Hi Dhruv,
Many thanks for following up on this. Only reading your question did I note my typo: it wasn’t supposed to be “buffer size” but rather “batch size”. Apologies for the confusion, everyone!
Ultimately, what I am looking to know is, how can I optimise the amount of data flowing into my GPU that maximises GPU usage while not overusing the CPU to transmit the data. What I have noted is that for the same batch size in the same dataset, normal tensorflow in CUDA and tensorflow-metal in my (AMD GPU) Mac lead to different GPU usages. This is expected, of course. But I wanted to know is, whether there is any guideline or rule of thumb that can us users set a batch size that can utilise resources more efficiently.
Please let me know if I can further clarify the question. And thanks again for the follow up, this is much appreciated!