NeptuneObserver raises Neptune.api_exceptions.ChannelsValuesSendBatchError
🐛 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


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:           #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)