Hello,
I want to use TF-TRT Python API to optimize the graph,
and then use TF C++ API for deployment on NVIDIA Xavier.
Is TF C++ API capable of running TF-TRT optimized graph?
Also, what is the preferred way of deploying TRT optimized model on Jetson?
According to item 8.1 of Developer Guide :: NVIDIA Deep Learning TensorRT Documentation
“Note: The UFF Parser which is used to parse a network in UFF format will be deprecated in a future release. The recommended method of importing TensorFlow models to TensorRT is using TensorFlow with TensorRT (TF-TRT).”
While item 7.2 says to use TRT C++ API with UFF as an intermediate format.
Thanks.
Now if I use the TF-TRT api on my workstation with
trt.create_inference_graph
Is the model serialized?
Can I deserialize on Xavier directly with TRT C++ API?
you write :
Note: Serialized engines are not portable across platforms or TensorRT versions. Engines are specific to the exact GPU model they were built on (in addition to platforms and the TensorRT version).
If not can you please indicate the steps?
a. how/where to build the engine?
b. how/where to serailize?
https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html#tensorrt-plan
states that “This feature requires that your entire model converts to TensorRT”.
What about nets that don’t convert entirely, like SSD+Mobilenet?
Can I just load and run TF-TRT optimized graph through TF C++ API in a standard way (I know that I need to build TF C++ API from source on Xavier).
want to ask the same question " Can I just load and run TF-TRT optimized graph through TF C++ API in a standard way ?" and any update on C++ sample for TF-TRT?
Is this example already online?
Actually, I ask this because my model did not work with pure TensorRT 5 like on the jetson nano, and I wanted to try with tensorflow.
Yeah I am starting to look into TF-TRT too, I was able to convert/build an object detection (od api2) model, but I cant see any clear c++ sample for infering it.