Tensorflow 2.11 support #1016
Tensorflow 2.11 support #1016
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| @pytest.mark.skipif( | ||
| version.parse(tf.__version__) < version.parse("2.9.0"), | ||
| reason="tf.keras.optimizers.legacy is not available in TF <= 2.8", | ||
| ) |
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Since we need to use legacy optimizers in MultiOptimizer, I updated the incremental training notebook to use tf.keras.optimizers.legacy optimizers. And since this namespace was not available in TF 2.8, I opted to skip testing for TF 2.8, effectively dropping support for TF 2.8 for this notebook. Open questions: Is there a better way to handle this? It's likely not feasible to support all previous versions forever. Do we need an SLA or something for when to drop support for old dependency versions?
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Maybe the last 4 releases of TensorFlow or something would be reasonable. Might depend a bit on the level of complexity in supporting each one
| elif hasattr(optimizer, "variables") and callable( | ||
| optimizer.variables | ||
| ): # Tensorflow >= 2.11 | ||
| weights += optimizer_blocks.optimizer.variables() |
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if variables() was present in earlier versions of tensorflow (2.8, 2.9, 2.10) and equivalent to .weights ? then we might be able to simplify to be only this line?
Goals ⚽
Support Tensorflow 2.11
Implementation Details 🚧
tf.keras.optimizers.legacy. Most of the changes in this PR involve checking the Tensorflow version and using the correct namespace for the version.MultiOptimizeronly works with legacy optimizers, and it does not work with newly introduced Keras optimizers. This PR opted to throw an error if the user tries to use it with the new optimizers. Supporting new optimizers in MultiOptimizer is left as future work.Testing Details 🔍
Needs horovod 0.27. Related: NVIDIA-Merlin/Merlin#883
For GPU test, manually built docker image with NVIDIA-Merlin/Merlin#883 and ran tests.