update examples

Author: ZhiyuanChen

examples/by_feature/fsdp_with_peak_mem_tracking.py

@@ -110,10 +110,9 @@ def training_function(config, args):
     batch_size = int(config["batch_size"])
 
     # We need to initialize the trackers we use, and also store our configuration
-    if args.with_tracking:
-        if accelerator.is_main_process:
-            experiment_config = vars(args)
-            accelerator.init_trackers("fsdp_glue_no_trainer", experiment_config)
+    if args.with_tracking and accelerator.is_main_process:
+        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")

examples/by_feature/tracking.py

@@ -162,10 +162,9 @@ def training_function(config, args):
 
     # New Code #
     # We need to initalize the trackers we use. Overall configurations can also be stored
-    if args.with_tracking:
-        if accelerator.is_main_process:
-            run = os.path.split(__file__)[-1].split(".")[0]
-            accelerator.init_trackers(run, config)
+    if args.with_tracking and accelerator.is_main_process:
+        run = os.path.split(__file__)[-1].split(".")[0]
+        accelerator.init_trackers(run, config)
 
     # Now we train the model
     for epoch in range(num_epochs):

examples/complete_cv_example.py

@@ -103,12 +103,11 @@ def training_function(config, args):
         checkpointing_steps = None
 
     # 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.init_trackers(run, config)
+    if args.with_tracking and 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.init_trackers(run, config)
 
     # Grab all the image filenames
     file_names = [os.path.join(args.data_dir, fname) for fname in os.listdir(args.data_dir) if fname.endswith(".jpg")]

examples/complete_nlp_example.py

@@ -75,12 +75,11 @@ def training_function(config, args):
     batch_size = int(config["batch_size"])
 
     # 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.init_trackers(run, config)
+    if args.with_tracking and 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.init_trackers(run, config)
 
     tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
     datasets = load_dataset("glue", "mrpc")