add tqdm for getting a progress bar of the training

egs/librispeech/ASR/conformer_ctc/train.py

@@ -28,6 +28,7 @@ import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
 import torch.nn as nn
+from tqdm import tqdm
 from asr_datamodule import LibriSpeechAsrDataModule
 from conformer import Conformer
 from lhotse.utils import fix_random_seed
@@ -46,6 +47,7 @@ from icefall.utils import (
     encode_supervisions,
     setup_logger,
     str2bool,
+    is_main_process,
 )
 
 
@@ -478,7 +480,17 @@ def train_one_epoch(
     tot_frames = 0.0  # sum of frames over all batches
     params.tot_loss = 0.0
     params.tot_frames = 0.0
-    for batch_idx, batch in enumerate(train_dl):
+
+    with tqdm(
+        train_dl,
+        initial=0,
+        total=train_dl.sampler.num_cuts,
+        leave=False,
+        unit="cut",  # a data fragment concept comes from "lhotse"
+        dynamic_ncols=True,
+        disable=not is_main_process(),
+    ) as t:
+        for batch_idx, batch in enumerate(t):
             params.batch_idx_train += 1
             batch_size = len(batch["supervisions"]["text"])
 
@@ -514,12 +526,18 @@ def train_one_epoch(
             tot_avg_ctc_loss = tot_ctc_loss / tot_frames
             tot_avg_att_loss = tot_att_loss / tot_frames
 
+            t.set_postfix(total_avg_loss=tot_avg_loss)
+            t.update(batch_size)
+
             if batch_idx % params.log_interval == 0:
                 logging.info(
                     f"Epoch {params.cur_epoch}, batch {batch_idx}, "
-                f"batch avg ctc loss {ctc_loss_cpu/params.train_frames:.4f}, "
-                f"batch avg att loss {att_loss_cpu/params.train_frames:.4f}, "
-                f"batch avg loss {loss_cpu/params.train_frames:.4f}, "
+                    f"batch avg ctc loss \
+                        {ctc_loss_cpu/params.train_frames:.4f}, "
+                    f"batch avg att loss \
+                        {att_loss_cpu/params.train_frames:.4f}, "
+                    f"batch avg loss \
+                        {loss_cpu/params.train_frames:.4f}, "
                     f"total avg ctc loss: {tot_avg_ctc_loss:.4f}, "
                     f"total avg att loss: {tot_avg_att_loss:.4f}, "
                     f"total avg loss: {tot_avg_loss:.4f}, "
@@ -692,7 +710,7 @@ def run(rank, world_size, args):
             )
             tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
 
-        if rank == 0:
+        if is_main_process():
             logging.info("epoch {}, learning rate {}".format(epoch, cur_lr))
 
         params.cur_epoch = epoch

icefall/utils.py

@@ -411,3 +411,20 @@ def write_error_stats(
 
         print(f"{word}   {corr} {tot_errs} {ref_count} {hyp_count}", file=f)
     return float(tot_err_rate)
+
+
+def is_main_process():
+    """Check if the current process is main.
+
+    - For DistributedDataParallel (DDP) mode:
+        The current is main process if its rank is 0, and its rank get from
+        os.environ["RANK"] (which already be set by DDP mode).
+    - For standard mode and others:
+        The os.environ["RANK"] is None, and
+    """
+    if os.environ.get('RANK') is None:
+        return True
+    elif os.environ.get("RANK") == "0":
+        return True
+    else:
+        return False