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Trainer

Once you’ve organized your PyTorch code into a LightningModule, the Trainer automates everything else.


This abstraction achieves the following:

  1. You maintain control over all aspects via PyTorch code without an added abstraction.

  2. The trainer uses best practices embedded by contributors and users from top AI labs such as Facebook AI Research, NYU, MIT, Stanford, etc…

  3. The trainer allows overriding any key part that you don’t want automated.



Basic use

This is the basic use of the trainer:

model = MyLightningModule()

trainer = Trainer()
trainer.fit(model, train_dataloader, val_dataloader)

Trainer in Python scripts

In Python scripts, it’s recommended you use a main function to call the Trainer.

from argparse import ArgumentParser

def main(hparams):
    model = LightningModule()
    trainer = Trainer(gpus=hparams.gpus)
    trainer.fit(model)

if __name__ == '__main__':
    parser = ArgumentParser()
    parser.add_argument('--gpus', default=None)
    args = parser.parse_args()

    main(args)

So you can run it like so:

python main.py --gpus 2

Note

Pro-tip: You don’t need to define all flags manually. Lightning can add them automatically

from argparse import ArgumentParser

def main(args):
    model = LightningModule()
    trainer = Trainer.from_argparse_args(args)
    trainer.fit(model)

if __name__ == '__main__':
    parser = ArgumentParser()
    parser = Trainer.add_argparse_args(parser)
    args = parser.parse_args()

    main(args)

So you can run it like so:

python main.py --gpus 2 --max_steps 10 --limit_train_batches 10 --any_trainer_arg x

Note

If you want to stop a training run early, you can press “Ctrl + C” on your keyboard. The trainer will catch the KeyboardInterrupt and attempt a graceful shutdown, including running callbacks such as on_train_end. The trainer object will also set an attribute interrupted to True in such cases. If you have a callback which shuts down compute resources, for example, you can conditionally run the shutdown logic for only uninterrupted runs.


Testing

Once you’re done training, feel free to run the test set! (Only right before publishing your paper or pushing to production)

trainer.test(test_dataloader=test_dataloader)

Deployment / prediction

You just trained a LightningModule which is also just a torch.nn.Module. Use it to do whatever!

# load model
pretrained_model = LightningModule.load_from_checkpoint(PATH)
pretrained_model.freeze()

# use it for finetuning
def forward(self, x):
    features = pretrained_model(x)
    classes = classifier(features)

# or for prediction
out = pretrained_model(x)
api_write({'response': out}

You may wish to run the model on a variety of devices. Instead of moving the data manually to the correct device, decorate the forward method (or any other method you use for inference) with auto_move_data() and Lightning will take care of the rest.


Reproducibility

To ensure full reproducibility from run to run you need to set seeds for pseudo-random generators, and set deterministic flag in Trainer.

Example:

from pytorch_lightning import Trainer, seed_everything

seed_everything(42)
# sets seeds for numpy, torch, python.random and PYTHONHASHSEED.
model = Model()
trainer = Trainer(deterministic=True)

Trainer flags

accelerator


The accelerator backend to use (previously known as distributed_backend).

  • (`dp`) is DataParallel (split batch among GPUs of same machine)

  • (`ddp`) is DistributedDataParallel (each gpu on each node trains, and syncs grads)

  • (`ddp_cpu`) is DistributedDataParallel on CPU (same as ddp, but does not use GPUs. Useful for multi-node CPU training or single-node debugging. Note that this will not give a speedup on a single node, since Torch already makes effient use of multiple CPUs on a single machine.)

  • (`ddp2`) dp on node, ddp across nodes. Useful for things like increasing

    the number of negative samples

# default used by the Trainer
trainer = Trainer(distributed_backend=None)

Example:

# dp = DataParallel
trainer = Trainer(gpus=2, distributed_backend='dp')

# ddp = DistributedDataParallel
trainer = Trainer(gpus=2, num_nodes=2, distributed_backend='ddp')

# ddp2 = DistributedDataParallel + dp
trainer = Trainer(gpus=2, num_nodes=2, distributed_backend='ddp2')

Note

this option does not apply to TPU. TPUs use `ddp` by default (over each core)

You can also modify hardware behavior by subclassing an existing accelerator to adjust for your needs.

Example:

class MyOwnDDP(DDPAccelerator):
    ...

Trainer(accelerator=MyOwnDDP())

Warning

Passing in custom accelerators is experimental but work is in progress to enable full compatibility.

accumulate_grad_batches


Accumulates grads every k batches or as set up in the dict. Trainer also calls optimizer.step() for the last indivisible step number.

# default used by the Trainer (no accumulation)
trainer = Trainer(accumulate_grad_batches=1)

Example:

# accumulate every 4 batches (effective batch size is batch*4)
trainer = Trainer(accumulate_grad_batches=4)

# no accumulation for epochs 1-4. accumulate 3 for epochs 5-10. accumulate 20 after that
trainer = Trainer(accumulate_grad_batches={5: 3, 10: 20})

amp_backend


Use PyTorch AMP (‘native’) (available PyTorch 1.6+), or NVIDIA apex (‘apex’).

# using PyTorch built-in AMP, default used by the Trainer
trainer = Trainer(amp_backend='native')

# using NVIDIA Apex
trainer = Trainer(amp_backend='apex')

amp_level


The optimization level to use (O1, O2, etc…) for 16-bit GPU precision (using NVIDIA apex under the hood).

Check NVIDIA apex docs for level

Example:

# default used by the Trainer
trainer = Trainer(amp_level='O2')

automatic_optimization

When set to False, Lightning does not automate the optimization process. This means you are responsible for your own optimizer behavior

Example:

def training_step(self, batch, batch_idx):
    opt = self.optimizers()

    loss = ...
    self.manual_backward(loss, opt)
    opt.step()
    opt.zero_grad()

This is not recommended when using a single optimizer, instead it’s recommended when using 2+ optimizers AND you are an expert user. Most useful for research like RL, sparse coding and GAN research.

In the multi-optimizer case, ignore the optimizer_idx flag and use the optimizers directly

Example:

def training_step(self, batch, batch_idx, optimizer_idx):
    (opt_a, opt_b) = self.optimizers()

    gen_loss = ...
    self.manual_backward(gen_loss, opt_a)
    opt_a.step()
    opt_a.zero_grad()

    disc_loss = ...
    self.manual_backward(disc_loss, opt_b)
    opt_b.step()
    opt_b.zero_grad()

auto_scale_batch_size


Automatically tries to find the largest batch size that fits into memory, before any training.

# default used by the Trainer (no scaling of batch size)
trainer = Trainer(auto_scale_batch_size=None)

# run batch size scaling, result overrides hparams.batch_size
trainer = Trainer(auto_scale_batch_size='binsearch')

# call tune to find the batch size
trainer.tune(model)

auto_select_gpus


If enabled and gpus is an integer, pick available gpus automatically. This is especially useful when GPUs are configured to be in “exclusive mode”, such that only one process at a time can access them.

Example:

# no auto selection (picks first 2 gpus on system, may fail if other process is occupying)
trainer = Trainer(gpus=2, auto_select_gpus=False)

# enable auto selection (will find two available gpus on system)
trainer = Trainer(gpus=2, auto_select_gpus=True)

auto_lr_find


Runs a learning rate finder algorithm (see this paper) when calling trainer.tune(), to find optimal initial learning rate.

# default used by the Trainer (no learning rate finder)
trainer = Trainer(auto_lr_find=False)

Example:

# run learning rate finder, results override hparams.learning_rate
trainer = Trainer(auto_lr_find=True)

# call tune to find the lr
trainer.tune(model)

Example:

# run learning rate finder, results override hparams.my_lr_arg
trainer = Trainer(auto_lr_find='my_lr_arg')

# call tune to find the lr
trainer.tune(model)

benchmark


If true enables cudnn.benchmark. This flag is likely to increase the speed of your system if your input sizes don’t change. However, if it does, then it will likely make your system slower.

The speedup comes from allowing the cudnn auto-tuner to find the best algorithm for the hardware [see discussion here].

Example:

# default used by the Trainer
trainer = Trainer(benchmark=False)

deterministic


If true enables cudnn.deterministic. Might make your system slower, but ensures reproducibility. Also sets $HOROVOD_FUSION_THRESHOLD=0.

For more info check [pytorch docs].

Example:

# default used by the Trainer
trainer = Trainer(deterministic=False)

callbacks


Add a list of Callback. These callbacks DO NOT replace the explicit callbacks (loggers or ModelCheckpoint).

Note

Only user defined callbacks (ie: Not ModelCheckpoint)

# a list of callbacks
callbacks = [PrintCallback()]
trainer = Trainer(callbacks=callbacks)

Example:

from pytorch_lightning.callbacks import Callback

class PrintCallback(Callback):
    def on_train_start(self, trainer, pl_module):
        print("Training is started!")
    def on_train_end(self, trainer, pl_module):
        print("Training is done.")

check_val_every_n_epoch


Check val every n train epochs.

Example:

# default used by the Trainer
trainer = Trainer(check_val_every_n_epoch=1)

# run val loop every 10 training epochs
trainer = Trainer(check_val_every_n_epoch=10)

checkpoint_callback


Pass in a callback for checkpointing. Checkpoints capture the exact value of all parameters used by a model. By default Lightning saves a checkpoint for you in your current working directory, with the state of your last training epoch, but you can override the default behavior by Initializing the ModelCheckpoint callback, and passing it to Trainer checkpoint_callback flag.

from pytorch_lightning.callbacks import ModelCheckpoint

# default used by the Trainer
checkpoint_callback = ModelCheckpoint(
    filepath=os.getcwd(),
    save_top_k=True,
    verbose=True,
    monitor='checkpoint_on',
    mode='min',
    prefix=''
)

trainer = Trainer(checkpoint_callback=checkpoint_callback)

To disable automatic checkpointing, set this to False.

trainer = Trainer(checkpoint_callback=False)

See also Saving and Loading Weights.

default_root_dir


Default path for logs and weights when no logger or pytorch_lightning.callbacks.ModelCheckpoint callback passed. On certain clusters you might want to separate where logs and checkpoints are stored. If you don’t then use this argument for convenience. Paths can be local paths or remote paths such as s3://bucket/path or ‘hdfs://path/’. Credentials will need to be set up to use remote filepaths.

Example:

# default used by the Trainer
trainer = Trainer(default_root_path=os.getcwd())

distributed_backend

This has been renamed “accelerator”.

fast_dev_run



Runs 1 batch of train, test and val to find any bugs (ie: a sort of unit test).

Under the hood the pseudocode looks like this:

# loading
__init__()
prepare_data

# test training step
training_batch = next(train_dataloader)
training_step(training_batch)

# test val step
val_batch = next(val_dataloader)
out = validation_step(val_batch)
validation_epoch_end([out])
# default used by the Trainer
trainer = Trainer(fast_dev_run=False)

# runs 1 train, val, test batch and program ends
trainer = Trainer(fast_dev_run=True)

gpus


  • Number of GPUs to train on (int)

  • or which GPUs to train on (list)

  • can handle strings

# default used by the Trainer (ie: train on CPU)
trainer = Trainer(gpus=None)

# equivalent
trainer = Trainer(gpus=0)

Example:

# int: train on 2 gpus
trainer = Trainer(gpus=2)

# list: train on GPUs 1, 4 (by bus ordering)
trainer = Trainer(gpus=[1, 4])
trainer = Trainer(gpus='1, 4') # equivalent

# -1: train on all gpus
trainer = Trainer(gpus=-1)
trainer = Trainer(gpus='-1') # equivalent

# combine with num_nodes to train on multiple GPUs across nodes
# uses 8 gpus in total
trainer = Trainer(gpus=2, num_nodes=4)

# train only on GPUs 1 and 4 across nodes
trainer = Trainer(gpus=[1, 4], num_nodes=4)

gradient_clip_val


Gradient clipping value

  • 0 means don’t clip.

# default used by the Trainer
trainer = Trainer(gradient_clip_val=0.0)

limit_test_batches


How much of test dataset to check.

# default used by the Trainer
trainer = Trainer(limit_test_batches=1.0)

# run through only 25% of the test set each epoch
trainer = Trainer(limit_test_batches=0.25)

# run for only 10 batches
trainer = Trainer(limit_test_batches=10)

In the case of multiple test dataloaders, the limit applies to each dataloader individually.

limit_val_batches


How much of validation dataset to check. Useful when debugging or testing something that happens at the end of an epoch.

# default used by the Trainer
trainer = Trainer(limit_val_batches=1.0)

# run through only 25% of the validation set each epoch
trainer = Trainer(limit_val_batches=0.25)

# run for only 10 batches
trainer = Trainer(limit_val_batches=10)

In the case of multiple validation dataloaders, the limit applies to each dataloader individually.

log_gpu_memory


Options:

  • None

  • ‘min_max’

  • ‘all’

# default used by the Trainer
trainer = Trainer(log_gpu_memory=None)

# log all the GPUs (on master node only)
trainer = Trainer(log_gpu_memory='all')

# log only the min and max memory on the master node
trainer = Trainer(log_gpu_memory='min_max')

Note

Might slow performance because it uses the output of nvidia-smi.

flush_logs_every_n_steps


Writes logs to disk this often.

# default used by the Trainer
trainer = Trainer(flush_logs_every_n_steps=100)

See also

logger


Logger (or iterable collection of loggers) for experiment tracking.

from pytorch_lightning.loggers import TensorBoardLogger

# default logger used by trainer
logger = TensorBoardLogger(
    save_dir=os.getcwd(),
    version=1,
    name='lightning_logs'
)
Trainer(logger=logger)

max_epochs


Stop training once this number of epochs is reached

# default used by the Trainer
trainer = Trainer(max_epochs=1000)

min_epochs


Force training for at least these many epochs

# default used by the Trainer
trainer = Trainer(min_epochs=1)

max_steps


Stop training after this number of steps Training will stop if max_steps or max_epochs have reached (earliest).

# Default (disabled)
trainer = Trainer(max_steps=None)

# Stop after 100 steps
trainer = Trainer(max_steps=100)

min_steps


Force training for at least these number of steps. Trainer will train model for at least min_steps or min_epochs (latest).

# Default (disabled)
trainer = Trainer(min_steps=None)

# Run at least for 100 steps (disable min_epochs)
trainer = Trainer(min_steps=100, min_epochs=0)

num_nodes


Number of GPU nodes for distributed training.

# default used by the Trainer
trainer = Trainer(num_nodes=1)

# to train on 8 nodes
trainer = Trainer(num_nodes=8)

num_processes


Number of processes to train with. Automatically set to the number of GPUs when using distrbuted_backend="ddp". Set to a number greater than 1 when using distributed_backend="ddp_cpu" to mimic distributed training on a machine without GPUs. This is useful for debugging, but will not provide any speedup, since single-process Torch already makes effient use of multiple CPUs.

# Simulate DDP for debugging on your GPU-less laptop
trainer = Trainer(distributed_backend="ddp_cpu", num_processes=2)

num_sanity_val_steps


Sanity check runs n batches of val before starting the training routine. This catches any bugs in your validation without having to wait for the first validation check. The Trainer uses 2 steps by default. Turn it off or modify it here.

# default used by the Trainer
trainer = Trainer(num_sanity_val_steps=2)

# turn it off
trainer = Trainer(num_sanity_val_steps=0)

# check all validation data
trainer = Trainer(num_sanity_val_steps=-1)

Example:

python -m torch_xla.distributed.xla_dist
--tpu=$TPU_POD_NAME
--conda-env=torch-xla-nightly
--env=XLA_USE_BF16=1
-- python your_trainer_file.py

plugins


Plugins allow you to connect arbitrary backends, precision libraries, SLURM, etc… For example:

  • DDP

  • SLURM

  • TorchElastic

  • Apex

To define your own behavior, subclass the relevant class and pass it in. Here’s an example linking up your own cluster.

from pytorch_lightning.cluster_environments import cluster_environment

class MyCluster(ClusterEnvironment):

    def master_address(self):
        return your_master_address

    def master_port(self):
        return your_master_port

    def world_size(self):
        return the_world_size

trainer = Trainer(cluster_environment=cluster_environment())

prepare_data_per_node


If True will call prepare_data() on LOCAL_RANK=0 for every node. If False will only call from NODE_RANK=0, LOCAL_RANK=0

# default
Trainer(prepare_data_per_node=True)

# use only NODE_RANK=0, LOCAL_RANK=0
Trainer(prepare_data_per_node=False)

tpu_cores


  • How many TPU cores to train on (1 or 8).

  • Which TPU core to train on [1-8]

A single TPU v2 or v3 has 8 cores. A TPU pod has up to 2048 cores. A slice of a POD means you get as many cores as you request.

Your effective batch size is batch_size * total tpu cores.

Note

No need to add a DistributedDataSampler, Lightning automatically does it for you.

This parameter can be either 1 or 8.

# your_trainer_file.py

# default used by the Trainer (ie: train on CPU)
trainer = Trainer(tpu_cores=None)

# int: train on a single core
trainer = Trainer(tpu_cores=1)

# list: train on a single selected core
trainer = Trainer(tpu_cores=[2])

# int: train on all cores few cores
trainer = Trainer(tpu_cores=8)

# for 8+ cores must submit via xla script with
# a max of 8 cores specified. The XLA script
# will duplicate script onto each TPU in the POD
trainer = Trainer(tpu_cores=8)

To train on more than 8 cores (ie: a POD), submit this script using the xla_dist script.

Example:

python -m torch_xla.distributed.xla_dist
--tpu=$TPU_POD_NAME
--conda-env=torch-xla-nightly
--env=XLA_USE_BF16=1
-- python your_trainer_file.py

overfit_batches


Uses this much data of the training set. If nonzero, will use the same training set for validation and testing. If the training dataloaders have shuffle=True, Lightning will automatically disable it.

Useful for quickly debugging or trying to overfit on purpose.

# default used by the Trainer
trainer = Trainer(overfit_batches=0.0)

# use only 1% of the train set (and use the train set for val and test)
trainer = Trainer(overfit_batches=0.01)

# overfit on 10 of the same batches
trainer = Trainer(overfit_batches=10)

precision


Full precision (32), half precision (16). Can be used on CPU, GPU or TPUs.

If used on TPU will use torch.bfloat16 but tensor printing will still show torch.float32.

# default used by the Trainer
trainer = Trainer(precision=32)

# 16-bit precision
trainer = Trainer(precision=16)

Example:

# one day
trainer = Trainer(precision=8|4|2)

process_position


Orders the progress bar. Useful when running multiple trainers on the same node.

# default used by the Trainer
trainer = Trainer(process_position=0)

Note

This argument is ignored if a custom callback is passed to callbacks.

profiler


To profile individual steps during training and assist in identifying bottlenecks.

See the profiler documentation. for more details.

from pytorch_lightning.profiler import SimpleProfiler, AdvancedProfiler

# default used by the Trainer
trainer = Trainer(profiler=None)

# to profile standard training events
trainer = Trainer(profiler=True)

# equivalent to profiler=True
trainer = Trainer(profiler=SimpleProfiler())

# advanced profiler for function-level stats
trainer = Trainer(profiler=AdvancedProfiler())

progress_bar_refresh_rate


How often to refresh progress bar (in steps). In notebooks, faster refresh rates (lower number) is known to crash them because of their screen refresh rates, so raise it to 50 or more.

# default used by the Trainer
trainer = Trainer(progress_bar_refresh_rate=1)

# disable progress bar
trainer = Trainer(progress_bar_refresh_rate=0)

Note

This argument is ignored if a custom callback is passed to callbacks.

reload_dataloaders_every_epoch


Set to True to reload dataloaders every epoch.

# if False (default)
train_loader = model.train_dataloader()
for epoch in epochs:
    for batch in train_loader:
        ...

# if True
for epoch in epochs:
    train_loader = model.train_dataloader()
    for batch in train_loader:

replace_sampler_ddp


Enables auto adding of distributed sampler. By default it will add shuffle=True for train sampler and shuffle=False for val/test sampler. If you want to customize it, you can set replace_sampler_ddp=False and add your own distributed sampler.

# default used by the Trainer
trainer = Trainer(replace_sampler_ddp=True)

By setting to False, you have to add your own distributed sampler:

# default used by the Trainer
sampler = torch.utils.data.distributed.DistributedSampler(dataset, shuffle=True)
dataloader = DataLoader(dataset, batch_size=32, sampler=sampler)

resume_from_checkpoint


To resume training from a specific checkpoint pass in the path here.

# default used by the Trainer
trainer = Trainer(resume_from_checkpoint=None)

# resume from a specific checkpoint
trainer = Trainer(resume_from_checkpoint='some/path/to/my_checkpoint.ckpt')

log_every_n_steps


How often to add logging rows (does not write to disk)

# default used by the Trainer
trainer = Trainer(log_every_n_steps=50)

See also

sync_batchnorm


Enable synchronization between batchnorm layers across all GPUs.

trainer = Trainer(sync_batchnorm=True)

track_grad_norm


  • no tracking (-1)

  • Otherwise tracks that norm (2 for 2-norm)

# default used by the Trainer
trainer = Trainer(track_grad_norm=-1)

# track the 2-norm
trainer = Trainer(track_grad_norm=2)

limit_train_batches


How much of training dataset to check. Useful when debugging or testing something that happens at the end of an epoch.

# default used by the Trainer
trainer = Trainer(limit_train_batches=1.0)

Example:

# default used by the Trainer
trainer = Trainer(limit_train_batches=1.0)

# run through only 25% of the training set each epoch
trainer = Trainer(limit_train_batches=0.25)

# run through only 10 batches of the training set each epoch
trainer = Trainer(limit_train_batches=10)

truncated_bptt_steps


Truncated back prop breaks performs backprop every k steps of a much longer sequence.

If this is enabled, your batches will automatically get truncated and the trainer will apply Truncated Backprop to it.

(Williams et al. “An efficient gradient-based algorithm for on-line training of recurrent network trajectories.”)

# default used by the Trainer (ie: disabled)
trainer = Trainer(truncated_bptt_steps=None)

# backprop every 5 steps in a batch
trainer = Trainer(truncated_bptt_steps=5)

Note

Make sure your batches have a sequence dimension.

Lightning takes care to split your batch along the time-dimension.

# we use the second as the time dimension
# (batch, time, ...)
sub_batch = batch[0, 0:t, ...]

Using this feature requires updating your LightningModule’s pytorch_lightning.core.LightningModule.training_step() to include a hiddens arg with the hidden

# Truncated back-propagation through time
def training_step(self, batch, batch_idx, hiddens):
    # hiddens are the hiddens from the previous truncated backprop step
    out, hiddens = self.lstm(data, hiddens)

    return {
        "loss": ...,
        "hiddens": hiddens  # remember to detach() this
    }

To modify how the batch is split, override pytorch_lightning.core.LightningModule.tbptt_split_batch():

class LitMNIST(LightningModule):
    def tbptt_split_batch(self, batch, split_size):
        # do your own splitting on the batch
        return splits

val_check_interval


How often within one training epoch to check the validation set. Can specify as float or int.

  • use (float) to check within a training epoch

  • use (int) to check every n steps (batches)

# default used by the Trainer
trainer = Trainer(val_check_interval=1.0)

# check validation set 4 times during a training epoch
trainer = Trainer(val_check_interval=0.25)

# check validation set every 1000 training batches
# use this when using iterableDataset and your dataset has no length
# (ie: production cases with streaming data)
trainer = Trainer(val_check_interval=1000)

weights_save_path


Directory of where to save weights if specified.

# default used by the Trainer
trainer = Trainer(weights_save_path=os.getcwd())

# save to your custom path
trainer = Trainer(weights_save_path='my/path')

Example:

# if checkpoint callback used, then overrides the weights path
# **NOTE: this saves weights to some/path NOT my/path
checkpoint = ModelCheckpoint(filepath='some/path')
trainer = Trainer(
    checkpoint_callback=checkpoint,
    weights_save_path='my/path'
)

weights_summary


Prints a summary of the weights when training begins. Options: ‘full’, ‘top’, None.

# default used by the Trainer (ie: print summary of top level modules)
trainer = Trainer(weights_summary='top')

# print full summary of all modules and submodules
trainer = Trainer(weights_summary='full')

# don't print a summary
trainer = Trainer(weights_summary=None)

Trainer class API

class pytorch_lightning.trainer.Trainer(logger=True, checkpoint_callback=True, callbacks=None, default_root_dir=None, gradient_clip_val=0, process_position=0, num_nodes=1, num_processes=1, gpus=None, auto_select_gpus=False, tpu_cores=None, log_gpu_memory=None, progress_bar_refresh_rate=1, overfit_batches=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_epochs=1000, min_epochs=1, max_steps=None, min_steps=None, limit_train_batches=1.0, limit_val_batches=1.0, limit_test_batches=1.0, val_check_interval=1.0, flush_logs_every_n_steps=100, log_every_n_steps=50, accelerator=None, sync_batchnorm=False, precision=32, weights_summary='top', weights_save_path=None, num_sanity_val_steps=2, truncated_bptt_steps=None, resume_from_checkpoint=None, profiler=None, benchmark=False, deterministic=False, reload_dataloaders_every_epoch=False, auto_lr_find=False, replace_sampler_ddp=True, terminate_on_nan=False, auto_scale_batch_size=False, prepare_data_per_node=True, plugins=None, amp_backend='native', amp_level='O2', distributed_backend=None, automatic_optimization=True)[source]

Bases: pytorch_lightning.trainer.properties.TrainerProperties, pytorch_lightning.trainer.callback_hook.TrainerCallbackHookMixin, pytorch_lightning.trainer.model_hooks.TrainerModelHooksMixin, pytorch_lightning.trainer.optimizers.TrainerOptimizersMixin, pytorch_lightning.trainer.logging.TrainerLoggingMixin, pytorch_lightning.trainer.training_tricks.TrainerTrainingTricksMixin, pytorch_lightning.trainer.data_loading.TrainerDataLoadingMixin

Customize every aspect of training via flags

Parameters
  • accelerator (Union[str, Accelerator, None]) – Previously known as distributed_backend (dp, ddp, ddp2, etc…). Can also take in an accelerator object for custom hardware.

  • accumulate_grad_batches (Union[int, Dict[int, int], List[list]]) – Accumulates grads every k batches or as set up in the dict.

  • amp_backend (str) – The mixed precision backend to use (“native” or “apex”)

  • amp_level (str) – The optimization level to use (O1, O2, etc…).

  • auto_lr_find (Union[bool, str]) – If set to True, will make trainer.tune() run a learning rate finder, trying to optimize initial learning for faster convergence. trainer.tune() method will set the suggested learning rate in self.lr or self.learning_rate in the LightningModule. To use a different key set a string instead of True with the key name.

  • auto_scale_batch_size (Union[str, bool]) – If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory. The result will be stored in self.batch_size in the LightningModule. Additionally, can be set to either power that estimates the batch size through a power search or binsearch that estimates the batch size through a binary search.

  • auto_select_gpus (bool) – If enabled and gpus is an integer, pick available gpus automatically. This is especially useful when GPUs are configured to be in “exclusive mode”, such that only one process at a time can access them.

  • benchmark (bool) – If true enables cudnn.benchmark.

  • callbacks (Optional[List[Callback]]) – Add a list of callbacks.

  • checkpoint_callback (Union[ModelCheckpoint, bool]) – Callback for checkpointing.

  • check_val_every_n_epoch (int) – Check val every n train epochs.

  • default_root_dir (Optional[str]) – Default path for logs and weights when no logger/ckpt_callback passed. Default: os.getcwd(). Can be remote file paths such as s3://mybucket/path or ‘hdfs://path/’

  • deterministic (bool) – If true enables cudnn.deterministic.

  • distributed_backend (Optional[str]) – deprecated. Please use ‘accelerator’

  • fast_dev_run (bool) – runs 1 batch of train, test and val to find any bugs (ie: a sort of unit test).

  • flush_logs_every_n_steps (int) – How often to flush logs to disk (defaults to every 100 steps).

  • gpus (Union[int, str, List[int], None]) – number of gpus to train on (int) or which GPUs to train on (list or str) applied per node

  • gradient_clip_val (float) – 0 means don’t clip.

  • limit_train_batches (Union[int, float]) – How much of training dataset to check (floats = percent, int = num_batches)

  • limit_val_batches (Union[int, float]) – How much of validation dataset to check (floats = percent, int = num_batches)

  • limit_test_batches (Union[int, float]) – How much of test dataset to check (floats = percent, int = num_batches)

  • logger (Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool]) – Logger (or iterable collection of loggers) for experiment tracking.

  • log_gpu_memory (Optional[str]) – None, ‘min_max’, ‘all’. Might slow performance

  • log_every_n_steps (int) – How often to log within steps (defaults to every 50 steps).

  • automatic_optimization (bool) – If False you are responsible for calling .backward, .step, zero_grad. Meant to be used with multiple optimizers by advanced users.

  • prepare_data_per_node (bool) – If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data

  • process_position (int) – orders the progress bar when running multiple models on same machine.

  • progress_bar_refresh_rate (int) – How often to refresh progress bar (in steps). Value 0 disables progress bar. Ignored when a custom callback is passed to callbacks.

  • profiler (Union[BaseProfiler, bool, None]) – To profile individual steps during training and assist in identifying bottlenecks.

  • overfit_batches (Union[int, float]) – Overfit a percent of training data (float) or a set number of batches (int). Default: 0.0

  • plugins (Optional[list]) – Plugins allow modification of core behavior like ddp and amp.

  • precision (int) – Full precision (32), half precision (16). Can be used on CPU, GPU or TPUs.

  • max_epochs (int) – Stop training once this number of epochs is reached.

  • min_epochs (int) – Force training for at least these many epochs

  • max_steps (Optional[int]) – Stop training after this number of steps. Disabled by default (None).

  • min_steps (Optional[int]) – Force training for at least these number of steps. Disabled by default (None).

  • num_nodes (int) – number of GPU nodes for distributed training.

  • num_sanity_val_steps (int) – Sanity check runs n validation batches before starting the training routine. Set it to -1 to run all batches in all validation dataloaders. Default: 2

  • reload_dataloaders_every_epoch (bool) – Set to True to reload dataloaders every epoch.

  • replace_sampler_ddp (bool) – Explicitly enables or disables sampler replacement. If not specified this will toggled automatically when DDP is used. By default it will add shuffle=True for train sampler and shuffle=False for val/test sampler. If you want to customize it, you can set replace_sampler_ddp=False and add your own distributed sampler.

  • resume_from_checkpoint (Optional[str]) – To resume training from a specific checkpoint pass in the path here. This can be a URL.

  • sync_batchnorm (bool) – Synchronize batch norm layers between process groups/whole world.

  • terminate_on_nan (bool) – If set to True, will terminate training (by raising a ValueError) at the end of each training batch, if any of the parameters or the loss are NaN or +/-inf.

  • tpu_cores (Union[int, str, List[int], None]) – How many TPU cores to train on (1 or 8) / Single TPU to train on [1]

  • track_grad_norm (Union[int, float, str]) – -1 no tracking. Otherwise tracks that p-norm. May be set to ‘inf’ infinity-norm.

  • truncated_bptt_steps (Optional[int]) – Truncated back prop breaks performs backprop every k steps of much longer sequence.

  • val_check_interval (Union[int, float]) – How often to check the validation set. Use float to check within a training epoch, use int to check every n steps (batches).

  • weights_summary (Optional[str]) – Prints a summary of the weights when training begins.

  • weights_save_path (Optional[str]) – Where to save weights if specified. Will override default_root_dir for checkpoints only. Use this if for whatever reason you need the checkpoints stored in a different place than the logs written in default_root_dir. Can be remote file paths such as s3://mybucket/path or ‘hdfs://path/’ Defaults to default_root_dir.

fit(model, train_dataloader=None, val_dataloaders=None, datamodule=None)[source]

Runs the full optimization routine.

Parameters
  • datamodule (Optional[LightningDataModule]) – A instance of LightningDataModule.

  • model (LightningModule) – Model to fit.

  • train_dataloader (Optional[DataLoader]) – A Pytorch DataLoader with training samples. If the model has a predefined train_dataloader method this will be skipped.

  • val_dataloaders (Union[DataLoader, List[DataLoader], None]) – Either a single Pytorch Dataloader or a list of them, specifying validation samples. If the model has a predefined val_dataloaders method this will be skipped

test(model=None, test_dataloaders=None, ckpt_path='best', verbose=True, datamodule=None)[source]

Separates from fit to make sure you never run on your test set until you want to.

Parameters
  • ckpt_path (Optional[str]) – Either best or path to the checkpoint you wish to test. If None, use the weights from the last epoch to test. Default to best.

  • datamodule (Optional[LightningDataModule]) – A instance of LightningDataModule.

  • model (Optional[LightningModule]) – The model to test.

  • test_dataloaders (Union[DataLoader, List[DataLoader], None]) – Either a single Pytorch Dataloader or a list of them, specifying validation samples.

  • verbose (bool) – If True, prints the test results

Returns

The final test result dictionary. If no test_epoch_end is defined returns a list of dictionaries

tune(model, train_dataloader=None, val_dataloaders=None, datamodule=None)[source]

Runs routines to tune hyperparameters before training.

Parameters
  • datamodule (Optional[LightningDataModule]) – A instance of LightningDataModule.

  • model (LightningModule) – Model to tune.

  • train_dataloader (Optional[DataLoader]) – A Pytorch DataLoader with training samples. If the model has a predefined train_dataloader method this will be skipped.

  • val_dataloaders (Union[DataLoader, List[DataLoader], None]) – Either a single Pytorch Dataloader or a list of them, specifying validation samples. If the model has a predefined val_dataloaders method this will be skipped

pytorch_lightning.trainer.seed_everything(seed=None)[source]

Function that sets seed for pseudo-random number generators in: pytorch, numpy, python.random In addition, sets the env variable PL_GLOBAL_SEED which will be passed to spawned subprocesses (e.g. ddp_spawn backend).

Parameters

seed (Optional[int]) – the integer value seed for global random state in Lightning. If None, will read seed from PL_GLOBAL_SEED env variable or select it randomly.

Return type

int

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