How could we recognize that you want whole processing to be done purely in-browser? Anyway, just google for "webassembly barcode reader". There are implementations available.

I would say, if it is not a very wild environment, nn may be excessive.

Quick discovery: https://note.nkmk.me/en/python-opencv-barcode/

Try yolo tiny. Must detect well if it is trained properly. But do you think you need a nn model? Scanners read barcodes without neural networks.

Ram performance also may be affected by meltdown, spectre patches.

>Oh I thought maybe he is going for distributed learning since he has access to 2 GPUs. In that case MPI has some overhead simply because it has to replicate, scatter and gather all the gradients per batch every epoch.

It looks like no; they speculated about the internal design of A100.

Why so? GPUs are passed to VM in a pass-through mode, so no significant performance pitfails must happen. I recommend OP to look at CPU %steal, nvidia-smi (maybe it is A100 1/7 shard, not a full GPU). Run a single and multithreaded sysbench to compare CPU and RAM. Also, PCI-E generation or deficated bandwidth can be outperforming on your hardware if a cloud provider uses a not well-balanced custom build.

Just take a look at list of Intel's products with R.I.P. status to get the answer. The only thing Intel guarantees to live is X86_64 CPU.

Maybe, a decision tree is an example of a NN? I mean that NN is more generic structure because it may include an arbitrary Neuron design and custom layers design?