Thanks for your kind response. I am working on PYNQ z_1 and I am trying to process a pre-designed ConvNet on it. I have already installed TensorFlow version 1.1, but the main problem is that I need a newer version to be compatible with keras. The last thing which I have done is to use Bazel build system to build Tensorflow from source, but I am stuck till now in this stage (limited card storage so I will use another bigger one) and I won’t know whether it will succeed or not.
It seems like you are on track for getting this to work! I honestly don’t see why you should not be able to succeed. And you can always avail of some help here in case you can’t make it.
In general, as the Pynq-Z1 shares the same architecture of the Raspberry Pi, I can confirm now you should be able to find what you need by looking for solutions for the RPI (as I did in my initial search).
Unfortunately, it is not easy and I didn’t found anyone (till now) did it. The Raspberry works on Arm 64, while PYNQ uses 32 bit and this cause a big problem as the tensorflow versions are 64 bits. Right now I am trying to build it using Bazel, but still no success.
Yeah, I have tried to build bazel on arm as well - no success - which means you probably cannot install tf on arm. I have been able to build bazel on aarch64 however. A lot of users are asking for installation on arm in the tensorflow community, but there is no solution there yet.
This is a fairly common question. The Pynq-Z2 has a 32-bit arm processor on it, so if you need to run tensorflow on it you need to compile it from source or find a project that ships these binaries for that architecture. I assume if you want something that works for a raspberry pi, for example, shouldn’t be too far fetched to port into the Pynq-Z2.