CUDA8 code fails to run on RTX2080Ti (Ubuntu 18.04)

Hi,

I have similar issue at JIT-compiled legacy CUDA8 code fails to run on RTX2080 (Ubuntu 16.04), but have different system environment.

I have an old s/w (code available), which was written for CUDA8 (and not compatible to work with CUDA9/10 without code changes). I had to change the GPU to RTX2080Ti. OS is Ubuntu 18.04.

Information of installed nvidia-driver is
NVRM version: NVIDIA UNIX x86_64 Kernel Module 440.64.00 Wed Feb 26 16:26:08 UTC 2020
GCC version: gcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04)

The code terminated with following warning message:
/home/sonic/Desktop/WVI/.env/local/lib/python2.7/site-packages/torch/cuda/init.py:95: UserWarning:
Found GPU0 GeForce RTX 2080 Ti which requires CUDA_VERSION >= 9000 for
optimal performance and fast startup time, but your PyTorch was compiled
with CUDA_VERSION 8000. Please install the correct PyTorch binary
using instructions from http://pytorch.org

warnings.warn(incorrect_binary_warn % (d, name, 9000, CUDA_VERSION))
/home/sonic/Desktop/WVI/.env/local/lib/python2.7/site-packages/torch/cuda/init.py:95: UserWarning:
Found GPU1 GeForce RTX 2080 Ti which requires CUDA_VERSION >= 9000 for
optimal performance and fast startup time, but your PyTorch was compiled
with CUDA_VERSION 8000. Please install the correct PyTorch binary
using instructions from http://pytorch.org

warnings.warn(incorrect_binary_warn % (d, name, 9000, CUDA_VERSION))

Is there anything I can do to make it running with CUDA 8.0?
Or my only option is to fix this code, to make it suitable for CUDA9?

Detailed information of packages is as follows. (The code is built in python 2.7)

cffi==1.11.2

cycler==0.10.0

decorator==4.1.2

fpdf==1.7.2

functools32==3.2.3.post2

imageio==2.2.0

matplotlib==2.0.2

moviepy==0.2.3.5

networkx==2.0

numpy==1.13.1

pillow==4.1.1

protobuf==3.4.0

pycparser==2.18

pyparsing==2.2.0

python-dateutil==2.6.0

pytz==2017.2

pywavelets==0.5.2

pyyaml==3.12

requests==2.19.1

scikit-image==0.13.1

scipy==0.18.1

six==1.10.0

subprocess32==3.2.7

tensorboardX==0.8

torch==0.3.1

torchvision==0.2.0

seaborn==0.9.0

pandas==0.20.3

prettytable==0.7.2