@lida2003,
Could you please confirm whether the difference in Accuracy & Loss has happened in GPU/CPU with both tensorflow v2.15, v2.16? Also I will also try to debug more on this issue and provide the resolution. Thank you!

@lida2003,
Could you please confirm whether the difference in Accuracy & Loss has happened in GPU/CPU with both tensorflow v2.15, v2.16?

• NVIDIA 2.15.0+nv24.03 GPU version faild 100%
• Colab is consistent with tensorflow document (But I don't know the version)

PS: Jetson Orin Nano 8GB, CPU&GPU shared memory

v2.16

Well, on the very begining, I have installed (pip binary installation) 2.16 CPU version on Jetson Orin Nano. Runing Keras-Fine-Tuning-Pre-Trained-Models without any resource warning. It might be the way CPU using swap area.

The result is also different from the document, See link below:

• Multiple executive warnings after switching tensorflow from 2.16.1 CPU to v60dp tensorflow==2.15.0+nv24.03 GPU version

When I switched to NVIDIA 2.15.0+nv24.03 GPU version: Tensorflow v2.16.1 GPU version local build on Jetson Orin Nano failed

• The resource issue, we have confirmed with NVIDIA.

Also I will also try to debug more on this issue and provide the resolution. Thank you!

There are also a copule of other things might be a clue for you. Here is a link on NVIDIA forum:

• Tensorflow v2.16.1 GPU version local build on Jetson Orin Nano failed
• Multiple executive warnings after switching tensorflow from 2.16.1 CPU to v60dp tensorflow==2.15.0+nv24.03 GPU version

Please take a look at those warnings and memory issue. I think we need a sanity check before software is packed for release (put on repo).

EDIT: Keep sync with NVIDIA feedback.

• NVIDIA claimed their binary compiled following tensorflow guide.
• Those warnings seems OK with NVIDIA 2.15 binary version.
• Inconsistency of NVIDIA 2.15.0+nv24.03 v.s. Colab v.s. Tensorflow Documentation

@lida2003,
As you mentioned, I tried to execute the transfer learning code on the colab with the Tensorflow v2.16, v2.15 and observed that the loss and Accuracy is the same as the document. I suspect there might be an issue with the GPU. I will re-check and deep dive on the same.

Thank you!

@tilakrayal

Please check Inconsistency of NVIDIA 2.15.0+nv24.03 v.s. Colab v.s. Tensorflow Documentation

• JetPack 6.0 DP and tf2.15.0+nv24.04 ==> BAD
• JetPack v6.0 + tensorflow2.15.0+nv24.05 ==>OK

@lida2003,
Could you please let me know if you are facing the same issue on TensorFlow v2.16 where keras3.0 is by default included. eras 3 is a full rewrite of Keras that enables you to run your Keras workflows on top of either JAX, TensorFlow, or PyTorch, and that unlocks brand new large-scale model training and deployment capabilities.

Also please try to comment this particular line and execute the code.
predictions = tf.nn.sigmoid(predictions)

Thank you!

Could you please let me know if you are facing the same issue on TensorFlow v2.16 where keras3.0 is by default included.

Sorry, I have met difficulties on build from source code (I have asked about some build steps, but I built without any luck)

• https://forums.developer.nvidia.com/t/how-to-compile-tensorflow-for-jetson-orin-nano/291321
• https://forums.developer.nvidia.com/t/tensorflow-v2-16-1-gpu-version-local-build-on-jetson-orin-nano-failed/291195

so I have used NVIDIA binary.

• 2.15 is their latest build for v60

• 2.15 is their latest build for v60dp

But I think it might be some issue with NVIDIA V60DP version, check this for details: Inconsistency of NVIDIA 2.15.0+nv24.03 v.s. Colab v.s. Tensorflow Documentation