zodios VS tensorflow

Zodios This is like tRPC but for REST APIs. Get an end-to-end typesafe developer experience in your Next.js apps

Zod is great. If you have an API that is also written in Typescript you can use zodios to make it type-safe from end to end.

@ecyrbe - Zodios | End-to-end typesafe REST API toolbox

Writing client APIs, that gives you autocompletion is time consuming. You allways endup writing the same kind of boilerplate code or have to use openapi code generators that are a pain to maintain. Zodios was created to make creating frontend (and optionally backend) API client a breeze. It's is an open source REST API toolbox with end-to-end typesafety. It allows you to create a REST API with a clean, intuitive and declarative syntax. It's best used with TypeScript, but it's also usable with pure JavaScript.

if you want to see it in practice, you can check some tests here for zodios library.

In the last article we have seen how to use zodios to declare custom REST APIs. As a reminder, Zodios is an open source HTTP client with type checking at runtime (zod validation) and also at compile time (typescript) without needing to write both types and validation schemas. You just need to write your API declaration once and you're done.

Zodios is a small open-source library that helps you declare your REST APIs in frontend applications thanks to Zod library.

# 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

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? :)