--- a
+++ b/tests/test_runtime/test_train.py
@@ -0,0 +1,134 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import tempfile
+from collections import OrderedDict
+
+import pytest
+import torch
+import torch.nn as nn
+from mmcv import Config
+from torch.utils.data import Dataset
+
+from mmaction.apis import train_model
+from mmaction.datasets import DATASETS
+
+
+@DATASETS.register_module()
+class ExampleDataset(Dataset):
+
+    def __init__(self, test_mode=False):
+        self.test_mode = test_mode
+
+    @staticmethod
+    def evaluate(results, logger=None):
+        eval_results = OrderedDict()
+        eval_results['acc'] = 1
+        return eval_results
+
+    def __getitem__(self, idx):
+        results = dict(imgs=torch.tensor([1]))
+        return results
+
+    def __len__(self):
+        return 1
+
+
+class ExampleModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.test_cfg = None
+        self.conv1 = nn.Conv2d(3, 8, kernel_size=1)
+        self.norm1 = nn.BatchNorm1d(2)
+
+    def forward(self, imgs, return_loss=False):
+        self.norm1(torch.rand(3, 2).cuda())
+        losses = dict()
+        losses['test_loss'] = torch.tensor([0.5], requires_grad=True)
+        return losses
+
+    def train_step(self, data_batch, optimizer, **kwargs):
+        imgs = data_batch['imgs']
+        losses = self.forward(imgs, True)
+        loss = torch.tensor([0.5], requires_grad=True)
+        outputs = dict(loss=loss, log_vars=losses, num_samples=3)
+        return outputs
+
+    def val_step(self, data_batch, optimizer, **kwargs):
+        imgs = data_batch['imgs']
+        self.forward(imgs, False)
+        outputs = dict(results=0.5)
+        return outputs
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available(), reason='requires CUDA support')
+def test_train_model():
+    model = ExampleModel()
+    dataset = ExampleDataset()
+    datasets = [ExampleDataset(), ExampleDataset()]
+    _cfg = dict(
+        seed=0,
+        gpus=1,
+        gpu_ids=[0],
+        resume_from=None,
+        load_from=None,
+        workflow=[('train', 1)],
+        total_epochs=5,
+        evaluation=dict(interval=1, save_best='acc'),
+        data=dict(
+            videos_per_gpu=1,
+            workers_per_gpu=0,
+            val=dict(type='ExampleDataset')),
+        optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001),
+        optimizer_config=dict(grad_clip=dict(max_norm=40, norm_type=2)),
+        lr_config=dict(policy='step', step=[40, 80]),
+        omnisource=False,
+        precise_bn=False,
+        checkpoint_config=dict(interval=1),
+        log_level='INFO',
+        log_config=dict(interval=20, hooks=[dict(type='TextLoggerHook')]))
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # normal train
+        cfg = copy.deepcopy(_cfg)
+        cfg['work_dir'] = tmpdir
+        config = Config(cfg)
+        train_model(model, dataset, config)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # train with validation
+        cfg = copy.deepcopy(_cfg)
+        cfg['work_dir'] = tmpdir
+        config = Config(cfg)
+        train_model(model, dataset, config, validate=True)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # train with Fp16OptimizerHook
+        cfg = copy.deepcopy(_cfg)
+        cfg['work_dir'] = tmpdir
+        cfg['fp16'] = dict(loss_scale=512.)
+        config = Config(cfg)
+        model.fp16_enabled = None
+        train_model(model, dataset, config)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        cfg = copy.deepcopy(_cfg)
+        cfg['work_dir'] = tmpdir
+        cfg['omnisource'] = True
+        config = Config(cfg)
+        train_model(model, datasets, config)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # train with precise_bn on
+        cfg = copy.deepcopy(_cfg)
+        cfg['work_dir'] = tmpdir
+        cfg['workflow'] = [('train', 1), ('val', 1)]
+        cfg['data'] = dict(
+            videos_per_gpu=1,
+            workers_per_gpu=0,
+            train=dict(type='ExampleDataset'),
+            val=dict(type='ExampleDataset'))
+        cfg['precise_bn'] = dict(num_iters=1, interval=1)
+        config = Config(cfg)
+        train_model(model, datasets, config)