elbencho VS tensorflow

You can also use elbencho (https://github.com/breuner/elbencho) which is functionally equivalent to IOR but a little more flexible.

I won't speak for him but Tallis is definitely aware (see his recent article on updated testing) and so are others. I regularly work with Sean Webster of Tom's Hardware (/u/TurboSSD) and we spend an insane amount of time working around SLC cache response and discussing it on my discord. These guys often use different tools (e.g. Iometer vs. FIO) although the one I've been playing with moving forward is elbencho. Either way, it's something that takes up a lot of time in SSD reviewer circles since it's a relatively tightknit group. It's a challenging topic especially as SLC caching algorithms are getting more complex, with behavioral and performance-based profiles and reviewers already doing preconditioning.

# 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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Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.

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? :)