xorbits VS tensorflow

Here’s our project: https://github.com/xprobe-inc/xorbits

Hey everyone, we are excited to announce our new project, Xorbits, a scalable data science framework that aims to scale the entire Python data science world.

My suggestion is to use some parallel computing framework like Xorbits. The framework will parallel your workload automatically. For data processing tasks, just use xorbits.pandas or xorbits.numpy, and you can run almost any python workload with xorbits.remote.

If you are interested in learning more about Xorbits, please visit our project's Github for more information: https://github.com/xprobe-inc/xorbits

Xorbits can be an ideal solution for these issues. Xorbits is a scalable Python data science framework that aims to scale the Python data science stack while keeping the API compatibility. You can get an out-of-box performance gain by changing `import pandas as pd` to `import xorbits.pandas as pd`.

# 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? :)