NeptuneObserver raises Neptune.api_exceptions.ChannelsValuesSendBatchError #5130
Description
🐛 Bug
NeptuneObserver throws
Failed to send channel value.
Traceback (most recent call last):
File "/home/wojciech/miniconda3/envs/ml/lib/python3.7/site-packages/neptune/internal/channels/channels_values_sender.py", line 156, in _send_values
self._experiment._send_channels_values(channels_with_values)
File "/home/wojciech/miniconda3/envs/ml/lib/python3.7/site-packages/neptune/experiments.py", line 1167, in _send_channels_values
self._backend.send_channels_values(self, channels_with_values)
File "/home/wojciech/miniconda3/envs/ml/lib/python3.7/site-packages/neptune/utils.py", line 211, in wrapper
return func(*args, **kwargs)
File "/home/wojciech/miniconda3/envs/ml/lib/python3.7/site-packages/neptune/internal/backends/hosted_neptune_backend.py", line 571, in send_channels_values
raise ChannelsValuesSendBatchError(experiment.id, batch_errors)
neptune.api_exceptions.ChannelsValuesSendBatchError: Received batch errors sending channels' values to experiment SAN-28. Cause: Error(code=400, message='X-coordinates must be strictly increasing for channel: e968f192-b466-4419-89ce-469fdc4cf86f. Invalid point: InputChannelValue(timestamp=2020-12-14T15:53:20.877Z, x=0.0, numericValue=0.0, textValue=null, image', type=None) (metricId: 'e968f192-b466-4419-89ce-469fdc4cf86f', x: 0.0) Skipping 1 values.
/home/wojciech/miniconda3/envs/ml/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:49: UserWarning: The dataloader, test dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 6 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
warnings.warn(*args, **kwargs)
import os
import torch
from pytorch_lightning.loggers import NeptuneLogger
from torch.utils.data import Dataset
from pytorch_lightning import Trainer, LightningModule
class RandomDataset(Dataset):
def __init__(self, size, length):
self.len = length
self.data = torch.randn(length, size)
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return self.len
class BoringModel(LightningModule):
def __init__(self):
"""
Testing PL Module
Use as follows:
- subclass
- modify the behavior for what you want
class TestModel(BaseTestModel):
def training_step(...):
# do your own thing
or:
model = BaseTestModel()
model.training_epoch_end = None
"""
super().__init__()
self.layer = torch.nn.Linear(32, 2)
def forward(self, x):
return self.layer(x)
def loss(self, batch, prediction):
# An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))
def step(self, x):
x = self.layer(x)
out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
return out
def training_step(self, batch, batch_idx):
output = self.layer(batch)
loss = self.loss(batch, output)
self.log('train_loss', loss, on_step=True, on_epoch=True)
return {"loss": loss}
def training_step_end(self, training_step_outputs):
return training_step_outputs
def training_epoch_end(self, outputs) -> None:
torch.stack([x["loss"] for x in outputs]).mean()
def validation_step(self, batch, batch_idx):
output = self.layer(batch)
loss = self.loss(batch, output)
self.log('val_loss', loss, on_step=False, on_epoch=True)
return {"x": loss}
def validation_epoch_end(self, outputs) -> None:
torch.stack([x['x'] for x in outputs]).mean()
def test_step(self, batch, batch_idx):
output = self.layer(batch)
loss = self.loss(batch, output)
return {"y": loss}
def test_epoch_end(self, outputs) -> None:
torch.stack([x["y"] for x in outputs]).mean()
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
return [optimizer], [lr_scheduler]
# NOTE: If you are using a cmd line to run your script,
# provide the cmd line as below.
# opt = "--max_epochs 1 --limit_train_batches 1".split(" ")
# parser = ArgumentParser()
# args = parser.parse_args(opt)
def run_test():
class TestModel(BoringModel):
def on_train_epoch_start(self) -> None:
print('override any method to prove your bug')
# fake data
train_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
val_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
test_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
neptune_logger = NeptuneLogger(
api_key = "ANONYMOUS",
project_name = "shared/pytorch-lightning-integration",
)
# model
model = TestModel()
trainer = Trainer(
default_root_dir=os.getcwd(),
limit_train_batches=1,
limit_val_batches=1,
max_epochs=1,
weights_summary=None,
logger=neptune_logger
)
trainer.fit(model, train_data, val_data)
trainer.test(test_dataloaders=test_data)
if __name__ == '__main__':
run_test()Expected behavior
No exception.
Environment
This happens both with pytorch-lighting 1.0.7 and 1.1 and neptune-client-0.4.126 and 129.
- CUDA:
- GPU:
- GeForce GTX 1080 Ti
- available: True
- version: 11.0
- GPU:
- Packages:
- numpy: 1.18.5
- pyTorch_debug: False
- pyTorch_version: 1.7.1
- pytorch-lightning: 1.1.0
- tqdm: 4.54.1
- System:
- OS: Linux
- architecture:
- 64bit
- processor: x86_64
- python: 3.7.4
- version: moving examples out of the package #36-Ubuntu SMP Wed Dec 9 09:14:40 UTC 2020
Additional context
Happens only when we log during validation_step only with on_epoch=True, i.e.:
self.log('val_loss', loss, on_step=False, on_epoch=True)