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Updated FBResearchLogger example doctring
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Diff for: ‎ignite/handlers/fbresearch_logger.py

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@@ -30,10 +30,64 @@ class FBResearchLogger:
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.. code-block:: python
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import logging
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from ignite.handlers.fbresearch_logger import *
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logger = FBResearchLogger(logger=logging.Logger(__name__), show_output=True)
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logger.attach(trainer, name="Train", every=10, optimizer=my_optimizer)
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from ignite.engine import create_supervised_trainer, Events
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from ignite.handlers.fbresearch_logger import FBResearchLogger
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from ignite.utils import setup_logger
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model = nn.Linear(10, 5)
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opt = optim.SGD(model.parameters(), lr=0.001)
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criterion = nn.CrossEntropyLoss()
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data = [(torch.rand(4, 10), torch.randint(0, 5, size=(4, ))) for _ in range(100)]
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trainer = create_supervised_trainer(
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model, opt, criterion, output_transform=lambda x, y, y_pred, loss: {"total_loss": loss.item()}
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)
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logger = setup_logger("trainer", level=logging.INFO)
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logger = FBResearchLogger(logger=logger, show_output=True)
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logger.attach(trainer, name="Train", every=20, optimizer=opt)
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trainer.run(data, max_epochs=4)
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Output:
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.. code-block:: text
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2024-04-22 12:05:47,843 trainer INFO: Train: start epoch [1/4]
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... Epoch [1/4] [20/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5999 Iter time: 0.0008 s Data prep ..
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... Epoch [1/4] [40/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9297 Iter time: 0.0008 s Data prep ..
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... Epoch [1/4] [60/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9985 Iter time: 0.0008 s Data prep ..
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... Epoch [1/4] [80/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9785 Iter time: 0.0008 s Data prep ..
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... Epoch [1/4] [100/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6211 Iter time: 0.0008 s Data prep .
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... Train: Epoch [1/4] Total time: 0:00:00 (0.0008 s / it)
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... Train: start epoch [2/4]
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... Epoch [2/4] [19/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5981 Iter time: 0.0009 s Data prep ..
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... Epoch [2/4] [39/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9013 Iter time: 0.0008 s Data prep ..
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... Epoch [2/4] [59/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9811 Iter time: 0.0008 s Data prep ..
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... Epoch [2/4] [79/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9434 Iter time: 0.0008 s Data prep ..
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... Epoch [2/4] [99/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6116 Iter time: 0.0008 s Data prep ..
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... Train: Epoch [2/4] Total time: 0:00:00 (0.0009 s / it)
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... Train: start epoch [3/4]
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... Epoch [3/4] [18/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5972 Iter time: 0.0008 s Data prep ..
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... Epoch [3/4] [38/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8753 Iter time: 0.0008 s Data prep ..
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... Epoch [3/4] [58/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9657 Iter time: 0.0009 s Data prep ..
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... Epoch [3/4] [78/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9112 Iter time: 0.0008 s Data prep ..
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... Epoch [3/4] [98/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6035 Iter time: 0.0008 s Data prep ..
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... Train: Epoch [3/4] Total time: 0:00:00 (0.0009 s / it)
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... Train: start epoch [4/4]
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... Epoch [4/4] [17/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5969 Iter time: 0.0008 s Data prep ..
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... Epoch [4/4] [37/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8516 Iter time: 0.0008 s Data prep ..
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... Epoch [4/4] [57/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9521 Iter time: 0.0008 s Data prep ..
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... Epoch [4/4] [77/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8816 Iter time: 0.0008 s Data prep ..
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... Epoch [4/4] [97/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5966 Iter time: 0.0009 s Data prep ..
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... Train: Epoch [4/4] Total time: 0:00:00 (0.0009 s / it)
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... Train: run completed Total time: 0:00:00
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"""
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def __init__(self, logger: Any, delimiter: str = " ", show_output: bool = False):

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