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Track and Visualize Experiments (advanced)

Audience: Users who want to do advanced speed optimizations by customizing the logging behavior.

Change progress bar defaults

To change the default values (ie: version number) shown in the progress bar, override the get_metrics() method in your logger.

from pytorch_lightning.callbacks.progress import Tqdm

class CustomProgressBar(Tqdm):
    def get_metrics(self, *args, **kwargs):
        # don't show the version number
        items = super().get_metrics()
        items.pop("v_num", None)
        return items

Customize tracking to speed up model

Modify logging frequency

Logging a metric on every single batch can slow down training. By default, Lightning logs every 50 rows, or 50 training steps. To change this behaviour, set the log_every_n_steps Trainer flag.

k = 10
trainer = Trainer(log_every_n_steps=k)

Modify flushing frequency

Some loggers keep logged metrics in memory for N steps and only periodically flush them to disk to improve training efficiency. Every logger handles this a bit differently. For example, here is how to fine-tune flushing for the TensorBoard logger:

# Default used by TensorBoard: Write to disk after 10 logging events or every two minutes
logger = TensorBoardLogger(..., max_queue=10, flush_secs=120)

# Faster training, more memory used
logger = TensorBoardLogger(..., max_queue=100)

# Slower training, less memory used
logger = TensorBoardLogger(..., max_queue=1)

Customize self.log

The LightningModule self.log method offers many configurations to customize its behavior.


Default: True

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.



Default: None

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.



Default: True

If True, will not auto detach the graph.



Default: True

Send logs to the logger like Tensorboard, or any other custom logger passed to the Trainer (Default: True).



Default: It varies

If this is True, that specific self.log call accumulates and reduces all metrics to the end of the epoch.


The default value depends in which function this is called

def training_step(self, batch, batch_idx):
    # Default: False

def validation_step(self, batch, batch_idx):
    # Default: True

def test_step(self, batch, batch_idx):
    # Default: True


Default: It varies

If this is True, that specific self.log call will NOT accumulate metrics. Instead it will generate a timeseries across steps.


The default value depends in which function this is called

def training_step(self, batch, batch_idx):
    # Default: True

def validation_step(self, batch, batch_idx):
    # Default: False

def test_step(self, batch, batch_idx):
    # Default: False


Default: False

If set to True, logs will be sent to the progress bar.



Default: True

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.



Default: torch.mean()

Reduction function over step values for end of epoch. Uses torch.mean() by default and is not applied when a torchmetrics.Metric is logged.

self.log(..., reduce_fx=torch.mean)


Default: False

If True, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.



Default: None

The DDP group to sync across.

import torch.distributed as dist

group = dist.init_process_group("nccl", rank=self.global_rank, world_size=self.world_size)

Enable metrics for distributed training

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.

First, implement your metric:

import torch
import torchmetrics

class MyAccuracy(Metric):
    def __init__(self, dist_sync_on_step=False):
        # call `self.add_state`for every internal state that is needed for the metrics computations
        # dist_reduce_fx indicates the function that should be used to reduce
        # state from multiple processes

        self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")

    def update(self, preds: torch.Tensor, target: torch.Tensor):
        # update metric states
        preds, target = self._input_format(preds, target)
        assert preds.shape == target.shape

        self.correct += torch.sum(preds == target)
        self.total += target.numel()

    def compute(self):
        # compute final result
        return self.correct.float() / self.total

To use the metric inside Lightning, 1) initialize it in the init, 2) compute the metric, 3) pass it into self.log

class LitModel(LightningModule):
    def __init__(self):
        # 1. initialize the metric
        self.accuracy = MyAccuracy()

    def training_step(self, batch, batch_idx):
        x, y = batch
        preds = self(x)

        # 2. compute the metric
        self.accuracy(preds, y)

        # 3. log it
        self.log("train_acc_step", self.accuracy)

Log to a custom cloud filesystem

Lightning is integrated with the major remote file systems including local filesystems and several cloud storage providers such as S3 on AWS, GCS on Google Cloud, or ADL on Azure.

PyTorch Lightning uses fsspec internally to handle all filesystem operations.

To save logs to a remote filesystem, prepend a protocol like “s3:/” to the root_dir used for writing and reading model data.

from pytorch_lightning.loggers import TensorBoardLogger

logger = TensorBoardLogger(save_dir="s3://my_bucket/logs/")

trainer = Trainer(logger=logger)

Track both step and epoch metrics

To track the timeseries over steps (on_step) as well as the accumulated epoch metric (on_epoch), set both to True

self.log(on_step=True, on_epoch=True)

Setting both to True will generate two graphs with _step for the timeseries over steps and _epoch for the epoch metric.

# TODO: show images of both

Understand self.log automatic behavior

This table shows the default values of on_step and on_epoch depending on the LightningModule or Callback method.

In LightningModule

Default behavior of logging in ightningModule




on_after_backward, on_before_backward, on_before_optimizer_step, on_before_zero_grad, training_step, training_step_end



training_epoch_end, test_epoch_end, test_step, test_step_end, validation_epoch_end, validation_step, validation_step_end



In Callback

Default behavior of logging in Callback




on_after_backward, on_before_backward, on_before_optimizer_step, on_before_zero_grad, on_train_batch_start, on_train_batch_end



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




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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