Comments (5)
I had noted in the readme that I have yet to update the batch size for 3090. Previously I had run both at the batch size, but after comments from other people I managed to improve the M1 Max performance (very marginally) by increasing the batch size, but I have yet to re-run the 3090 benchmarks at the larger batch size. I do expect it to improve.
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Cool! I think having best-performing results for both platforms would give people understanding what can you achieve in terms of peak performance.
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Batch size is kept smaller, in line with the batch size running on M1 Max. This is for apple-to-apples comparison.
I did note on the results that the 3090 would perform better at higher batch sizes.
from tf-metal-experiments.
But batch size for 3090 is actually lower than for M1?
You could put numbers for best performance in both cases (i.e. run optimal bs for M1 on both and for 3090 on both) if you don't want to compare different bs.
from tf-metal-experiments.
I have updated the readme. I can't get his level of performance even at BS=256. I suspect Ross Wightman is using PyTorch and it ends up being more performant for some reason. This is why DL benchmarking is hard... it is about the software as much as it is about the hardware.
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Related Issues (15)
- High Power Mode HOT 5
- M1 Max Thermal Throttling HOT 9
- Which CPU and RAM in the Reference Benchmarks 3090 ? HOT 1
- adding A100, other configs comparison for fun HOT 30
- typo and probably do all stuff in conda? HOT 3
- does M1 max can use all of his memory in training??? HOT 2
- smaller batch sizes HOT 3
- Did anyone try M1 Pro HOT 2
- 2x Faster Inferencing
- Issues from "Big Sur" to "Monterey"
- Disable Eager Execution
- Adding pytorch benchmark?
- Graph execution error
- TFLOPS is off by a factor of 2 in conv_benchmark.py?
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