tensorflow installation on python docker-CodePudding

Running in a fresh python:3.9 docker image.

pip install tensorflow==2.8.0

import tensorflow

I get the error

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.9/site-packages/tensorflow/__init__.py", line 37, in <module>
    from tensorflow.python.tools import module_util as _module_util
  File "/usr/local/lib/python3.9/site-packages/tensorflow/python/__init__.py", line 37, in <module>
    from tensorflow.python.eager import context
  File "/usr/local/lib/python3.9/site-packages/tensorflow/python/eager/context.py", line 29, in <module>
    from tensorflow.core.framework import function_pb2
  File "/usr/local/lib/python3.9/site-packages/tensorflow/core/framework/function_pb2.py", line 16, in <module>
    from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
  File "/usr/local/lib/python3.9/site-packages/tensorflow/core/framework/attr_value_pb2.py", line 16, in <module>
    from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
  File "/usr/local/lib/python3.9/site-packages/tensorflow/core/framework/tensor_pb2.py", line 16, in <module>
    from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
  File "/usr/local/lib/python3.9/site-packages/tensorflow/core/framework/resource_handle_pb2.py", line 16, in <module>
    from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
  File "/usr/local/lib/python3.9/site-packages/tensorflow/core/framework/tensor_shape_pb2.py", line 36, in <module>
    _descriptor.FieldDescriptor(
  File "/usr/local/lib/python3.9/site-packages/google/protobuf/descriptor.py", line 560, in __new__
    _message.Message._CheckCalledFromGeneratedFile()
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If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc 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)

Why is that? It does not occur in Windows.

CodePudding user response：

I had a similar issue with ubuntu:20.04 image and Tensorflow 2.3.0. As suggested, I downgraded to protobuf 3.19.0 (added protobuf==3.19.0 to requirements.txt file) and the issue went away