1.pip install -U protobuf
conda install scikit-learn
2. jupyterLab生成一个新的kernel：
conda create -n kgat5 python=3.7.2 ipykernel
python -m ipykernel install --name kgat5 --display-name kgat5 --user
3.pip install tensorflow-gpu=1.12.0
安装后import tensorflow as tf报错，按照如下修改后，还是报错
于是，提升了tf的版本号，还是1.x：
pip install tensorflow-gpu=1.15.0
pip install tensorflow_gpu-1.15.0-cp37-cp37m-manylinux2010_x86_64.whl
4.报错：
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
-
Downgrade the protobuf package to 3.20.x or lower.
-
Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
解决方案：
pip install protobuf==3.20.*
5.CPU训练：
gpu-id=-1
6.报错：
2023-08-01 10:22:25.625741: F tensorflow/stream_executor/lib/statusor.cc:34] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
Aborted (core dumped)
解决方案：
查看gpu使用情况： nvidia-smi
修改默认gpu-id=1