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PROFESSIONAL-DATA-ENGINEER · Question #210
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
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Submitted by packet_pusher· Mar 30, 2026Operationalizing machine learning models
Question
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?
Options
- AUse Cloud TPUs without any additional adjustment to your code.
- BUse Cloud TPUs after implementing GPU kernel support for your customs ops.
- CUse Cloud GPUs after implementing GPU kernel support for your customs ops.
- DStay on CPUs, and increase the size of the cluster you're training your model on.
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Topics
#Cloud TPUs#custom TensorFlow ops#GPU kernels#hardware accelerators