Row 80411

Row ID: 80411 | Dataset Entry | Axioma AXP Content Repository

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As long as the NVIDIA GPUs are CUDA enabled (make sure that they are! Otherwise tf/pytorch cannot access the device), I think those are the most relevant specs. Large mem allows larger models and batch sizes, FLOPS/TOPS reflects the speed of performing operations. AFAIK the type of data does not matter to the device itself, since you are converting it to torch/tf tensors anyways. Of course, it is a matter of programming not to waste memory and other resources by using e.g. very sparce matrices if that is avoidable.

FieldValue
text As long as the NVIDIA GPUs are CUDA enabled (make sure that they are! Otherwise tf/pytorch cannot access the device), I think those are the most relevant specs. Large mem allows larger models and batch sizes, FLOPS/TOPS reflects the speed of performing operations. AFAIK the type of data does not matter to the device itself, since you are converting it to torch/tf tensors anyways. Of course, it is a matter of programming not to waste memory and other resources by using e.g. very sparce matrices i…
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-24
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Raw Record

{
  "text": "As long as the NVIDIA GPUs are CUDA enabled (make sure that they are! Otherwise tf/pytorch cannot access the device), I think those are the most relevant specs. Large mem allows larger models and batch sizes, FLOPS/TOPS reflects the speed of performing operations. AFAIK the type of data does not matter to the device itself, since you are converting it to torch/tf tensors anyways. Of course, it is a matter of programming not to waste memory and other resources by using e.g. very sparce matrices if that is avoidable.",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-24",
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}
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