How to build custom model using tf.keras on TensorFlow 2.x that supports SageMaker distributed training?

Asked Sep 17 '22 at 04:37

Active Sep 17 '22 at 05:34

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How to create custom models built using tf.keras on TensorFlow 2.x that support distributed training (multiple GPU instances) in Amazon SageMaker?

E.g. using Distributed Data Parallel Library (DDPL)?

The documentation mentioned that tf.keras is not supported by DDPL library, so that shouldn't be an option. I've seen examples of distributed training using Horovod: https://sagemaker-examples.readthedocs.io/en/latest/aws_sagemaker_studio/frameworks/keras_pipe_mode_horovod/keras_pipe_mode_horovod_cifar10.html

edited Sep 17 '22 at 05:34

asked Sep 17 '22 at 04:37

juvchan

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How to build custom model using tf.keras on TensorFlow 2.x that supports SageMaker distributed training?

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