Comments (4)
To train with multiple GPUs, you should turn off JIT (the way I implemented it doesn't play well with multiple GPUs): add --no_jit
to the commandline arguments. Otherwise, you'll get the performance degradation you mentioned because it will retrace the backbone every iteration.
Also, if your batch size is less than 8 per GPU, you should also freeze batch norm by adding 'freeze_bn': True
to the config you use in data/config.py
(e.g., ctrl+f yolact_base_config
and add it to the dict there).
The DataParallel instance is in train.py
btw.
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Hi,
So what are the changes one needs to make to make it train on multiple GPUs?
Any lead will be highly appreciated.
Thanks. :)
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See #8 for all of the changes. One of these days I'll add the necessary changes as a command-line argument.
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For future reference: I removed --no_jit
and now it'll just turn JIT off if it detects more than one GPU.
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