sourcery VS tensorflow

In my experience, the developer tools that really catch on do so via word of mouth. For example, our whole team recently adopted https://sourcery.ai/ (not an ad) because one developer tried it and hyped it up to everyone else who also liked it.

To those that wish to automate a subset of these conventions, there is a tool called Sourcery[1] that I, personally, am a huge fan of! Not only does it have a large set of default rules[2], but it can also allow you to write your own rules that may be specific to your team or organization, and as mentioned it can enable you to follow Google's Python style guide as well[3].

There are some refactorings that Sourcery suggest that I don't agree with myself, namely the usage of 'contextlib.suppress'[4] as I don't like to introduce an additional 'import' statement just to do something so trivial. I wish Sourcery would add the relevance of having possibly too many 'import' statements as a heuristic.

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[1]: https://sourcery.ai/

[2]: https://docs.sourcery.ai/Reference/Default-Rules/ (expand the sub-pages)

[3]: https://docs.sourcery.ai/Reference/Optional-Rules/gpsg/

During development, tools like Sourcery could show you improvements for code quality.

One of the first tools I install when setting up my Python dev environment is Sourcery. This still uses AI/ML to suggest code improvements to your Python code, but unlike GitHub's Copilot, it won't write code for you.

"Sourcery" exists

Looks downright wicked https://sourcery.ai/

As a bit of trivia, https://sourcery.ai/ will replace

I guess you have to signup in their website sourcery.ai. I actually don't use sourcery, I don't know the details on how to get the token.

When I'm writing Python, one of my favorite tools is [Sourcery](https://sourcery.ai/). Are there any similar tools for Go? What else do you recommend?

# 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 ⭐

Explore here.

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? :)