diff --git a/README.md b/README.md
index 09a10f9..969d2a5 100644
--- a/README.md
+++ b/README.md
@@ -69,6 +69,9 @@ def distributed_training(model: nn.Module, num_steps: int = 10) -> nn.Module | N
 We can distribute and run this function (e.g. on 2 machines x 2 GPUs) using **`torchrunx`**!
 
 ```python
+import logging
+logging.basicConfig(level=logging.INFO)
+
 import torchrunx
 
 launcher = torchrunx.Launcher(
diff --git a/docs/source/usage/logging.md b/docs/source/usage/logging.md
index 0e76493..2352db1 100644
--- a/docs/source/usage/logging.md
+++ b/docs/source/usage/logging.md
@@ -1,6 +1,6 @@
 # Custom Logging
 
-We forward all agent and worker logs (i.e. from {mod}`logging`, {obj}`sys.stdout`, and {obj}`sys.stderr`) to the launcher process.
+We forward all agent and worker logs (i.e. from {mod}`logging`, `stdout`, and `stderr`) to the launcher process.
 
 ## Defaults
 
diff --git a/src/torchrunx/utils/log_streaming.py b/src/torchrunx/utils/log_streaming.py
index af5ff52..69f8d58 100644
--- a/src/torchrunx/utils/log_streaming.py
+++ b/src/torchrunx/utils/log_streaming.py
@@ -10,17 +10,17 @@
 ]
 
 import logging
+import os
 import pickle
 import signal
 import struct
 import sys
-from contextlib import redirect_stderr, redirect_stdout
 from dataclasses import dataclass
-from io import StringIO
 from logging import Handler, Logger
 from logging.handlers import SocketHandler
 from multiprocessing.synchronize import Event as EventClass
 from socketserver import StreamRequestHandler, ThreadingTCPServer
+from threading import Thread
 from typing import Callable
 
 import cloudpickle
@@ -129,24 +129,35 @@ def start_logging_server(serialized_args: bytes, stop_event: EventClass) -> None
 
 def redirect_stdio_to_logger(logger: Logger) -> None:
     """Redirect stderr/stdout: send output to logger at every flush."""
-
-    class _LoggingStream(StringIO):
-        def __init__(self, logger: Logger, level: int = logging.NOTSET) -> None:
-            super().__init__()
-            self.logger = logger
-            self.level = level
-
-        def flush(self) -> None:
-            super().flush()  # At "flush" to avoid logs of partial bytes
-            value = self.getvalue()
-            if value != "":
-                self.logger.log(self.level, value)
-                self.truncate(0)
-                self.seek(0)
-
     logging.captureWarnings(capture=True)
-    redirect_stderr(_LoggingStream(logger, level=logging.ERROR)).__enter__()
-    redirect_stdout(_LoggingStream(logger, level=logging.INFO)).__enter__()
+
+    def redirect_fd_to_logger(read_fd: int, level: int) -> None:
+        for line in os.fdopen(read_fd):
+            logger.log(level, line.rstrip())
+
+    # create (r, w) pipe and start logging all outputs from r
+    read_out_fd, write_out_fd = os.pipe()
+    Thread(
+        target=redirect_fd_to_logger,
+        kwargs={"read_fd": read_out_fd, "level": logging.INFO},
+        daemon=True,
+    ).start()
+    # flush buffer before redirecting stdout
+    sys.stdout.flush()
+    # pipe: r <-> stdout instead of r <-> w
+    os.dup2(write_out_fd, sys.stdout.fileno())  # set stdout fd to pipe
+    os.close(write_out_fd)
+
+    # repeat for stderr
+    read_err_fd, write_err_fd = os.pipe()
+    Thread(
+        target=redirect_fd_to_logger,
+        kwargs={"read_fd": read_err_fd, "level": logging.ERROR},
+        daemon=True,
+    ).start()
+    sys.stderr.flush()
+    os.dup2(write_err_fd, sys.stderr.fileno())
+    os.close(write_err_fd)
 
 
 @dataclass