Hyperparameter tracking

fine_tune.py

@@ -310,7 +310,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             init_kwargs["wandb"] = {"name": args.wandb_run_name}
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
-        accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
+        accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs)
 
     # For --sample_at_first
     train_util.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)

library/train_util.py

@@ -3378,6 +3378,33 @@ def add_masked_loss_arguments(parser: argparse.ArgumentParser):
         help="apply mask for calculating loss. conditioning_data_dir is required for dataset. / 損失計算時にマスクを適用する。datasetにはconditioning_data_dirが必要",
     )
 
+def filter_sensitive_args(args: argparse.Namespace):
+    sensitive_args = ["wandb_api_key", "huggingface_token"]
+    sensitive_path_args = [
+        "pretrained_model_name_or_path",
+        "vae",
+        "tokenizer_cache_dir",
+        "train_data_dir",
+        "conditioning_data_dir",
+        "reg_data_dir",
+        "output_dir",
+        "logging_dir",
+    ]
+    filtered_args = {}
+    for k, v in vars(args).items():
+    # filter out sensitive values
+        if k not in sensitive_args + sensitive_path_args:
+            #Accelerate values need to have type `bool`,`str`, `float`, `int`, or `None`.
+            if v is None or isinstance(v, bool) or isinstance(v, str) or isinstance(v, float) or isinstance(v, int):
+                filtered_args[k] = v
+            # accelerate does not support lists
+            elif isinstance(v, list):
+                filtered_args[k] = f"{v}"
+            # accelerate does not support objects
+            elif isinstance(v, object):
+                filtered_args[k] = f"{v}"
+
+    return filtered_args
 
 # verify command line args for training
 def verify_command_line_training_args(args: argparse.Namespace):

sdxl_train.py

@@ -487,7 +487,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             init_kwargs["wandb"] = {"name": args.wandb_run_name}
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
-        accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
+        accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs)
 
     # For --sample_at_first
     sdxl_train_util.sample_images(

sdxl_train_control_net_lllite.py

@@ -353,7 +353,7 @@ def train(args):
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
         accelerator.init_trackers(
-            "lllite_control_net_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
+            "lllite_control_net_train" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs
         )
 
     loss_recorder = train_util.LossRecorder()

sdxl_train_control_net_lllite_old.py

@@ -324,7 +324,7 @@ def train(args):
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
         accelerator.init_trackers(
-            "lllite_control_net_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
+            "lllite_control_net_train" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs
         )
 
     loss_recorder = train_util.LossRecorder()

train_controlnet.py

@@ -344,7 +344,7 @@ def train(args):
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
         accelerator.init_trackers(
-            "controlnet_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
+            "controlnet_train" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs
         )
 
     loss_recorder = train_util.LossRecorder()

train_db.py

@@ -290,7 +290,7 @@ def train(args):
             init_kwargs["wandb"] = {"name": args.wandb_run_name}
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
-        accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
+        accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs)
 
     # For --sample_at_first
     train_util.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)

train_network.py

@@ -753,7 +753,7 @@ def load_model_hook(models, input_dir):
             if args.log_tracker_config is not None:
                 init_kwargs = toml.load(args.log_tracker_config)
             accelerator.init_trackers(
-                "network_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
+                "network_train" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs
             )
 
         loss_recorder = train_util.LossRecorder()

train_textual_inversion.py

@@ -510,7 +510,7 @@ def train(self, args):
             if args.log_tracker_config is not None:
                 init_kwargs = toml.load(args.log_tracker_config)
             accelerator.init_trackers(
-                "textual_inversion" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
+                "textual_inversion" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs
             )
 
         # function for saving/removing

train_textual_inversion_XTI.py

@@ -407,7 +407,7 @@ def train(args):
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
         accelerator.init_trackers(
-            "textual_inversion" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
+            "textual_inversion" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.filter_sensitive_args(args), init_kwargs=init_kwargs
         )
 
     # function for saving/removing