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lightning/pytorch_lightning/models/trainer.py

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"""
The trainer handles all the logic for running a val loop, training loop, distributing, etc...
"""
import subprocess
import traceback
import warnings
import os
import pdb
import re
import torch
from torch.utils.data.distributed import DistributedSampler
from torch.optim.lr_scheduler import MultiStepLR
import torch.multiprocessing as mp
import torch.distributed as dist
import numpy as np
import tqdm
from pytorch_lightning.root_module.memory import get_gpu_memory_map
from pytorch_lightning.root_module.model_saving import TrainerIO
from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistributedDataParallel, LightningDataParallel
from pytorch_lightning.utils.debugging import ForkedPdb
try:
from apex import amp
APEX_AVAILABLE = True
except ModuleNotFoundError:
APEX_AVAILABLE = False
def reduce_distributed_output(output, nb_gpus):
if nb_gpus <= 1:
return output
# when using DP, we get one output per gpu
# average outputs and return
if type(output) is torch.Tensor:
return output.mean()
for k, v in output.items():
# recurse on nested dics
if isinstance(output[k], dict):
output[k] = reduce_distributed_output(output[k], nb_gpus)
# reduce only metrics that have the same nb of gpus
elif output[k].size(0) == nb_gpus:
reduced = torch.mean(output[k])
output[k] = reduced
return output
class Trainer(TrainerIO):
def __init__(self,
experiment,
early_stop_callback=None,
checkpoint_callback=None,
gradient_clip=0,
cluster=None,
process_position=0,
current_gpu_name=0,
nb_gpu_nodes=1,
gpus=None,
progress_bar=True,
overfit_pct=0.0,
track_grad_norm=-1,
check_val_every_n_epoch=1,
fast_dev_run=False,
accumulate_grad_batches=1,
max_nb_epochs=1000, min_nb_epochs=1,
train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0,
val_check_interval=0.95,
log_save_interval=100, add_log_row_interval=10,
lr_scheduler_milestones=None,
distributed_backend='dp',
use_amp=False,
print_nan_grads=False,
print_weights_summary=True,
amp_level='O2',
nb_sanity_val_steps=5):
"""
:param experiment: Test-tube experiment
:param early_stop_callback: from pytorch_lightning import EarlyStopping
:param checkpoint_callback: from pytorch_lightning import Checkpoint
:param gradient_clip:
:param cluster:
:param process_position:
:param current_gpu_name:
:param nb_gpu_nodes:
:param gpus:
:param progress_bar:
:param overfit_pct:
:param track_grad_norm:
:param check_val_every_n_epoch:
:param fast_dev_run:
:param accumulate_grad_batches:
:param max_nb_epochs:
:param min_nb_epochs:
:param train_percent_check:
:param val_percent_check:
:param test_percent_check:
:param val_check_interval:
:param log_save_interval:
:param add_log_row_interval:
:param lr_scheduler_milestones:
:param distributed_backend: 'np' to use DistributedParallel, 'ddp' to use DistributedDataParallel
:param use_amp:
:param print_nan_grads:
:param print_weights_summary:
:param amp_level:
:param nb_sanity_val_steps:
"""
# Transfer params
self.nb_gpu_nodes = nb_gpu_nodes
self.gradient_clip = gradient_clip
self.check_val_every_n_epoch = check_val_every_n_epoch
self.enable_early_stop = early_stop_callback is not None
self.track_grad_norm = track_grad_norm
self.fast_dev_run = fast_dev_run
self.on_gpu = gpus is not None and torch.cuda.is_available()
self.progress_bar = progress_bar
self.experiment = experiment
self.exp_save_path = experiment.get_data_path(experiment.name, experiment.version)
self.cluster = cluster
self.process_position = process_position
self.current_gpu_name = current_gpu_name
self.print_weights_summary = print_weights_summary
self.checkpoint_callback = checkpoint_callback
if self.checkpoint_callback is not None:
self.checkpoint_callback.save_function = self.save_checkpoint
self.early_stop = early_stop_callback
self.model = None
self.max_nb_epochs = max_nb_epochs
self.accumulate_grad_batches = accumulate_grad_batches
self.early_stop_callback = early_stop_callback
self.min_nb_epochs = min_nb_epochs
self.nb_sanity_val_steps = nb_sanity_val_steps
self.lr_scheduler_milestones = [] if lr_scheduler_milestones is None else [int(x.strip()) for x in lr_scheduler_milestones.split(',')]
self.lr_schedulers = []
self.amp_level = amp_level
self.print_nan_grads = print_nan_grads
self.data_parallel_device_ids = None
self.world_size = 1
self.node_rank = 0
self.use_ddp = False
self.use_dp = False
# training bookeeping
self.total_batch_nb = 0
self.running_loss = []
self.avg_loss = 0
self.batch_nb = 0
self.tqdm_metrics = {}
self.nb_val_batches = None
self.nb_tng_batches = None
self.nb_test_batches = None
# gpus come in as a string.
# if gpus = -1 then use all available devices
# otherwise, split the string using commas
if gpus is not None:
if type(gpus) is list:
self.data_parallel_device_ids = gpus
elif type(gpus) is str:
if gpus == '-1':
self.data_parallel_device_ids = list(range(0, torch.cuda.device_count()))
else:
self.data_parallel_device_ids = [int(x.strip()) for x in gpus.split(',')]
else:
raise Exception('gpus has to be a string or list of ids')
# set the correct cuda visible devices (using pci order)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join([str(x) for x in self.data_parallel_device_ids])
print(f'VISIBLE GPUS: {os.environ["CUDA_VISIBLE_DEVICES"]}')
# make DP and DDP mutually exclusive
# single GPU will also use DP with devices=[0]
have_gpus = self.data_parallel_device_ids is not None and len(self.data_parallel_device_ids) > 0
if have_gpus:
self.use_dp = distributed_backend == 'dp'
self.use_ddp = distributed_backend == 'ddp'
# use ddp automatically if nb_gpu_nodes > 1
if nb_gpu_nodes > 1 and self.use_dp:
self.use_ddp = True
self.use_dp = False
w = 'DataParallel does not support nb_gpu_nodes > 1. ' \
'Switching to DistributedDataParallel for you. ' \
'To silence this warning set distributed_backend=ddp'
warnings.warn(w)
# process info
self.proc_rank = 0
# training state
self.optimizers = None
self.prog_bar = None
self.global_step = 0
self.current_epoch = 0
self.total_batches = 0
# logging
self.log_save_interval = log_save_interval
self.val_check_interval = val_check_interval
self.add_log_row_interval = add_log_row_interval
# dataloaders
self.tng_dataloader = None
self.test_dataloader = None
self.val_dataloader = None
# how much of the data to use
self.__determine_data_use_amount(train_percent_check, val_percent_check, test_percent_check, overfit_pct)
print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))
# 16 bit mixed precision training using apex
self.use_amp = use_amp and APEX_AVAILABLE
if self.use_amp:
print('using 16bit precision')
if use_amp and not APEX_AVAILABLE:
msg = '''
You set use_amp=True but do not have apex installed.
Install apex first using this guide and rerun with use_amp=True:
https://github.com/NVIDIA/apex#linux
this run will NOT use 16 bit precision
'''
raise ModuleNotFoundError(msg)
@property
def data_parallel(self):
return self.use_dp or self.use_ddp
def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct):
"""
Use less data for debugging purposes
"""
self.train_percent_check = train_percent_check
self.val_percent_check = val_percent_check
self.test_percent_check = test_percent_check
if overfit_pct > 0:
self.train_percent_check = overfit_pct
self.val_percent_check = overfit_pct
self.test_percent_check = overfit_pct
def __get_model(self):
return self.model.module if self.data_parallel else self.model
def __is_function_implemented(self, f_name):
model = self.__get_model()
f_op = getattr(model, f_name, None)
return callable(f_op)
@property
def __tng_tqdm_dic(self):
# ForkedPdb().set_trace()
tqdm_dic = {
'tng_loss': '{0:.3f}'.format(self.avg_loss),
'v_nb': '{}'.format(self.experiment.version),
'epoch': '{}'.format(self.current_epoch),
'batch_nb':'{}'.format(self.batch_nb),
}
tqdm_dic.update(self.tqdm_metrics)
if self.on_gpu:
tqdm_dic['gpu'] = '{}'.format(self.current_gpu_name)
return tqdm_dic
@property
def tng_tqdm_dic(self):
"""
Read-only for tqdm metrics
:return:
"""
return self.__tng_tqdm_dic
def __layout_bookeeping(self):
# determine number of training batches
self.nb_tng_batches = len(self.tng_dataloader)
self.nb_tng_batches = int(self.nb_tng_batches * self.train_percent_check)
# determine number of validation batches
self.nb_val_batches = len(self.val_dataloader)
self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)
self.nb_val_batches = max(1, self.nb_val_batches)
self.nb_val_batches = self.nb_val_batches
# determine number of test batches
self.nb_test_batches = len(self.test_dataloader)
self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)
# determine when to check validation
self.val_check_batch = int(self.nb_tng_batches * self.val_check_interval)
def __add_tqdm_metrics(self, metrics):
for k, v in metrics.items():
if type(v) is torch.Tensor:
v = v.item()
self.tqdm_metrics[k] = v
def validate(self, model, dataloader, max_batches):
"""
Run validation code
:param model: PT model
:param dataloader: PT dataloader
:param max_batches: Scalar
:return:
"""
# enable eval mode
model.zero_grad()
model.eval()
# disable gradients to save memory
torch.set_grad_enabled(False)
# bookkeeping
outputs = []
# run training
for batch_i, data_batch in enumerate(dataloader):
if data_batch is None:
continue
# stop short when on fast dev run
if max_batches is not None and batch_i >= max_batches:
break
# -----------------
# RUN VALIDATION STEP
# -----------------
if self.use_ddp:
output = model(data_batch, batch_i)
elif self.use_dp:
output = model(data_batch, batch_i)
output = reduce_distributed_output(output, len(self.data_parallel_device_ids))
else:
output = model.validation_step(data_batch, batch_i)
outputs.append(output)
# batch done
if self.progress_bar and self.prog_bar is not None:
self.prog_bar.update(1)
# give model a chance to do something with the outputs
if self.data_parallel:
val_results = model.module.validation_end(outputs)
else:
val_results = model.validation_end(outputs)
# enable train mode again
model.train()
# enable gradients to save memory
torch.set_grad_enabled(True)
return val_results
def __get_dataloaders(self, model):
"""
Dataloaders are provided by the model
:param model:
:return:
"""
self.tng_dataloader = model.tng_dataloader
self.test_dataloader = model.test_dataloader
self.val_dataloader = model.val_dataloader
if self.use_ddp and not isinstance(self.tng_dataloader.sampler, DistributedSampler):
msg = '''
when using multiple gpus and multiple nodes you must pass a DistributedSampler to DataLoader(sampler).
ie: this:
dataset = myDataset()
dataloader = Dataloader(dataset)
becomes:
dataset = myDataset()
dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = Dataloader(dataset, sampler=dist_sampler)
'''
raise Exception(msg)
# -----------------------------
# MODEL TRAINING
# -----------------------------
def fit(self, model):
# when using multi-node or DDP within a node start each module in a separate process
if self.use_ddp:
# must copy only the meta of the exp so it survives pickle/unpickle when going to new process
self.experiment = self.experiment.get_meta_copy()
# whenever we have the correct number of tasks, we let slurm manage processes
# otherwise we launch the required number of processes
nb_requested_gpus = len(self.data_parallel_device_ids) * self.nb_gpu_nodes
nb_slurm_tasks = 0
try:
nb_slurm_tasks = int(os.environ['SLURM_NTASKS'])
is_slurm_managing_tasks = nb_slurm_tasks == nb_requested_gpus
except Exception as e:
# likely not on slurm, so set the slurm managed flag to false
is_slurm_managing_tasks = False
if is_slurm_managing_tasks:
task = int(os.environ['SLURM_LOCALID'])
self.ddp_train(task, model)
else:
msg = f"""
You requested {nb_requested_gpus} GPUs but launched {nb_slurm_tasks} slurm tasks.
We will launch {nb_requested_gpus} processes for you.
We recommend you let slurm manage the processes by setting: --ntasks-per-node={nb_requested_gpus}
If you're not using SLURM, ignore this message!
"""
warnings.warn(msg)
mp.spawn(self.ddp_train, nprocs=len(self.data_parallel_device_ids), args=(model, ))
# 1 gpu or dp option triggers training using DP module
# easier to avoid NCCL issues
elif self.use_dp:
self.__dp_train(model)
# ON CPU
else:
# CHOOSE OPTIMIZER
# filter out the weights that were done on gpu so we can load on good old cpus
self.optimizers = model.configure_optimizers()
# run through amp wrapper
if self.use_amp:
# An example
model, optimizers = amp.initialize(
model, self.optimizers, opt_level=self.amp_level,
)
self.optimizers = optimizers
self.__run_pretrain_routine(model)
# return 1 when finished
# used for testing or when we need to know that training succeeded
return 1
def __dp_train(self, model):
# CHOOSE OPTIMIZER
# filter out the weights that were done on gpu so we can load on good old cpus
self.optimizers = model.configure_optimizers()
# run through amp wrapper
if self.use_amp:
# An example
model, optimizers = amp.initialize(
model, self.optimizers, opt_level=self.amp_level,
)
self.optimizers = optimizers
model = LightningDataParallel(model, device_ids=self.data_parallel_device_ids)
self.__run_pretrain_routine(model)
def ddp_train(self, gpu_nb, model):
"""
Entry point into a DP thread
:param gpu_nb:
:param model:
:param cluster_obj:
:return:
"""
# node rank using relative slurm id
# otherwise default to node rank 0
try:
node_id = os.environ['SLURM_NODEID']
self.node_rank = int(node_id)
except Exception as e:
self.node_rank = 0
# recover original exp before went into process
# init in write mode only on proc 0
self.experiment.debug = self.proc_rank > 0
self.experiment = self.experiment.get_non_ddp_exp()
# show progbar only on prog_rank 0
self.prog_bar = self.prog_bar and self.node_rank == 0 and gpu_nb == 0
# determine which process we are and world size
self.proc_rank = self.node_rank * len(self.data_parallel_device_ids) + gpu_nb
self.world_size = self.nb_gpu_nodes * len(self.data_parallel_device_ids)
# set up server using proc 0's ip address
# try to init for 20 times at max in case ports are taken
# where to store ip_table
self.__init_tcp_connection()
# CHOOSE OPTIMIZER
# filter out the weights that were done on gpu so we can load on good old cpus
self.optimizers = model.configure_optimizers()
# MODEL
# copy model to each gpu
torch.cuda.set_device(gpu_nb)
model.cuda(gpu_nb)
# AMP
# run through amp wrapper before going to distributed DP
if self.use_amp:
# An example
model, optimizers = amp.initialize(
model, self.optimizers, opt_level=self.amp_level,
)
self.optimizers = optimizers
model = LightningDistributedDataParallel(model, device_ids=[gpu_nb], find_unused_parameters=True)
# continue training routine
self.__run_pretrain_routine(model)
def __init_tcp_connection(self):
"""
Connect all procs in the world using the env:// init
Use the first node as the root address
:param port:
:param tries:
:return:
"""
try:
port = os.environ['MASTER_PORT']
except Exception as e:
port = 12910
os.environ['MASTER_PORT'] = f'{port}'
root_node = self.__resolve_root_node_address()
os.environ['MASTER_ADDR'] = root_node
dist.init_process_group("nccl", rank=self.proc_rank, world_size=self.world_size)
def __resolve_root_node_address(self):
try:
root_node = os.environ['SLURM_NODELIST'].split(' ')[0]
if '[' in root_node:
name = root_node.split('[')[0]
number = root_node.split(',')[0]
if '-' in number:
number = number.split('-')[0]
number = re.sub('[^0-9]', '', number)
root_node = name + number
except Exception as e:
root_node = '127.0.0.2'
return root_node
def __run_pretrain_routine(self, model):
"""
Sanity check a few things before starting actual training
:param model:
:return:
"""
ref_model = model
if self.data_parallel:
ref_model = model.module
ref_model.trainer = self
# set local properties on the model
ref_model.on_gpu = self.on_gpu
# transfer data loaders from model
self.__get_dataloaders(ref_model)
# init training constants
self.__layout_bookeeping()
# add lr schedulers
if self.lr_scheduler_milestones is not None:
for optimizer in self.optimizers:
scheduler = MultiStepLR(optimizer, self.lr_scheduler_milestones)
self.lr_schedulers.append(scheduler)
# print model summary
if self.proc_rank == 0 and self.print_weights_summary:
ref_model.summarize()
# give model convenience properties
ref_model.trainer = self
ref_model.experiment = self.experiment
# run tiny validation to make sure program won't crash during val
_ = self.validate(model, self.val_dataloader, max_batches=self.nb_sanity_val_steps)
# save exp to get started
if self.proc_rank == 0:
self.experiment.save()
# enable cluster checkpointing
if self.cluster is not None:
self.enable_auto_hpc_walltime_manager()
# ---------------------------
# CORE TRAINING LOOP
# ---------------------------
self.model = model
self.__train()
def __train(self):
# run all epochs
for epoch_nb in range(self.current_epoch, self.max_nb_epochs):
# update the lr scheduler
for lr_scheduler in self.lr_schedulers:
lr_scheduler.step()
model = self.__get_model()
model.current_epoch = epoch_nb
# hook
if self.__is_function_implemented('on_epoch_start'):
model = self.__get_model()
model.on_epoch_start()
self.current_epoch = epoch_nb
self.total_batches = self.nb_tng_batches + self.nb_val_batches
self.batch_loss_value = 0 # accumulated grads
# init progbar when requested
if self.progress_bar:
self.prog_bar = tqdm.tqdm(range(self.total_batches), position=self.process_position)
for batch_nb, data_batch in enumerate(self.tng_dataloader):
self.batch_nb = batch_nb
self.global_step += 1
model = self.__get_model()
model.global_step = self.global_step
# stop when the flag is changed or we've gone past the amount requested in the batches
self.total_batch_nb += 1
met_batch_limit = batch_nb > self.nb_tng_batches
if met_batch_limit:
break
# ---------------
# RUN TRAIN STEP
# ---------------
batch_result = self.__run_tng_batch(data_batch, batch_nb)
early_stop_epoch = batch_result == -1
# ---------------
# RUN VAL STEP
# ---------------
is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0
if self.fast_dev_run or is_val_check_batch or early_stop_epoch:
self.__run_validation()
# when batch should be saved
if (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch:
if self.proc_rank == 0:
self.experiment.save()
# when metrics should be logged
if batch_nb % self.add_log_row_interval == 0 or early_stop_epoch:
# count items in memory
# nb_params, nb_tensors = count_mem_items()
model = self.__get_model()
metrics = model.update_tng_log_metrics(self.__tng_tqdm_dic)
# add gpu memory
if self.on_gpu:
mem_map = get_gpu_memory_map()
metrics.update(mem_map)
# add norms
if self.track_grad_norm > 0:
model = self.__get_model()
grad_norm_dic = model.grad_norm(self.track_grad_norm)
metrics.update(grad_norm_dic)
if self.__is_function_implemented('on_tng_metrics'):
model.on_tng_metrics(metrics)
# log metrics
scalar_metrics = self.__metrics_to_scalars(metrics, blacklist=self.__log_vals_blacklist())
if self.proc_rank == 0:
self.experiment.log(scalar_metrics, global_step=self.global_step)
self.experiment.save()
# hook
if self.__is_function_implemented('on_batch_end'):
model = self.__get_model()
model.on_batch_end()
# end epoch early
if early_stop_epoch:
break
# hook
if self.__is_function_implemented('on_epoch_end'):
model = self.__get_model()
model.on_epoch_end()
# early stopping
met_min_epochs = epoch_nb > self.min_nb_epochs
if self.enable_early_stop and met_min_epochs:
should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch_nb, logs=self.__tng_tqdm_dic)
# stop training
stop = should_stop and met_min_epochs
if stop:
return
def __metrics_to_scalars(self, metrics, blacklist=[]):
new_metrics = {}
for k, v in metrics.items():
if type(v) is torch.Tensor:
v = v.item()
if type(v) is dict:
v = self.__metrics_to_scalars(v)
if k not in blacklist:
new_metrics[k] = float(v)
return new_metrics
def __log_vals_blacklist(self):
"""avoid logging some vals lightning uses to maintain state"""
blacklist = {'batch_nb', 'v_nb', 'gpu'}
return blacklist
def __run_tng_batch(self, data_batch, batch_nb):
if data_batch is None:
return 0
# hook
if self.__is_function_implemented('on_batch_start'):
model_ref = self.__get_model()
response = model_ref.on_batch_start(data_batch)
if response == -1:
return -1
if self.progress_bar:
self.prog_bar.update(1)
# forward pass
# return a scalar value and a dic with tqdm metrics
if self.use_ddp:
output = self.model(data_batch, batch_nb)
elif self.use_dp:
output = self.model(data_batch, batch_nb)
output = reduce_distributed_output(output, len(self.data_parallel_device_ids))
else:
output = self.model.training_step(data_batch, batch_nb)
try:
model_specific_tqdm_metrics_dic = output['tqdm_metrics']
except Exception as e:
model_specific_tqdm_metrics_dic = {}
# if output dict doesn't have the keyword loss
# then assume the output=loss if scalar
try:
loss = output['loss']
except Exception as e:
if type(output) is torch.Tensor:
loss = output
self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
# backward pass
if self.use_amp:
# scale loss when using amp
for optimizer in self.optimizers:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# insert after step hook
if self.__is_function_implemented('on_after_backward'):
model_ref = self.__get_model()
response = model_ref.on_after_backward()
if self.print_nan_grads:
model = self.__get_model()
for param in model.parameters():
print(param.grad.float().sum())
# avoid memory leaks
self.batch_loss_value += loss.item()
# gradient update with accumulated gradients
if (self.batch_nb + 1) % self.accumulate_grad_batches == 0:
# clip gradients
if self.gradient_clip > 0:
model = self.__get_model()
torch.nn.utils.clip_grad_norm(model.parameters(), self.gradient_clip)
# update gradients across all optimizers
for optimizer in self.optimizers:
optimizer.step()
# insert after step hook
if self.__is_function_implemented('on_before_zero_grad'):
model_ref = self.__get_model()
response = model_ref.on_before_zero_grad(optimizer)
# clear gradients
optimizer.zero_grad()
# queuing loss across batches blows it up proportionally... divide out the number accumulated
self.batch_loss_value = self.batch_loss_value / self.accumulate_grad_batches
# track loss
self.running_loss.append(self.batch_loss_value)
self.batch_loss_value = 0
self.avg_loss = np.mean(self.running_loss[-100:])
# update progbar
if self.progress_bar:
# add model specific metrics
tqdm_metrics = self.__tng_tqdm_dic
self.prog_bar.set_postfix(**tqdm_metrics)
# activate batch end hook
if self.__is_function_implemented('on_batch_end'):
model = self.__get_model()
model.on_batch_end()
return 0
def __run_validation(self):
# decide if can check epochs
can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
if self.fast_dev_run:
print('skipping to check performance bc of --fast_dev_run')
elif not can_check_epoch:
return
try:
# hook
if self.__is_function_implemented('on_pre_performance_check'):
model = self.__get_model()
model.on_pre_performance_check()
# use full val set on end of epoch
# use a small portion otherwise
max_batches = None if not self.fast_dev_run else 1
model_specific_tqdm_metrics_dic = self.validate(
self.model,
self.val_dataloader,
max_batches
)
self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
# hook
if self.__is_function_implemented('on_post_performance_check'):
model = self.__get_model()
model.on_post_performance_check()
except Exception as e:
print(e)
print(traceback.print_exc())
if self.progress_bar:
# add model specific metrics
tqdm_metrics = self.__tng_tqdm_dic
self.prog_bar.set_postfix(**tqdm_metrics)
# model checkpointing
if self.proc_rank == 0 and self.checkpoint_callback:
print('save callback...')
self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch, logs=self.__tng_tqdm_dic)
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