chore(deps): bump the pip group across 3 directories with 1 update

[dependabot]

Bumps the pip group with 1 update in the / directory: keras.
Bumps the pip group with 1 update in the /.devcontainer directory: keras.
Bumps the pip group with 1 update in the /binder directory: keras.

Updates keras from 3.12.0 to 3.13.1

Release notes

Sourced from keras's releases.

v3.13.1

Bug Fixes & Improvements

• General
  • Removed a persistent warning triggered during import keras when using NumPy 2.0 or higher. (#21949)
• Backends
  • JAX: Fixed an issue where CUDNN flash attention was broken when using JAX versions greater than 0.6.2. (#21970)
• Export & Serialization
  • Resolved a regression in the export pipeline that incorrectly forced batch sizes to be dynamic. The export process now correctly respects static batch sizes when defined. (#21944)

Full Changelog: keras-team/keras@v3.13.0...v3.13.1

v3.13.0

BREAKING changes

Starting with version 3.13.0, Keras now requires Python 3.11 or higher. Please ensure your environment is updated to Python 3.11+ to install the latest version.

Highlights

LiteRT Export

You can now export Keras models directly to the LiteRT format (formerly TensorFlow Lite) for on-device inference. This changes comes with improvements to input signature handling and export utility documentation. The changes ensure that LiteRT export is only available when TensorFlow is installed, update the export API and documentation, and enhance input signature inference for various model types.

Example:

import keras
import numpy as np
1. Define a simple model
model = keras.Sequential([
keras.layers.Input(shape=(10,)),
keras.layers.Dense(10, activation="relu"),
keras.layers.Dense(1, activation="sigmoid")
])
2. Compile and train (optional, but recommended before export)
model.compile(optimizer="adam", loss="binary_crossentropy")
model.fit(np.random.rand(100, 10), np.random.randint(0, 2, 100), epochs=1)
3. Export the model to LiteRT format
model.export("my_model.tflite", format="litert")
print("Model exported successfully to 'my_model.tflite' using LiteRT format.")

GPTQ Quantization

• Introduced keras.quantizers.QuantizationConfig API that allows for customizable weight and activation quantizers, providing greater flexibility in defining quantization schemes.

... (truncated)

Commits

• 8914427 Patch release commits for 3.13.1 (#22005)
• 986ff97 Update release version and comment orbax checkpoint (#21934)
• ca23fce Refactors AbsMaxQuantizer to accept axis in call (#21931)
• 1a9893f Adds Serialization Support for QuantizationConfig based quantized models (#21...
• 86bfab4 More OpenVINO Numpy Operations (#21925)
• f48f480 Add adaptive pooling (1D, 2D, 3D) support across JAX, NumPy, TensorFlow, and ...
• 0771c80 Fix ops.tile shape inference issue on TensorFlow backend (#21860)
• 024c96d Extended fix OOM Issue #21634 on Keras side (#21755)
• 71f4997 Introduces QuantizationConfig for fine-grained quantization control (#21896)
• 3989d64 Fix fake quant gradient output shape and use jax.grad for tests. (#21927)
• Additional commits viewable in compare view

Updates keras from 3.12.0 to 3.13.1

Release notes

Sourced from keras's releases.

v3.13.1

Bug Fixes & Improvements

• General
  • Removed a persistent warning triggered during import keras when using NumPy 2.0 or higher. (#21949)
• Backends
  • JAX: Fixed an issue where CUDNN flash attention was broken when using JAX versions greater than 0.6.2. (#21970)
• Export & Serialization
  • Resolved a regression in the export pipeline that incorrectly forced batch sizes to be dynamic. The export process now correctly respects static batch sizes when defined. (#21944)

Full Changelog: keras-team/keras@v3.13.0...v3.13.1

v3.13.0

BREAKING changes

Starting with version 3.13.0, Keras now requires Python 3.11 or higher. Please ensure your environment is updated to Python 3.11+ to install the latest version.

Highlights

LiteRT Export

You can now export Keras models directly to the LiteRT format (formerly TensorFlow Lite) for on-device inference. This changes comes with improvements to input signature handling and export utility documentation. The changes ensure that LiteRT export is only available when TensorFlow is installed, update the export API and documentation, and enhance input signature inference for various model types.

Example:

import keras
import numpy as np
1. Define a simple model
model = keras.Sequential([
keras.layers.Input(shape=(10,)),
keras.layers.Dense(10, activation="relu"),
keras.layers.Dense(1, activation="sigmoid")
])
2. Compile and train (optional, but recommended before export)
model.compile(optimizer="adam", loss="binary_crossentropy")
model.fit(np.random.rand(100, 10), np.random.randint(0, 2, 100), epochs=1)
3. Export the model to LiteRT format
model.export("my_model.tflite", format="litert")
print("Model exported successfully to 'my_model.tflite' using LiteRT format.")

GPTQ Quantization

• Introduced keras.quantizers.QuantizationConfig API that allows for customizable weight and activation quantizers, providing greater flexibility in defining quantization schemes.

... (truncated)

Commits

• 8914427 Patch release commits for 3.13.1 (#22005)
• 986ff97 Update release version and comment orbax checkpoint (#21934)
• ca23fce Refactors AbsMaxQuantizer to accept axis in call (#21931)
• 1a9893f Adds Serialization Support for QuantizationConfig based quantized models (#21...
• 86bfab4 More OpenVINO Numpy Operations (#21925)
• f48f480 Add adaptive pooling (1D, 2D, 3D) support across JAX, NumPy, TensorFlow, and ...
• 0771c80 Fix ops.tile shape inference issue on TensorFlow backend (#21860)
• 024c96d Extended fix OOM Issue #21634 on Keras side (#21755)
• 71f4997 Introduces QuantizationConfig for fine-grained quantization control (#21896)
• 3989d64 Fix fake quant gradient output shape and use jax.grad for tests. (#21927)
• Additional commits viewable in compare view

Updates keras from 3.12.0 to 3.13.1

Release notes

Sourced from keras's releases.

v3.13.1

Bug Fixes & Improvements

• General
  • Removed a persistent warning triggered during import keras when using NumPy 2.0 or higher. (#21949)
• Backends
  • JAX: Fixed an issue where CUDNN flash attention was broken when using JAX versions greater than 0.6.2. (#21970)
• Export & Serialization
  • Resolved a regression in the export pipeline that incorrectly forced batch sizes to be dynamic. The export process now correctly respects static batch sizes when defined. (#21944)

Full Changelog: keras-team/keras@v3.13.0...v3.13.1

v3.13.0

BREAKING changes

Starting with version 3.13.0, Keras now requires Python 3.11 or higher. Please ensure your environment is updated to Python 3.11+ to install the latest version.

Highlights

LiteRT Export

You can now export Keras models directly to the LiteRT format (formerly TensorFlow Lite) for on-device inference. This changes comes with improvements to input signature handling and export utility documentation. The changes ensure that LiteRT export is only available when TensorFlow is installed, update the export API and documentation, and enhance input signature inference for various model types.

Example:

import keras
import numpy as np
1. Define a simple model
model = keras.Sequential([
keras.layers.Input(shape=(10,)),
keras.layers.Dense(10, activation="relu"),
keras.layers.Dense(1, activation="sigmoid")
])
2. Compile and train (optional, but recommended before export)
model.compile(optimizer="adam", loss="binary_crossentropy")
model.fit(np.random.rand(100, 10), np.random.randint(0, 2, 100), epochs=1)
3. Export the model to LiteRT format
model.export("my_model.tflite", format="litert")
print("Model exported successfully to 'my_model.tflite' using LiteRT format.")

GPTQ Quantization

• Introduced keras.quantizers.QuantizationConfig API that allows for customizable weight and activation quantizers, providing greater flexibility in defining quantization schemes.

... (truncated)

Commits

• 8914427 Patch release commits for 3.13.1 (#22005)
• 986ff97 Update release version and comment orbax checkpoint (#21934)
• ca23fce Refactors AbsMaxQuantizer to accept axis in call (#21931)
• 1a9893f Adds Serialization Support for QuantizationConfig based quantized models (#21...
• 86bfab4 More OpenVINO Numpy Operations (#21925)
• f48f480 Add adaptive pooling (1D, 2D, 3D) support across JAX, NumPy, TensorFlow, and ...
• 0771c80 Fix ops.tile shape inference issue on TensorFlow backend (#21860)
• 024c96d Extended fix OOM Issue #21634 on Keras side (#21755)
• 71f4997 Introduces QuantizationConfig for fine-grained quantization control (#21896)
• 3989d64 Fix fake quant gradient output shape and use jax.grad for tests. (#21927)
• Additional commits viewable in compare view

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[Copilot]

Pull request overview

This PR updates the Keras dependency from version 3.12.0 to 3.13.1 across three different requirement files in the repository. The update includes bug fixes and improvements from the Keras team, but introduces a breaking change requiring Python 3.11 or higher.

Changes:

• Updated Keras from 3.12.0 to 3.13.1 in all three requirements files
• Changes apply to the main requirements, devcontainer, and binder environments

Reviewed changes

Copilot reviewed 2 out of 3 changed files in this pull request and generated no comments.

File	Description
requirements.txt	Updated Keras to 3.13.1 for the main project environment
.devcontainer/requirements.txt	Updated Keras to 3.13.1 for the devcontainer environment
binder/requirements.txt	Updated Keras to 3.13.1 for the binder environment

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