I am trying to use tensorflow C++ API to run tf-trt graphs, and my problem is that it takes around 5 minutes to load the graph (in function TF_LoadSessionFromSavedModel, it stucks at this step : “Adding visible gpu devices: 0”)
I first had the problem on python API and C++ API. I found a solution for the python api (I followed this steps : JetPack-4.5), but couldn’t find one for C++ API.
The default value of the logger is to WARNING. I don’t see any difference in the log messages when I change its value (with tf.get_logger().setLevel()). However I was able to change the log level using “export TF_CPP_MIN_LOG_LEVEL=0”, but only with the python api. It does not change the log level when I use the c++ api.
Since JetPack 4.5 is released for a while, could you try our latest JetPack 4.6.2 or JetPack 5 instead?
You can find the corresponding TF-TRT package below:
I installed the jetpack 5.0.2, with tensorflow 2.9.1. It works well on python, so I am now searching to use it in c++. I wasn’t able to find the prebuilt libraries. Is it available somewhere ?
If not I will try to build it from source
I can’t build tensorflow c_api on my jetson nano with jetpack 5.0.2 because the disk space is too small.
So I tried do it with the jetpack 4.6.2. The problem is that I can’t find a version of tensorflow built with tensorrt.
Until today I was installing tensorflow using this command : python3 -m pip install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v$JP_VERSION tensorflow==$TF_VERSION+nv$NV_VERSION
But I can’t find any version available for the jetpack 4.6.2 ERROR: Could not find a version that satisfies the requirement tensorflow==2.7.0+nv22.01 (from versions: 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3)
So I installed it with this command : python3 -m pip install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v462 tensorflow
Which installed the version 2.10.0rc3
Then when I try to use it with tensorrt I have this error : RuntimeError: Tensorflow has not been built with TensorRT support.