cmake-init-multi-target VS tensorflow

But also a lot of problems come from people not understanding that regardless of CMake and C++, how shared and static libraries work and why they work the way they work. For example, if your CMake project has multiple targets, one being the main export and another being just a "utilities" target of sorts, then you must do some extra work to make the main export target be installed properly when it is built as a static library. This has nothing to do with CMake or C++, but that fact that static libraries are "just" archives of object files that the linker will later roll into a "real" binary (shared library or executable). When you are creating a project you must account for propagating the "utility" target as well, because otherwise the consuming project will not get the code for the "utility" target that was linked to your main export PRIVATEly. I created an example repository on how to deal with this, because a Conan package maintainer was curious about why CMake was inserting $ genex into the installed export set.

# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")

[3]: https://github.com/tensorflow/tensorflow/graphs/contributors

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Right but that's not an inherent GPU determinism issue. It's a software issue.

https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.

Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.

Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.

and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:

Exactly what language do you think TensorFlow is written in? :)