Topic 1 Question 127
You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?
Use Cloud TPUs without any additional adjustment to your code.
Use Cloud TPUs after implementing GPU kernel support for your customs ops.
Use Cloud GPUs after implementing GPU kernel support for your customs ops.
Stay on CPUs, and increase the size of the cluster you're training your model on.
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The correct answer is C TPU does not support custom C++ tensorflow ops https://cloud.google.com/tpu/docs/tpus#when_to_use_tpus
👍 57dhs2272020/04/01D: Cloud TPUs are not suited to the following workloads: [...] Neural network workloads that contain custom TensorFlow operations written in C++. Specifically, custom operations in the body of the main training loop are not suitable for TPUs.
👍 42aiguy2020/04/01CPUs -Quick prototyping that requires maximum flexibility -Simple models that do not take long to train -Small models with small effective batch sizes -Models that are dominated by custom TensorFlow operations written in C++ -Models that are limited by available I/O or the networking bandwidth of the host system GPUs -Models for which source does not exist or is too onerous to change -Models with a significant number of custom TensorFlow operations that must run at least partially on CPUs -Models with TensorFlow ops that are not available on Cloud TPU (see the list of available TensorFlow ops) -Medium-to-large models with larger effective batch sizes TPUs -Models dominated by matrix computations -Models with no custom TensorFlow operations inside the main training loop -Models that train for weeks or months -Larger and very large models with very large effective batch sizes
👍 8Azlijaffar2022/10/25
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