LucidMQ VS tensorflow

I'm using Cap'N Proto in a message broker application(LcuidMQ) I'm building for serialization. It has allowed me to created client applications rather quickly. There are some quirks can be difficult to wrap your head around, but once you understand it is really solid.

There are some difference between the language libraries and documentation can be lacking around those language specific solutions. I'm hoping to add blog articles and or contribute back to the example of these repositories to help future users in the future.

Check out my repo here for how I use it across Rust and Python, with Golang coming soon: https://github.com/lucidmq/lucidmq

I've been working on something that may fit your criteria. The API is kafka-like and it's whole goal is to provide an easy to use local-first stream datastore/data-engine.

It currently only has support for Rust and Python clients but working on expanding to node(JS) and C/C++ in the future.

https://github.com/lucidmq/lucidmq

If you're interested, check out the repo. Feedback is much appreciated to make this useful: https://github.com/bdkiran/lucidmq

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

To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐

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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? :)

You can use Tensorflow's deep learning API for this.