diff --git a/examples/by_feature/fsdp_with_peak_mem_tracking.py b/examples/by_feature/fsdp_with_peak_mem_tracking.py
index 4f0311785..421a79d6b 100644
--- a/examples/by_feature/fsdp_with_peak_mem_tracking.py
+++ b/examples/by_feature/fsdp_with_peak_mem_tracking.py
@@ -112,11 +112,8 @@ def training_function(config, args):
     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
         if accelerator.is_main_process:
-            run = os.path.split(__file__)[-1].split(".")[0]
-            if args.logging_dir:
-                run = os.path.join(args.logging_dir, run)
-                accelerator.print(run)
-            accelerator.init_trackers(run, config)
+            experiment_config = vars(args)
+            accelerator.init_trackers("fsdp_glue_no_trainer", experiment_config)
 
     tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
     datasets = load_dataset("glue", "mrpc")
diff --git a/src/accelerate/accelerator.py b/src/accelerate/accelerator.py
index 1b3fa27e9..99afddeb8 100644
--- a/src/accelerate/accelerator.py
+++ b/src/accelerate/accelerator.py
@@ -188,8 +188,8 @@ def __init__(
             deepspeed_plugin.set_deepspeed_weakref()
 
         if os.environ.get("USE_FSDP", "false") == "true" or isinstance(fsdp_plugin, FullyShardedDataParallelPlugin):
-            if is_torch_version("<", "1.12.0.dev20220418+cu113"):
-                raise ValueError("FSDP requires PyTorch >= 1.12.0.dev20220418+cu113")
+            if is_torch_version("<", "1.12.0"):
+                raise ValueError("FSDP requires PyTorch >= 1.12.0")
 
         if fsdp_plugin is None:  # init from env variables
             fsdp_plugin = FullyShardedDataParallelPlugin() if os.environ.get("USE_FSDP", "false") == "true" else None
diff --git a/src/accelerate/utils/dataclasses.py b/src/accelerate/utils/dataclasses.py
index 3ccf2012b..28fc3826d 100644
--- a/src/accelerate/utils/dataclasses.py
+++ b/src/accelerate/utils/dataclasses.py
@@ -442,7 +442,7 @@ class FullyShardedDataParallelPlugin:
 
     def __post_init__(self):
         from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, ShardingStrategy
-        from torch.distributed.fsdp.wrap import default_auto_wrap_policy
+        from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy
 
         if self.sharding_strategy is None:
             self.sharding_strategy = ShardingStrategy(int(os.environ.get("FSDP_SHARDING_STRATEGY", 1)))
@@ -458,4 +458,6 @@ def __post_init__(self):
 
         if self.auto_wrap_policy is None:
             if self.min_num_params > 0:
-                self.auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=self.min_num_params)
+                self.auto_wrap_policy = functools.partial(
+                    size_based_auto_wrap_policy, min_num_params=self.min_num_params
+                )



Discussion: body: @pacman100 I'd recommend perhaps doing a version check to see what version of torch we're on to import one vs the other?
body: _The documentation is not available anymore as the PR was closed or merged._
body: > @pacman100 I'd recommend perhaps doing a version check to see what version of torch we're on to import one vs the other?

Done. As the FSDP support needs torch nightlies, just bumping up the version is viable.