Segmentation fault on import

Issue

I am getting a segmentation fault when running the following import statement

from autogluon.tabular import TabularPrediction

and segmentation fault: 11 when running

from autogluon.text import TextPrediction

I am using a recent branch of autogluon, installed in a clean virtual environment: autogluon version 0.0.16b20210120

Environment

conda create -n autogluon-dev python=3.7
conda activate autogluon-dev
python3 -m pip install --upgrade "mxnet_cu100<2.0.0"
python3 -m pip install --pre autogluon

I have also tried installing by cloning autogluon from github, replacing the last line with

git clone https://github.com/awslabs/autogluon.git
cd autogluon
bash full_install.sh

Platform: Linux/UNIX (ec2) Instance type: p2.8xlarge AMI: Deep Learning AMI (Amazon Linux 2) Version 36.0

python3 --version: Python 3.7.9 nvcc --version: Cuda compilation tools, release 10.0, V10.0.130 pip freeze:

attrs==20.3.0
autocfg==0.0.6
autogluon==0.0.16b20210120
autogluon-contrib-nlp==0.0.1b20210112
autogluon.core==0.0.16b20210120
autogluon.extra==0.0.16b20210120
autogluon.mxnet==0.0.16b20210120
autogluon.tabular==0.0.16b20210120
autogluon.text==0.0.16b20210120
autogluon.vision==0.0.16b20210120
autograd==1.3
bcrypt==3.2.0
beautifulsoup4==4.9.3
blis==0.7.4
bokeh==2.2.0.dev4
boto3==1.16.57
botocore==1.19.57
Bottleneck==1.3.2
catalogue==1.0.0
catboost==0.24.4
certifi==2020.12.5
cffi==1.14.4
chardet==4.0.0
click==8.0.0a1
cloudpickle==1.6.0
ConfigSpace==0.4.16
contextvars==2.4
cryptography==3.3.1
cycler==0.10.0
cymem==2.0.5
Cython==3.0a6
d8==0.0.2
dask==2021.1.0
decorator==4.4.2
decord==0.4.2
dill==0.3.3
distributed==2021.1.0
fastai==1.0.61
fastprogress==1.0.0
flake8==3.8.4
future==0.18.2
gluoncv==0.10.0b20210120
graphviz==0.8.4
HeapDict==1.0.1
idna==2.10
immutables==0.14
importlib-metadata==3.4.0
iniconfig==1.1.1
Jinja2==3.0.0a1
jmespath==0.10.0
joblib==1.0.0
kaggle==1.5.10
kiwisolver==1.3.1
liac-arff==2.5.0
lightgbm==3.1.1
MarkupSafe==2.0.0a1
matplotlib==3.3.3
mccabe==0.6.1
msgpack==1.0.2
murmurhash==1.0.5
mxnet-cu100==1.7.0
networkx==2.5
numexpr==2.7.2
numpy==1.19.5
nvidia-ml-py3==7.352.0
opencv-python==4.5.1.48
openml==0.11.0
packaging==20.8
pandas==1.2.1
paramiko==2.7.2
Pillow==6.2.1
plac==1.1.3
plotly==4.14.3
pluggy==1.0.0.dev0
portalocker==2.0.0
preshed==3.0.5
protobuf==3.14.0
psutil==5.7.0
py==1.10.0
pyaml==20.4.0
pyarrow==1.0.0
pycodestyle==2.6.0
pycparser==2.20
pyflakes==2.2.0
PyNaCl==1.4.0
pyparsing==3.0.0b2
pytest==6.2.1
python-dateutil==2.8.1
python-slugify==4.0.1
pytz==2020.5
PyYAML==5.4
regex==2020.11.13
requests==2.25.1
retrying==1.3.3
s3transfer==0.3.4
sacrebleu==1.4.14
sacremoses==0.0.43
scikit-learn==0.23.2
scikit-optimize==0.8.1
scipy==1.4.1
sentencepiece==0.1.95
six==1.15.0
sortedcontainers==2.3.0
soupsieve==2.1
spacy==2.3.5
srsly==1.0.5
tblib==1.7.0
tensorboardX==2.1
text-unidecode==1.3
thinc==7.4.5
threadpoolctl==2.1.0
tokenizers==0.9.4
toml==0.10.2
toolz==0.11.1
torch==1.7.1
torchvision==0.8.2
tornado==6.1
tqdm==4.56.0
typing-extensions==3.7.4.3
urllib3==1.26.2
wasabi==0.8.0
xgboost==1.2.1
xmltodict==0.12.0
yacs==0.1.8
zict==2.0.0
zipp==3.4.0

[dai-ichiro]

My environment was:

Ubuntu 18.04 LTS
NVIDIA GTX 1080
CUDA toolkit 10.2
python 3.7.5

I got this error.

[dai-ichiro]

I got the same error. I solved it by changing python version from 3.7 to 3.6.

Hope this helps.