Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Updated FBResearchLogger example doctring #3237

Merged
merged 1 commit into from
Jun 14, 2024
Merged

Updated FBResearchLogger example doctring #3237

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
60 changes: 57 additions & 3 deletions ignite/handlers/fbresearch_logger.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,10 +30,64 @@ class FBResearchLogger:
.. code-block:: python

import logging
from ignite.handlers.fbresearch_logger import *

logger = FBResearchLogger(logger=logging.Logger(__name__), show_output=True)
logger.attach(trainer, name="Train", every=10, optimizer=my_optimizer)
import torch
import torch.nn as nn
import torch.optim as optim

from ignite.engine import create_supervised_trainer, Events
from ignite.handlers.fbresearch_logger import FBResearchLogger
from ignite.utils import setup_logger

model = nn.Linear(10, 5)
opt = optim.SGD(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

data = [(torch.rand(4, 10), torch.randint(0, 5, size=(4, ))) for _ in range(100)]

trainer = create_supervised_trainer(
model, opt, criterion, output_transform=lambda x, y, y_pred, loss: {"total_loss": loss.item()}
)

logger = setup_logger("trainer", level=logging.INFO)
logger = FBResearchLogger(logger=logger, show_output=True)
logger.attach(trainer, name="Train", every=20, optimizer=opt)

trainer.run(data, max_epochs=4)

Output:

.. code-block:: text

2024-04-22 12:05:47,843 trainer INFO: Train: start epoch [1/4]
... Epoch [1/4] [20/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5999 Iter time: 0.0008 s Data prep ..
... Epoch [1/4] [40/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9297 Iter time: 0.0008 s Data prep ..
... Epoch [1/4] [60/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9985 Iter time: 0.0008 s Data prep ..
... Epoch [1/4] [80/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9785 Iter time: 0.0008 s Data prep ..
... Epoch [1/4] [100/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6211 Iter time: 0.0008 s Data prep .
... Train: Epoch [1/4] Total time: 0:00:00 (0.0008 s / it)
... Train: start epoch [2/4]
... Epoch [2/4] [19/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5981 Iter time: 0.0009 s Data prep ..
... Epoch [2/4] [39/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9013 Iter time: 0.0008 s Data prep ..
... Epoch [2/4] [59/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9811 Iter time: 0.0008 s Data prep ..
... Epoch [2/4] [79/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9434 Iter time: 0.0008 s Data prep ..
... Epoch [2/4] [99/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6116 Iter time: 0.0008 s Data prep ..
... Train: Epoch [2/4] Total time: 0:00:00 (0.0009 s / it)
... Train: start epoch [3/4]
... Epoch [3/4] [18/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5972 Iter time: 0.0008 s Data prep ..
... Epoch [3/4] [38/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8753 Iter time: 0.0008 s Data prep ..
... Epoch [3/4] [58/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9657 Iter time: 0.0009 s Data prep ..
... Epoch [3/4] [78/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9112 Iter time: 0.0008 s Data prep ..
... Epoch [3/4] [98/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.6035 Iter time: 0.0008 s Data prep ..
... Train: Epoch [3/4] Total time: 0:00:00 (0.0009 s / it)
... Train: start epoch [4/4]
... Epoch [4/4] [17/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5969 Iter time: 0.0008 s Data prep ..
... Epoch [4/4] [37/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8516 Iter time: 0.0008 s Data prep ..
... Epoch [4/4] [57/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.9521 Iter time: 0.0008 s Data prep ..
... Epoch [4/4] [77/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.8816 Iter time: 0.0008 s Data prep ..
... Epoch [4/4] [97/100]: ETA: 0:00:00 lr: 0.00100 total_loss: 1.5966 Iter time: 0.0009 s Data prep ..
... Train: Epoch [4/4] Total time: 0:00:00 (0.0009 s / it)
... Train: run completed Total time: 0:00:00
"""

def __init__(self, logger: Any, delimiter: str = " ", show_output: bool = False):
Expand Down