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Rewrite the KerasHub Getting Started Guide #1961

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merged 1 commit into from
Oct 20, 2024

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mattdangerw
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I had to pin nightly a few days book, look like there was some issue with ResNet on the latest version. This also needs a change to Keras that will not yet be on nightly until tonight

I had to pin nightly a few days book, look like there was some
issue with ResNet on the latest version. This also needs a change
to Keras that will not yet be on nightly until tonight
@divyashreepathihalli
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Hi Matt!! The resizing has been added back and the new presets have been updated. So is the nightly working fine for you?

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@divyashreepathihalli divyashreepathihalli left a comment

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Thanks Matt! left a few NIT comments.

This is just scratching the surface of what you can do with the KerasHub.

This guide shows a few of the high-level tasks that we ship with the KerasHub library,
but there are many tasks we did not cover here. Try [generating images with Stable
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we can maybe link to this page too here to point to the full list of models to try out - https://keras.io/api/keras_hub/models/

keras.mixed_precision.set_global_policy("mixed_float16")
1. Go to the [Gemma 2](https://www.kaggle.com/models/keras/gemma2) model page, and accept
the license at the banner at the top.
2. Generate an Kaggle API key by going to [Kaggle settings](https://www.kaggle.com/settings)
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NIT: a Kaggle

and clicking "Create New Token" button under the "API" section.
3. Inside your colab notebook, click on the key icon on the left hand toolbar. Add two
secrets: `KAGGLE_USERNAME` with your username, and `KAGGLE_KEY` with the API key you just
created. Make these secrets visible to the notebook you are running.
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NIT: Provide access to these secrets in your notebook when prompted while running the Colab.

KerasHub will use [tf.data](https://www.tensorflow.org/guide/data) as the default API for
running multi-threaded preprocessing on the CPU. `tf.data` is a powerful API for training
input pipelines that can scale up to complex, multi-host training jobs easily. Using it
does not restrict your choice of backend, a `tf.data.Dataset` can be as an iterator of
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NIT: can be used as an iterator

@fchollet fchollet merged commit a93f382 into keras-team:master Oct 20, 2024
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4 participants