385 lines
10 KiB
ReStructuredText
385 lines
10 KiB
ReStructuredText
:orphan:
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.. _logging_advanced:
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##########################################
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Track and Visualize Experiments (advanced)
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##########################################
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**Audience:** Users who want to do advanced speed optimizations by customizing the logging behavior.
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----
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****************************
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Change progress bar defaults
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****************************
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To change the default values (ie: version number) shown in the progress bar, override the :meth:`~pytorch_lightning.callbacks.progress.base.ProgressBarBase.get_metrics` method in your logger.
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.. code-block:: python
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from pytorch_lightning.callbacks.progress import Tqdm
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class CustomProgressBar(Tqdm):
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def get_metrics(self, *args, **kwargs):
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# don't show the version number
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items = super().get_metrics()
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items.pop("v_num", None)
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return items
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----
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************************************
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Customize tracking to speed up model
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************************************
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Modify logging frequency
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========================
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Logging a metric on every single batch can slow down training. By default, Lightning logs every 50 rows, or 50 training steps.
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To change this behaviour, set the *log_every_n_steps* :class:`~pytorch_lightning.trainer.trainer.Trainer` flag.
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.. testcode::
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k = 10
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trainer = Trainer(log_every_n_steps=k)
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----
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Modify flushing frequency
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=========================
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Some loggers keep logged metrics in memory for N steps and only periodically flush them to disk to improve training efficiency.
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Every logger handles this a bit differently. For example, here is how to fine-tune flushing for the TensorBoard logger:
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.. code-block:: python
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# Default used by TensorBoard: Write to disk after 10 logging events or every two minutes
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logger = TensorBoardLogger(..., max_queue=10, flush_secs=120)
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# Faster training, more memory used
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logger = TensorBoardLogger(..., max_queue=100)
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# Slower training, less memory used
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logger = TensorBoardLogger(..., max_queue=1)
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----
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******************
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Customize self.log
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******************
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The LightningModule *self.log* method offers many configurations to customize its behavior.
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----
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add_dataloader_idx
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==================
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**Default:** True
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If True, appends the index of the current dataloader to the name (when using multiple dataloaders). If False, user needs to give unique names for each dataloader to not mix the values.
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.. code-block:: python
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self.log(add_dataloader_idx=True)
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----
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batch_size
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==========
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**Default:** None
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Current batch size used for accumulating logs logged with ``on_epoch=True``. This will be directly inferred from the loaded batch, but for some data structures you might need to explicitly provide it.
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.. code-block:: python
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self.log(batch_size=32)
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----
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enable_graph
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============
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**Default:** True
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If True, will not auto detach the graph.
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.. code-block:: python
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self.log(enable_graph=True)
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----
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logger
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======
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**Default:** True
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Send logs to the logger like ``Tensorboard``, or any other custom logger passed to the :class:`~pytorch_lightning.trainer.trainer.Trainer` (Default: ``True``).
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.. code-block:: python
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self.log(logger=True)
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----
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on_epoch
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========
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**Default:** It varies
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If this is True, that specific *self.log* call accumulates and reduces all metrics to the end of the epoch.
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.. code-block:: python
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self.log(on_epoch=True)
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The default value depends in which function this is called
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.. code-block:: python
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def training_step(self, batch, batch_idx):
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# Default: False
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self.log(on_epoch=False)
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def validation_step(self, batch, batch_idx):
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# Default: True
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self.log(on_epoch=True)
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def test_step(self, batch, batch_idx):
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# Default: True
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self.log(on_epoch=True)
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----
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on_step
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=======
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**Default:** It varies
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If this is True, that specific *self.log* call will NOT accumulate metrics. Instead it will generate a timeseries across steps.
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.. code-block:: python
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self.log(on_step=True)
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The default value depends in which function this is called
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.. code-block:: python
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def training_step(self, batch, batch_idx):
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# Default: True
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self.log(on_step=True)
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def validation_step(self, batch, batch_idx):
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# Default: False
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self.log(on_step=False)
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def test_step(self, batch, batch_idx):
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# Default: False
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self.log(on_step=False)
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----
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prog_bar
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========
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**Default:** False
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If set to True, logs will be sent to the progress bar.
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.. code-block:: python
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self.log(prog_bar=True)
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----
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rank_zero_only
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==============
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**Default:** True
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Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.
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.. code-block:: python
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self.log(rank_zero_only=True)
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----
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reduce_fx
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=========
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**Default:** :meth:`torch.mean`
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Reduction function over step values for end of epoch. Uses :meth:`torch.mean` by default and is not applied when a :class:`torchmetrics.Metric` is logged.
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.. code-block:: python
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self.log(..., reduce_fx=torch.mean)
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----
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sync_dist
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=========
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**Default:** False
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If True, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.
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.. code-block:: python
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self.log(sync_dist=False)
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----
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sync_dist_group
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===============
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**Default:** None
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The DDP group to sync across.
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.. code-block:: python
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import torch.distributed as dist
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group = dist.init_process_group("nccl", rank=self.global_rank, world_size=self.world_size)
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self.log(sync_dist_group=group)
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----
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***************************************
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Enable metrics for distributed training
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***************************************
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For certain types of metrics that need complex aggregation, we recommended to build your metric using torchmetric which ensures all the complexities of metric aggregation in distributed environments is handled.
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First, implement your metric:
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.. code-block:: python
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import torch
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import torchmetrics
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class MyAccuracy(Metric):
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def __init__(self, dist_sync_on_step=False):
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# call `self.add_state`for every internal state that is needed for the metrics computations
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# dist_reduce_fx indicates the function that should be used to reduce
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# state from multiple processes
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super().__init__(dist_sync_on_step=dist_sync_on_step)
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self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
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self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
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def update(self, preds: torch.Tensor, target: torch.Tensor):
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# update metric states
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preds, target = self._input_format(preds, target)
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assert preds.shape == target.shape
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self.correct += torch.sum(preds == target)
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self.total += target.numel()
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def compute(self):
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# compute final result
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return self.correct.float() / self.total
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To use the metric inside Lightning, 1) initialize it in the init, 2) compute the metric, 3) pass it into *self.log*
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.. code-block:: python
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class LitModel(LightningModule):
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def __init__(self):
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# 1. initialize the metric
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self.accuracy = MyAccuracy()
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def training_step(self, batch, batch_idx):
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x, y = batch
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preds = self(x)
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# 2. compute the metric
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self.accuracy(preds, y)
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# 3. log it
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self.log("train_acc_step", self.accuracy)
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----
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********************************
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Log to a custom cloud filesystem
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********************************
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Lightning is integrated with the major remote file systems including local filesystems and several cloud storage providers such as
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`S3 <https://aws.amazon.com/s3/>`_ on `AWS <https://aws.amazon.com/>`_, `GCS <https://cloud.google.com/storage>`_ on `Google Cloud <https://cloud.google.com/>`_,
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or `ADL <https://azure.microsoft.com/solutions/data-lake/>`_ on `Azure <https://azure.microsoft.com/>`_.
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PyTorch Lightning uses `fsspec <https://filesystem-spec.readthedocs.io/>`_ internally to handle all filesystem operations.
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To save logs to a remote filesystem, prepend a protocol like "s3:/" to the root_dir used for writing and reading model data.
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.. code-block:: python
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from pytorch_lightning.loggers import TensorBoardLogger
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logger = TensorBoardLogger(save_dir="s3://my_bucket/logs/")
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trainer = Trainer(logger=logger)
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trainer.fit(model)
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----
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*********************************
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Track both step and epoch metrics
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*********************************
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To track the timeseries over steps (*on_step*) as well as the accumulated epoch metric (*on_epoch*), set both to True
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.. code-block:: python
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self.log(on_step=True, on_epoch=True)
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Setting both to True will generate two graphs with *_step* for the timeseries over steps and *_epoch* for the epoch metric.
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# TODO: show images of both
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----
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**************************************
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Understand self.log automatic behavior
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**************************************
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This table shows the default values of *on_step* and *on_epoch* depending on the *LightningModule* or *Callback* method.
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----
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In LightningModule
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==================
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.. list-table:: Default behavior of logging in ightningModule
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:widths: 50 25 25
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:header-rows: 1
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* - Method
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- on_step
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- on_epoch
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* - on_after_backward, on_before_backward, on_before_optimizer_step, optimizer_step, configure_gradient_clipping, on_before_zero_grad, training_step
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- True
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- False
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* - test_step, validation_step
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- False
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- True
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----
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In Callback
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===========
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.. list-table:: Default behavior of logging in Callback
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:widths: 50 25 25
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:header-rows: 1
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* - Method
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- on_step
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- on_epoch
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* - on_after_backward, on_before_backward, on_before_optimizer_step, on_before_zero_grad, on_train_batch_start, on_train_batch_end
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- True
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- False
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* - on_train_epoch_start, on_train_epoch_end, on_train_start, on_validation_batch_start, on_validation_batch_end, on_validation_start, on_validation_epoch_start, on_validation_epoch_end
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- False
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- True
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.. note:: To add logging to an unsupported method, please open an issue with a clear description of why it is blocking you.
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