AI: Upgrade TensorFlow from v1.15.2 to v2.18.0

[lastzero]

As a Go developer, I want to upgrade to the latest version of the TensorFlow library, so that I get all the latest features and can use the latest hardware for faster inference.

We are currently using TensorFlow 1.15.2 and would like to upgrade to version 2.x (or later):

• https://github.com/galeone/tfgo
• https://github.com/tensorflow/tensorflow

Since the TensorFlow project team has decided to remove the Go library wrapper originally available in the tensorflow/tensorflow repository, the galeone/tfgo project provides an alternative Go wrapper for TensorFlow v2. However, it may not support the very latest v2 release or needs to be upgraded first to support it (as it is an open-source project, you can submit a PR with the necessary changes).

Related Issues:

• Config: Add support for AI inference with Coral Edge TPU USB devices #536
• AI: Integrate a model for Optical Character Recognition (OCR) #907
• AI: API to connect to external services for Image tagging / object recognition #1090
• AI: Integrate Florence 2 Vision AI for Auto Caption #4356
• AI: Generate captions with Clip Interrogator #3438
• Search: Find image content using CLIP #1287 #2005

Acceptance Criteria:

• Identify all major steps necessary to upgrade our Docker image and the application code to TensorFlow 2 (e.g. check documentation of TensorFlow Go library)
• Note that we also support arm64, not just x86, so we need to be able to build TF2 for/on the Raspberry Pi 3/4 (64bit Linux)
• Test our existing models with TensorFlow 2 and make sure they continue to work as expected (run existing unit tests and do manual acceptance tests)
• Document your findings here in this ticket plus add a summary to our wiki (stream of issue comments can be confusing to use as todo list to perform the actual upgrade later)

Our Developer Guide explains how to set up a local build environment. You are welcome to contact us via email or chat if you would like to work on this and need additional guidance to get started.

[guysoft]

I can help with the Rpi build.
You could also use qemu to build, but it might take days, so I would recommend just using a Pi.

[lastzero]

Good to know qemu is so slow! So not an option for us in practice.

We have a Raspberry Pi 4 with 4 GB RAM and an Nvidia Jetson in our lab for development.

[guysoft]

Good to know qemu is so slow! So not an option for us in practice.

Its usually OK, but tensorflow took 7 hours in Raspberrypi 4 running on two threads. If you have a super strong intel machine it might be worth it, but I'd recommend using a Pi just for now, since its a once-per-version build.

[lastzero]

We're also building the photoprism-arm64 image on the Raspberry Pi 4 using your 64bit Ubuntu image at the moment. It's reasonable fast, so the question is if it's worth finding a different (cloud based) solution.

Edit: Would qemu profit from more cores? We think about getting an AMD Threadripper...

[guysoft]

You could also use multi-arch to build it using docker hub. If you use a local x86_64 machine it might also build.

[savegame]

i built your project in FreeNAS Jail (FreeBSD 11.2 at this moment) , but it has only Tensorflow 1.14 :) it would be sad if i cant build tensorflow 2 in freebsd ... is it possible make it compatible with tensorflow v1 and v2 in same time?

[lastzero]

Where do you have TF 1.14 from? We'll probably stick with it until after our first stable release.

[savegame]

i build TF 1.14 from FreeBSD ports

[lastzero]

i build TF 1.14 from FreeBSD ports

Can you check for us if / when they have TF2? I've used FreeBSD many years ago. You're also welcome to write a howto for our docs.

[savegame]

i'll try later write some howto about FreeBSD (especially FreeNAS jail) (dont have much time 😞 )

[McQueen2063]

@savegame Sorry for being a bit off topic in this thread, but I try to build photoprism under FreeBSD as well (that is, a FreeBSD 12.1 jail, not inside FreeNAS, but this shouldn't matter).
I did install tensorflow from pkg (py37-tensorflow-1.14.0_3), but gmake all later fails with:

go build -v ./...
github.com/tensorflow/tensorflow/tensorflow/go
# github.com/tensorflow/tensorflow/tensorflow/go
ld: error: unable to find library -ltensorflow
clang: error: linker command failed with exit code 1 (use -v to see invocation)
gmake: *** [Makefile:59: dep-go] Error 2

Well, yes, there is no libtensorflow.so.
How did you proceed from there?

[lastzero]

You might need to build it from source. Takes a couple of hours to compile though.

[McQueen2063]

I'm building /usr/ports/science/py-tensorflow right now. I wonder why the outcome of building the port is different than the pkg, but I'll give it a try anyways :)