H13-311_V3.5 · Question #119
TensorFlow2.0 not support tensor in GPU with CPU Transfer between each other.
The correct answer is B. FALSE. B is correct because TensorFlow 2.0 fully supports transferring tensors between CPU and GPU memory. You can explicitly move tensors using tf.device() context managers or rely on TensorFlow's automatic device placement, which handles cross-device data movement transparently. Why…
Question
TensorFlow2.0 not support tensor in GPU with CPU Transfer between each other.
Options
- ATRUE
- BFALSE
How the community answered
(37 responses)- A27% (10)
- B73% (27)
Explanation
B is correct because TensorFlow 2.0 fully supports transferring tensors between CPU and GPU memory. You can explicitly move tensors using tf.device() context managers or rely on TensorFlow's automatic device placement, which handles cross-device data movement transparently.
Why A is wrong: The statement is a false claim. TensorFlow has supported GPU↔CPU tensor transfers since its early versions, and TF 2.0 made this even more seamless with eager execution - tensors can be moved between devices on demand, and operations automatically handle the necessary memory copies.
Memory tip: Think of TensorFlow 2.0 as a traffic manager between CPU and GPU - it doesn't block the road, it coordinates movement. The "2.0" in the question is a red herring; device interoperability has always been a core TensorFlow feature, not something removed or restricted in later versions.
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