django-silk VS Keras

But that's still not backend-specific SQL?

There should be an interface method for this. Why does psycopg call it mogrify?

https://django-debug-toolbar.readthedocs.io/en/latest/panels... :

> debug_toolbar.panels.sql.SQLPanel: SQL queries including time to execute and links to EXPLAIN each query

But debug toolbars mostly don't work with APIs.

https://github.com/django-query-profiler/django-query-profil... :

> Django query profiler - one profiler to rule them all. Shows queries, detects N+1 and gives recommendations on how to resolve them

https://github.com/jazzband/django-silk :

> Silk is a live profiling and inspection tool for the Django framework. Silk intercepts and stores HTTP requests and database queries before presenting them in a user interface for further inspection

you're using DRF, which DDT doesn't work well with. DDT is more for template rendered pages, not API responses. Have you brought in Django silk for monitoring this?

https://github.com/jazzband/django-silk/issues?q=is%3Aopen+is%3Aissue+label%3Abug

Are you using Django-silk? Googling for the error shows https://github.com/jazzband/django-silk/issues/374

Those docs are wrong. They still reference syncdb. Use the README at https://github.com/jazzband/django-silk.

Perhaps https://github.com/jazzband/django-silk?

If you're using Django as an API, without templates, django debug toolbar is useless because it requires templates. It won't work with DRF endpoints which means you miss out on profiling and SQL tracing. That's where django-silk shines.

You can also install django-silk to profile your app, and play around with different implementations.

https://github.com/ansible-community/ara/pull/358 https://github.com/b12io/orchestra/pull/830 https://github.com/batiste/django-page-cms/pull/210 https://github.com/carpentries/amy/pull/2130 https://github.com/celery/django-celery/pull/612 https://github.com/django-cms/django-cms/pull/7241 https://github.com/django-oscar/django-oscar/pull/3867 https://github.com/esrg-knights/Squire/pull/253https://github.com/Frojd/django-react-templatetags/pull/64 https://github.com/groveco/django-sql-explorer/pull/474 https://github.com/jazzband/django-silk/pull/550 https://github.com/keras-team/keras/pull/16073 https://github.com/ministryofjustice/cla_backend/pull/773 https://github.com/nitely/Spirit/pull/306 https://github.com/python/pythondotorg/pull/1987 https://github.com/rapidpro/rapidpro/pull/1610 https://github.com/ray-project/ray/pull/22396 https://github.com/saltstack/salt/pull/61647 https://github.com/Swiss-Polar-Institute/project-application/pull/483 https://github.com/UEWBot/dipvis/pull/216

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.

Keras documentation: https://keras.io/

3. Keras Keras is a high-level neural networks API written in Python that’s built on top of TensorFlow. It’s designed to enable fast experimentation with deep learning, allowing you to build and train models with just a few lines of code. If you’re new to deep learning or just want a more user-friendly interface, Keras is the way to go.