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@@ -111,7 +111,6 @@ from tqdm.auto import tqdm
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import hivemind
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-
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# Create dataset and model, same as in the basic tutorial
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# For this basic tutorial, we download only the training set
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transform = transforms.Compose(
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@@ -134,8 +133,8 @@ opt = hivemind.Optimizer(
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run_id='my_cifar_run', # unique identifier of this collaborative run
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batch_size_per_step=32, # each call to opt.step adds this many samples towards the next epoch
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target_batch_size=10000, # after peers collectively process this many samples, average weights and begin the next epoch
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- use_local_updates=True, # perform optimizer steps with local gradients, average parameters in background
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optimizer=opt, # wrap the SGD optimizer defined above
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+ use_local_updates=True, # perform optimizer steps with local gradients, average parameters in background
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matchmaking_time=3.0, # when averaging parameters, gather peers in background for up to this many seconds
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averaging_timeout=10.0, # give up on averaging if not successful in this many seconds
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verbose=True # print logs incessently
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