cleanup logging

docs/source/multi_gpu_training.mdx

@@ -10,7 +10,7 @@ First, ensure you have accelerate installed:
 pip install accelerate
 ```
 
-## Training with Multiple GPUss
+## Training with Multiple GPUs
 
 You can launch training in two ways:
 

src/lerobot/utils/utils.py

@@ -21,6 +21,7 @@ import subprocess
 import sys
 import time
 from collections.abc import Callable
+from accelerate import Accelerator
 from copy import copy, deepcopy
 from datetime import datetime
 from pathlib import Path
@@ -113,52 +114,52 @@ def init_logging(
     display_pid: bool = False,
     console_level: str = "INFO",
     file_level: str = "DEBUG",
-    accelerator: Callable | None = None,
+    accelerator: Accelerator | None = None,
 ):
+    """Initialize logging configuration for LeRobot.
+    
+    In multi-GPU training, only the main process logs to console to avoid duplicate output.
+    Non-main processes have console logging suppressed but can still log to file.
+    
+    Args:
+        log_file: Optional file path to write logs to
+        display_pid: Include process ID in log messages (useful for debugging multi-process)
+        console_level: Logging level for console output
+        file_level: Logging level for file output
+        accelerator: Optional Accelerator instance (for multi-GPU detection)
+    """
     def custom_format(record: logging.LogRecord) -> str:
         dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         fnameline = f"{record.pathname}:{record.lineno}"
-
-        # NOTE: Display PID is useful for multi-process logging.
-        if display_pid:
-            pid_str = f"[PID: {os.getpid()}]"
-            message = f"{record.levelname} {pid_str} {dt} {fnameline[-15:]:>15} {record.getMessage()}"
-        else:
-            message = f"{record.levelname} {dt} {fnameline[-15:]:>15} {record.getMessage()}"
-        return message
+        pid_str = f"[PID: {os.getpid()}] " if display_pid else ""
+        return f"{record.levelname} {pid_str}{dt} {fnameline[-15:]:>15} {record.getMessage()}"
 
     formatter = logging.Formatter()
     formatter.format = custom_format
 
     logger = logging.getLogger()
-    logger.setLevel(logging.NOTSET)  # Set the logger to the lowest level to capture all messages
-
-    # Remove unused default handlers
-    for handler in logger.handlers[:]:
-        logger.removeHandler(handler)
-
-    # Check if this is a non-main process in multi-GPU training
-    # Check environment variables set by accelerate (more reliable than checking accelerator object)
-    local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    is_non_main_process = local_rank > 0
+    logger.setLevel(logging.NOTSET)
     
-    # Fallback to accelerator object check if LOCAL_RANK not set
-    if local_rank == -1 and accelerator is not None:
-        is_non_main_process = hasattr(accelerator, 'process_index') and accelerator.process_index != 0
+    # Clear any existing handlers
+    logger.handlers.clear()
 
-    # Write logs to console (only for main process in multi-GPU training)
-    if not is_non_main_process:
+    # Determine if this is a non-main process in distributed training
+    # Check LOCAL_RANK env var (set by accelerate/torchrun)
+    local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    is_main_process = local_rank <= 0  # -1 means not distributed, 0 means main process
+
+    # Console logging (main process only)
+    if is_main_process:
         console_handler = logging.StreamHandler()
         console_handler.setFormatter(formatter)
         console_handler.setLevel(console_level.upper())
         logger.addHandler(console_handler)
     else:
-        # For non-main processes, add a NullHandler to completely suppress output
-        # and set level to ERROR to minimize any logging
+        # Suppress console output for non-main processes
         logger.addHandler(logging.NullHandler())
         logger.setLevel(logging.ERROR)
 
-    # Additionally write logs to file
+    # File logging (optional, all processes)
     if log_file is not None:
         file_handler = logging.FileHandler(log_file)
         file_handler.setFormatter(formatter)