Segmentation Fault when Importing AutoGluon

We have noticed that a specific order of import AutoGluon modules will result in seg fault. For example,

import autogluon.text  # same applies to autogluon.multimodal
from autogluon.vision import ImagePredictor

and

import autogluon.text. # same applies to autogluon.multimodal
import autogluon.timeseries

will result in

free(): invalid size
Aborted (core dumped)

This is because of some underlying issue with mxnet when it is used along with pytorch. There is not too much AutoGluon team can do about this, but we are actively working toward getting rid of mxnet.

To get around with it, simply reverse the order you import. For example,

from autogluon.vision import ImagePredictor
import autogluon.text

and

import autogluon.timeseries
import autogluon.text

[yinweisu]

Created a new env and wasn’t able to reproduce it either.

(temp) ubuntu@ip-172-31-11-12:~/yinweisu/autogluon$ pip3 freeze | grep mxnet
mxnet-cu110==1.9.1
(temp) ubuntu@ip-172-31-11-12:~/yinweisu/autogluon$ pip3 freeze | grep torch
pytorch-lightning==1.7.7
pytorch-metric-learning==1.3.2
torch==1.12.1
torchmetrics==0.8.2
torchtext==0.13.1
torchvision==0.13.1
(temp) ubuntu@ip-172-31-11-12:~/yinweisu/autogluon$ python3
Python 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:18) 
[GCC 10.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import autogluon.text
>>> from autogluon.vision import ImagePredictor
/home/ubuntu/anaconda3/envs/temp/lib/python3.8/site-packages/gluoncv/__init__.py:40: UserWarning: Both `mxnet==1.9.1` and `torch==1.12.1+cu102` are installed. You might encounter increased GPU memory footprint if both framework are used at the same time.
  warnings.warn(f'Both `mxnet=={mx.__version__}` and `torch=={torch.__version__}` are installed. '
>>> 

However, according to @gradientsky, there are still reports of such issues. Let’s just keep this thread open for anyone encountered this issue as a reference and close it once we completely remove mxnet in 0.7. @canerturkmen

[sxjscience]

This should be related to ImagePredictor hasn’t switched default backend to MXNet.