38) 
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from sklearn.metrics import f1_score
>>> import torch
>>>

Failed run

Python 3.8.15 (default, Nov 24 2022, 15:19:38) 
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> from sklearn.metrics import f1_score
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/anaconda/envs/xxx/lib/python3.8/site-packages/sklearn/metrics/__init__.py", line 41, in <module>
    from . import cluster
  File "/anaconda/envs/xxx/lib/python3.8/site-packages/sklearn/metrics/cluster/__init__.py", line 22, in <module>
    from ._unsupervised import silhouette_samples
  File "/anaconda/envs/xxx/lib/python3.8/site-packages/sklearn/metrics/cluster/_unsupervised.py", line 16, in <module>
    from ..pairwise import pairwise_distances_chunked
  File "/anaconda/envs/xxx/lib/python3.8/site-packages/sklearn/metrics/pairwise.py", line 33, in <module>
    from ._pairwise_distances_reduction import PairwiseDistancesArgKmin
ImportError: /lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /anaconda/envs/xxx/lib/python3.8/site-packages/sklearn/metrics/_pairwise_distances_reduction.cpython-38-x86_64-linux-gnu.so)
>>>

My environment

$ nvidia-smi
Wed Dec 21 12:13:56 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.60.13    Driver Version: 525.60.13    CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100 80G...  On   | 00000001:00:00.0 Off |                    0 |
| N/A   34C    P0    42W / 300W |      4MiB / 81920MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

$ python -c "import torch; print(torch.__config__.show())"
PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.6
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.3.2  (built against CUDA 11.5)
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
2

I cannot reproduce the issue with a recent nightly binary and the latest scikit-learn release:

import torch
from sklearn.metrics import f1_score

f1_score
# <function sklearn.metrics._classification.f1_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')>

print(torch.__version__)
# 1.14.0.dev20221207+cu117
import sklearn
print(sklearn.__version__)
# 1.2.0
3

Thanks for your reply! The problem is solved after updating sklearn into version 1.2.0.

The original versions of torch and sklearn I used were 1.12.0+cu116 and 1.1.3 respectively.

1 Like