Python dependencies for TensorRT container broken

Accelerated Computing NGC GPU Cloud Docker and NVIDIA Docker
#1

Based on https://ngc.nvidia.com/catalog/containers/nvidia:tensorrt, I am trying to use the TensorRT NGC container. However, I cannot get the Python dependencies installed due to import errors.

Procedure:

docker run --gpus all -it --rm nvcr.io/nvidia/tensorrt:19.09-py3

within the Docker container, run as instructed:

/opt/tensorrt/python/python_setup.sh

Results (only error pasted):

Setting up uff-converter-tf (6.0.1-1+cuda10.1) ...
Traceback (most recent call last):
  File "/usr/lib/python3.6/dist-packages/uff/converters/tensorflow/conversion_helpers.py", line 18, in <module>
    from tensorflow import GraphDef
ImportError: cannot import name 'GraphDef'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/usr/lib/python3.6/dist-packages/uff/__init__.py", line 2, in <module>
    from uff.converters.tensorflow.conversion_helpers import from_tensorflow  # noqa
  File "/usr/lib/python3.6/dist-packages/uff/converters/tensorflow/conversion_helpers.py", line 23, in <module>
    https://www.tensorflow.org/install/""".format(err))
ImportError: ERROR: Failed to import module (cannot import name 'GraphDef')

It does work using tag 18.12-py3, although a warning is given that tf.GraphDef is deprecated, please use tf.compat.v1.GraphDef instead.

#2

Hello, this is due uff converter not supporting TF version 2.0. Local workaround is to install tensorflow version 1.15 or 1.14. This will be resolved in a future container.

#3

Why is this not in the container release notes?

https://docs.nvidia.com/deeplearning/sdk/tensorrt-container-release-notes/rel_19-09.html#rel_19-09

Should at least be in the known issues section

1 Like
#4

Hi siegfried,

This issue didn’t appear until after the container was released.

pip install tensorflow (without a version specified) will install the latest stable version of tensorflow, and tensorflow==2.0 came out after the container/release notes were published.

As nworkerxz9q8, this will be fixed in the future by pinning the version to be < 2.0.