Bump keras from 3.10.0 to 3.12.0 in /dockerfiles/test

[dependabot]

User description

Bumps keras from 3.10.0 to 3.12.0.

Release notes

Sourced from keras's releases.

Keras 3.12.0

Highlights

Keras has a new model distillation API!

You now have access to an easy-to-use API for distilling large models into small models while minimizing performance drop on a reference dataset -- compatible with all existing Keras models. You can specify a range of different distillation losses, or create your own losses. The API supports multiple concurrent distillation losses at the same time.

Example:

# Load a model to distill
teacher = ...
# This is the model we want to distill it into
student = ...
Configure the process
distiller = Distiller(
teacher=teacher,
student=student,
distillation_losses=LogitsDistillation(temperature=3.0),
)
distiller.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
Train the distilled model
distiller.fit(x_train, y_train, epochs=10)

Keras supports GPTQ quantization!

GPTQ is now built into the Keras API. GPTQ is a post-training, weights-only quantization method that compresses a model to int4 layer by layer. For each layer, it uses a second-order method to update weights while minimizing the error on a calibration dataset.

Learn how to use it in this guide.

Example:

model = keras_hub.models.Gemma3CausalLM.from_preset("gemma3_1b")
gptq_config = keras.quantizers.GPTQConfig(
    dataset=calibration_dataset,
    tokenizer=model.preprocessor.tokenizer,
    weight_bits=4,
    group_size=128,
    num_samples=256,
    sequence_length=256,
    hessian_damping=0.01,
    symmetric=False,
</tr></table> 

... (truncated)

Commits

• adbfd13 Add warning to set_backend and more detailed example. (#21787)
• 70598b7 Fix typo in Distiller docstring
• eecd34f Fix: keras.ops.quantile works with tf graph execution (#21782)
• c2bc6cf Suport keras.op.view() to view the same data bitwise at a new dtype (#21763)
• 10b51ce Make confusion metrics compilable. (#21775)
• 18f79d6 Fix negative index handling in MultiHeadAttention attention_axes (#21721)
• 18e0364 Support for extracting volume patches (#21759)
• dc5e42c fix sas metrics in jax fit (#21765)
• 1ba3b8f Fix discretization discrepancy (#21769)
• 53987a7 Document that set_backend requires re-importing keras. (#21764)
• Additional commits viewable in compare view

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PR Type

dependencies

---

Description

• Bump keras to 3.12.0

• Update locked hashes accordingly

• Keep dependency source via tensorflow

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Diagram Walkthrough

flowchart LR
  deps["locked-requirements.txt (keras)"] -- "version 3.10.0" --> old["Old lock hashes"]
  deps -- "bump to 3.12.0" --> new["New lock hashes"]

Loading

File Walkthrough

	Relevant files
Dependencies	locked-requirements.txt Bump keras and refresh lock hashes                                              dockerfiles/test/locked-requirements.txt Bump keras from 3.10.0 to 3.12.0 Replace hash pins for new version Keep comment indicating dependency via tensorflow +3/-3

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PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 1 🔵⚪⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Compatibility Verify that keras 3.12.0 remains compatible with the pinned tensorflow version and any keras-dependent tooling in this image. Check for API changes or additional dependencies introduced between 3.10.0 and 3.12.0 that could affect runtime. keras==3.12.0 \ --hash=sha256:02b69e007d5df8042286c3bcc2a888539e3e487590ffb08f6be1b4354df50aa8 \ --hash=sha256:536e3f8385a05ae04e82e08715a1a59988578087e187b04cb0a6fad11743f07f Hash Integrity Confirm the added hashes correspond to the exact wheels intended for your target platforms to avoid install failures in multi-arch CI builds. --hash=sha256:02b69e007d5df8042286c3bcc2a888539e3e487590ffb08f6be1b4354df50aa8 \ --hash=sha256:536e3f8385a05ae04e82e08715a1a59988578087e187b04cb0a6fad11743f07f

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[dependabot]

Superseded by #52.