2

I am trying to standardize our deployment workflow for machine vision systems. So we were thinking of the following workflow.

Deployment workflow

So, we want to create the prototype for the same, so we followed the workflow. So, there is no problem with GCP operation whatsoever but when we try to export models, which we train on the vertexAI it will give three models as mentioned in the workflow which is:

  1. SaveModel
  2. TFLite
  3. TFJS

and we try these models to convert into the ONNX model but we failed due to different errors.

  1. SaveModel - Always getting the same error with any parameter which is as follows Error in savemodel I tried to track the error and I identified that the model is not loading inside the TensorFlow only which is wired since it is exported from the GCP vertexAI which leverages the power of TensorFlow.
  2. TFLite - Successfully converted but again the problem with the opset of ONNX but with 15 opset it gets successfully converted but then NVIDIA tensorRT ONNXparser doesn't recognize the model during ONNX to TRT conversion.
  3. TFJS - yet not tried.

So we are blocked here due to these problems.

We can run these models exported directly from the vertexAI on the Jetson Nano device but the problem is TF-TRT and TensorFlow is not memory-optimized on the GPU so the system gets frozen after 3 to 4 hours of running.

We try this workflow with google teachable machine once and it workout well all steps are working perfectly fine so I am really confused How I conclude this full workflow since it's working on a teachable machine which is created by Google and not working on vertexAI model which is again developed by same Company.

Or am I doing Something wrong in this workflow? For the background we are developing this workflow inside C++ framework for the realtime application in industrial environment.

Umang Patel
  • 21
  • 2

0 Answers0