我正在try 创建一个运行tensorflow的隔离虚拟环境&;tf2onnx在vscode中使用jupyter笔记本.
tf2onnx packge建议使用python 3.7,而我的本地3.7.9版本通常可以很好地与tensorflow项目配合使用,因此我使用pyenv将本地和全局版本设置为3.7.9.
以下是我的设置过程:
python -m venv .venv
然后在vscode中启动新终端后:
pip install tensorflow==2.7.0
pip freeze > requirements.txt
在此之后,在我jupyter笔记本的一个单元格中,以下行失败
import tensorflow.keras as keras
例外情况:
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 If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc 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: 1. Downgrade the protobuf package to 3.20.x or lower. 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
此时,在我的需求文件中,protobuf
包版本显示为v4.21.0.在安装tensorflow之前,我try 将3.20.1版本预安装到虚拟环境中,但这没有产生任何效果.
以下是安装tensorflow后的完整需求文件:
absl-py==1.0.0
astunparse==1.6.3
cachetools==5.1.0
certifi==2022.5.18.1
charset-normalizer==2.0.12
flatbuffers==2.0
gast==0.4.0
google-auth==2.6.6
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
grpcio==1.46.3
h5py==3.7.0
idna==3.3
importlib-metadata==4.11.4
keras==2.7.0
Keras-Preprocessing==1.1.2
libclang==14.0.1
Markdown==3.3.7
numpy==1.21.6
oauthlib==3.2.0
opt-einsum==3.3.0
protobuf==4.21.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
requests==2.27.1
requests-oauthlib==1.3.1
rsa==4.8
six==1.16.0
tensorboard==2.9.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorflow==2.7.0
tensorflow-estimator==2.7.0
tensorflow-io-gcs-filesystem==0.26.0
termcolor==1.1.0
typing-extensions==4.2.0
urllib3==1.26.9
Werkzeug==2.1.2
wrapt==1.14.1
zipp==3.8.0