Fix minor

examples/tune/README.md

@@ -19,5 +19,5 @@ python examples/tune/find_lr.py
 Distributed data parallel tuning
 
 ```bash
-torchrun -nnodes 1 -nproc_per_node 8 examples/tune/find_lr.py --launcher pytorch
+torchrun --nnodes 1 --nproc_per_node 8 examples/tune/find_lr.py --launcher pytorch
 ```

examples/tune/find_lr.py

@@ -40,7 +40,7 @@ def forward(self, inputs, data_samples=None, mode='tensor'):
 
 class ToyDataset(Dataset):
     METAINFO = dict()  # type: ignore
-    num_samples = 1000
+    num_samples = 100
     data = torch.rand(num_samples, 2) * 10
     label = 3 * data[:, 0] + 4 * data[:, 1] + torch.randn(num_samples) * 0.1
 
@@ -129,12 +129,12 @@ def main():
             'optim_wrapper.optimizer.lr': {
                 'type': 'continuous',
                 'lower': 1e-5,
-                'upper': 1e-1
+                'upper': 1e-3
             }
         },
         monitor='loss',
         rule='less',
-        num_trials=32,
+        num_trials=16,
         tuning_epoch=2,
         searcher_cfg=dict(type='NevergradSearcher'),
     )

mmengine/tune/tuner.py

@@ -6,7 +6,6 @@
 from mmengine.dist import (broadcast_object_list, init_dist, is_distributed,
                            is_main_process)
 from mmengine.logging import MMLogger
-from mmengine.runner import Runner
 from ._report_hook import ReportingHook
 from .searchers import HYPER_SEARCHERS, Searcher
 
@@ -241,6 +240,8 @@ def _run_trial(self) -> Tuple[Dict, float, Optional[Exception]]:
         # 2. Once retrieved, the hyperparameters are broadcasted to all other
         #   processes ensuring every process has the same set of
         #   hyperparameters for this trial.
+        from mmengine.runner import Runner
+
         if is_main_process():
             hparams_to_broadcast = [self._searcher.suggest()]
         else: