[RLlib; Tune] Fix WandB metric overlap after restore from checkpoint.

python/ray/air/integrations/wandb.py

@@ -15,6 +15,7 @@
 from ray._private.storage import _load_class
 from ray.air import session
 from ray.air._internal import usage as air_usage
+from ray.air.constants import TRAINING_ITERATION
 from ray.air.util.node import _force_on_current_node
 from ray.train._internal.syncer import DEFAULT_SYNC_TIMEOUT
 from ray.tune.experiment import Trial
@@ -166,7 +167,7 @@ def _setup_wandb(
     project = _get_wandb_project(kwargs.pop("project", None))
     group = kwargs.pop("group", os.environ.get(WANDB_GROUP_ENV_VAR))
 
-    # remove unpickleable items
+    # Remove unpickleable items.
     _config = _clean_log(_config)
 
     wandb_init_kwargs = dict(
@@ -415,7 +416,7 @@ def run(self):
             log, config_update = self._handle_result(item_content)
             try:
                 self._wandb.config.update(config_update, allow_val_change=True)
-                self._wandb.log(log)
+                self._wandb.log(log, step=log.get(TRAINING_ITERATION))
             except urllib.error.HTTPError as e:
                 # Ignore HTTPError. Missing a few data points is not a
                 # big issue, as long as things eventually recover.

python/ray/air/tests/mocked_wandb_integration.py

@@ -65,7 +65,7 @@ def init(self, *args, **kwargs):
 
         return mock
 
-    def log(self, data):
+    def log(self, data, step=None):
         try:
             json_dumps_safer(data)
         except Exception:

rllib/examples/checkpoints/continue_training_from_checkpoint.py

@@ -241,7 +241,7 @@ def on_train_result(self, *, algorithm, metrics_logger, result, **kwargs):
     test_results = test_algo.train()
     assert (
         test_results[ENV_RUNNER_RESULTS][EPISODE_RETURN_MEAN] >= args.stop_reward_crash
-    )
+    ), test_results[ENV_RUNNER_RESULTS][EPISODE_RETURN_MEAN]
     # Stop the test algorithm again.
     test_algo.stop()