ara VS Keras

Look into AWX as an alternative to Tower. If you just want better reporting on runs, check out ARA or callback plugins.

Also worth considering is ARA for playbook reporting, and then whatever you want for orchestration (Jenkins, Azure Devops, Rundeck, etc).

Why not use ara?

For general recording of playbook activity with a web dashboard, ARA works really well.

If you don’t want any UI / access control then you can also look at ARA - https://ara.recordsansible.org This works as a callback plugin to capture the job run data.

I'm not sure how to interpret running 100 debug messages (https://github.com/ansible-community/ara/blob/master/tests/integration/benchmark_tasks.yaml) into real life performance. Mitogen's Benchmark used either 100 times a "hostname" command on the target machine (https://github.com/mitogen-hq/mitogen/blob/master/tests/ansible/bench/loop-100-items.yml) or running the DebOps project (https://github.com/debops/debops-playbooks/blob/master/playbooks/common.yml) for some sorta real-world module usage.

Reach out on Mastodon or see this issue on GitHub.

There has been discussions about a prometheus exporter for monitoring, metrics and eventually fancy graphs in grafana but work on this has not started yet: https://github.com/ansible-community/ara/issues/177

Keras

As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.

After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.

All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467

You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?

Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

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I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.