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ultralytics 8.2.29 new fractional AutoBatch feature (#13446)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-06-09 00:28:11 +02:00 • committed by GitHub
parent 2fe0946376
commit 6a234f3639
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12 changed files with 92 additions and 49 deletions
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16
ultralytics/utils/torch_utils.py
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@ -146,11 +146,17 @@ def select_device(device="", batch=0, newline=False, verbose=True):
if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7
n = len(devices) # device count
if n > 1 and batch > 0 and batch % n != 0: # check batch_size is divisible by device_count
raise ValueError(
f"'batch={batch}' must be a multiple of GPU count {n}. Try 'batch={batch // n * n}' or "
f"'batch={batch // n * n + n}', the nearest batch sizes evenly divisible by {n}."
)
if n > 1: # multi-GPU
if batch < 1:
raise ValueError(
"AutoBatch with batch<1 not supported for Multi-GPU training, "
"please specify a valid batch size, i.e. batch=16."
)
if batch >= 0 and batch % n != 0: # check batch_size is divisible by device_count
raise ValueError(
f"'batch={batch}' must be a multiple of GPU count {n}. Try 'batch={batch // n * n}' or "
f"'batch={batch // n * n + n}', the nearest batch sizes evenly divisible by {n}."
)
space = " " * (len(s) + 1)
for i, d in enumerate(devices):
p = torch.cuda.get_device_properties(i)