diff --git a/mmaction/apis/__init__.py b/mmaction/apis/__init__.py
index 8a68055e70..15961080e7 100644
--- a/mmaction/apis/__init__.py
+++ b/mmaction/apis/__init__.py
@@ -1,9 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .inference import inference_recognizer, init_recognizer
 from .test import multi_gpu_test, single_gpu_test
-from .train import train_model
+from .train import init_random_seed, train_model
 
 __all__ = [
     'train_model', 'init_recognizer', 'inference_recognizer', 'multi_gpu_test',
-    'single_gpu_test'
+    'single_gpu_test', 'init_random_seed'
 ]
diff --git a/mmaction/apis/train.py b/mmaction/apis/train.py
index 54b1fb0163..f04ed6c712 100644
--- a/mmaction/apis/train.py
+++ b/mmaction/apis/train.py
@@ -2,7 +2,9 @@
 import copy as cp
 import os.path as osp
 
+import numpy as np
 import torch
+import torch.distributed as dist
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 from mmcv.runner import (DistSamplerSeedHook, EpochBasedRunner, OptimizerHook,
                          build_optimizer, get_dist_info)
@@ -15,6 +17,39 @@
 from .test import multi_gpu_test
 
 
+def init_random_seed(seed=None, device='cuda'):
+    """Initialize random seed.
+
+    If the seed is not set, the seed will be automatically randomized,
+    and then broadcast to all processes to prevent some potential bugs.
+    Args:
+        seed (int, Optional): The seed. Default to None.
+        device (str): The device where the seed will be put on.
+            Default to 'cuda'.
+    Returns:
+        int: Seed to be used.
+    """
+    if seed is not None:
+        return seed
+
+    # Make sure all ranks share the same random seed to prevent
+    # some potential bugs. Please refer to
+    # https://github.com/open-mmlab/mmdetection/issues/6339
+    rank, world_size = get_dist_info()
+    seed = np.random.randint(2**31)
+
+    if world_size == 1:
+        return seed
+
+    if rank == 0:
+        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
+    else:
+        random_num = torch.tensor(0, dtype=torch.int32, device=device)
+
+    dist.broadcast(random_num, src=0)
+    return random_num.item()
+
+
 def train_model(model,
                 dataset,
                 cfg,
diff --git a/tools/train.py b/tools/train.py
index 705473665e..e871c53088 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -13,7 +13,7 @@
 from mmcv.utils import get_git_hash
 
 from mmaction import __version__
-from mmaction.apis import train_model
+from mmaction.apis import init_random_seed, train_model
 from mmaction.datasets import build_dataset
 from mmaction.models import build_model
 from mmaction.utils import collect_env, get_root_logger, register_module_hooks
@@ -143,12 +143,13 @@ def main():
     logger.info(f'Config: {cfg.pretty_text}')
 
     # set random seeds
-    if args.seed is not None:
-        logger.info(f'Set random seed to {args.seed}, '
-                    f'deterministic: {args.deterministic}')
-        set_random_seed(args.seed, deterministic=args.deterministic)
-    cfg.seed = args.seed
-    meta['seed'] = args.seed
+    seed = init_random_seed(args.seed)
+    logger.info(f'Set random seed to {seed}, '
+                f'deterministic: {args.deterministic}')
+    set_random_seed(seed, deterministic=args.deterministic)
+
+    cfg.seed = seed
+    meta['seed'] = seed
     meta['config_name'] = osp.basename(args.config)
     meta['work_dir'] = osp.basename(cfg.work_dir.rstrip('/\\'))