frigate/frigate/log.py

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# In log.py
import atexit
import io
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import logging
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import os
import sys
import threading
from collections import deque
from contextlib import contextmanager
from enum import Enum
from functools import wraps
from logging.handlers import QueueHandler, QueueListener
Use Fork-Server As Spawn Method (#18682) * Set runtime * Use count correctly * Don't assume camera sizes * Use separate zmq proxy for object detection * Correct order * Use forkserver * Only store PID instead of entire process reference * Cleanup * Catch correct errors * Fix typing * Remove before_run from process util The before_run never actually ran because: You're right to suspect an issue with before_run not being called and a potential deadlock. The way you've implemented the run_wrapper using __getattribute__ for the run method of BaseProcess is a common pitfall in Python's multiprocessing, especially when combined with how multiprocessing.Process works internally. Here's a breakdown of why before_run isn't being called and why you might be experiencing a deadlock: The Problem: __getattribute__ and Process Serialization When you create a multiprocessing.Process object and call start(), the multiprocessing module needs to serialize the process object (or at least enough of it to re-create the process in the new interpreter). It then pickles this serialized object and sends it to the newly spawned process. The issue with your __getattribute__ implementation for run is that: run is retrieved during serialization: When multiprocessing tries to pickle your Process object to send to the new process, it will likely access the run attribute. This triggers your __getattribute__ wrapper, which then tries to bind run_wrapper to self. run_wrapper is bound to the parent process's self: The run_wrapper closure, when created in the parent process, captures the self (the Process instance) from the parent's memory space. Deserialization creates a new object: In the child process, a new Process object is created by deserializing the pickled data. However, the run_wrapper method that was pickled still holds a reference to the self from the parent process. This is a subtle but critical distinction. The child's run is not your wrapped run: When the child process starts, it internally calls its own run method. Because of the serialization and deserialization process, the run method that's ultimately executed in the child process is the original multiprocessing.Process.run or the Process.run if you had directly overridden it. Your __getattribute__ magic, which wraps run, isn't correctly applied to the Process object within the child's context. * Cleanup * Logging bugfix (#18465) * use mp Manager to handle logging queues A Python bug (https://github.com/python/cpython/issues/91555) was preventing logs from the embeddings maintainer process from printing. The bug is fixed in Python 3.14, but a viable workaround is to use the multiprocessing Manager, which better manages mp queues and causes the logging to work correctly. * consolidate * fix typing * Fix typing * Use global log queue * Move to using process for logging * Convert camera tracking to process * Add more processes * Finalize process * Cleanup * Cleanup typing * Formatting * Remove daemon --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
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from multiprocessing.managers import SyncManager
from queue import Empty, Queue
from typing import Any, Callable, Deque, Generator, Optional
from frigate.util.builtin import clean_camera_user_pass
LOG_HANDLER = logging.StreamHandler()
LOG_HANDLER.setFormatter(
logging.Formatter(
"[%(asctime)s] %(name)-30s %(levelname)-8s: %(message)s",
"%Y-%m-%d %H:%M:%S",
)
)
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# filter out norfair warning
LOG_HANDLER.addFilter(
lambda record: not record.getMessage().startswith(
"You are using a scalar distance function"
)
)
# filter out tflite logging
LOG_HANDLER.addFilter(
lambda record: "Created TensorFlow Lite XNNPACK delegate for CPU."
not in record.getMessage()
)
class LogLevel(str, Enum):
debug = "debug"
info = "info"
warning = "warning"
error = "error"
critical = "critical"
log_listener: Optional[QueueListener] = None
log_queue: Optional[Queue] = None
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Use Fork-Server As Spawn Method (#18682) * Set runtime * Use count correctly * Don't assume camera sizes * Use separate zmq proxy for object detection * Correct order * Use forkserver * Only store PID instead of entire process reference * Cleanup * Catch correct errors * Fix typing * Remove before_run from process util The before_run never actually ran because: You're right to suspect an issue with before_run not being called and a potential deadlock. The way you've implemented the run_wrapper using __getattribute__ for the run method of BaseProcess is a common pitfall in Python's multiprocessing, especially when combined with how multiprocessing.Process works internally. Here's a breakdown of why before_run isn't being called and why you might be experiencing a deadlock: The Problem: __getattribute__ and Process Serialization When you create a multiprocessing.Process object and call start(), the multiprocessing module needs to serialize the process object (or at least enough of it to re-create the process in the new interpreter). It then pickles this serialized object and sends it to the newly spawned process. The issue with your __getattribute__ implementation for run is that: run is retrieved during serialization: When multiprocessing tries to pickle your Process object to send to the new process, it will likely access the run attribute. This triggers your __getattribute__ wrapper, which then tries to bind run_wrapper to self. run_wrapper is bound to the parent process's self: The run_wrapper closure, when created in the parent process, captures the self (the Process instance) from the parent's memory space. Deserialization creates a new object: In the child process, a new Process object is created by deserializing the pickled data. However, the run_wrapper method that was pickled still holds a reference to the self from the parent process. This is a subtle but critical distinction. The child's run is not your wrapped run: When the child process starts, it internally calls its own run method. Because of the serialization and deserialization process, the run method that's ultimately executed in the child process is the original multiprocessing.Process.run or the Process.run if you had directly overridden it. Your __getattribute__ magic, which wraps run, isn't correctly applied to the Process object within the child's context. * Cleanup * Logging bugfix (#18465) * use mp Manager to handle logging queues A Python bug (https://github.com/python/cpython/issues/91555) was preventing logs from the embeddings maintainer process from printing. The bug is fixed in Python 3.14, but a viable workaround is to use the multiprocessing Manager, which better manages mp queues and causes the logging to work correctly. * consolidate * fix typing * Fix typing * Use global log queue * Move to using process for logging * Convert camera tracking to process * Add more processes * Finalize process * Cleanup * Cleanup typing * Formatting * Remove daemon --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
2025-06-12 21:12:34 +03:00
def setup_logging(manager: SyncManager) -> None:
global log_listener, log_queue
log_queue = manager.Queue()
log_listener = QueueListener(log_queue, LOG_HANDLER, respect_handler_level=True)
atexit.register(_stop_logging)
log_listener.start()
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logging.basicConfig(
level=logging.INFO,
handlers=[],
force=True,
)
logging.getLogger().addHandler(QueueHandler(log_listener.queue))
def _stop_logging() -> None:
Use Fork-Server As Spawn Method (#18682) * Set runtime * Use count correctly * Don't assume camera sizes * Use separate zmq proxy for object detection * Correct order * Use forkserver * Only store PID instead of entire process reference * Cleanup * Catch correct errors * Fix typing * Remove before_run from process util The before_run never actually ran because: You're right to suspect an issue with before_run not being called and a potential deadlock. The way you've implemented the run_wrapper using __getattribute__ for the run method of BaseProcess is a common pitfall in Python's multiprocessing, especially when combined with how multiprocessing.Process works internally. Here's a breakdown of why before_run isn't being called and why you might be experiencing a deadlock: The Problem: __getattribute__ and Process Serialization When you create a multiprocessing.Process object and call start(), the multiprocessing module needs to serialize the process object (or at least enough of it to re-create the process in the new interpreter). It then pickles this serialized object and sends it to the newly spawned process. The issue with your __getattribute__ implementation for run is that: run is retrieved during serialization: When multiprocessing tries to pickle your Process object to send to the new process, it will likely access the run attribute. This triggers your __getattribute__ wrapper, which then tries to bind run_wrapper to self. run_wrapper is bound to the parent process's self: The run_wrapper closure, when created in the parent process, captures the self (the Process instance) from the parent's memory space. Deserialization creates a new object: In the child process, a new Process object is created by deserializing the pickled data. However, the run_wrapper method that was pickled still holds a reference to the self from the parent process. This is a subtle but critical distinction. The child's run is not your wrapped run: When the child process starts, it internally calls its own run method. Because of the serialization and deserialization process, the run method that's ultimately executed in the child process is the original multiprocessing.Process.run or the Process.run if you had directly overridden it. Your __getattribute__ magic, which wraps run, isn't correctly applied to the Process object within the child's context. * Cleanup * Logging bugfix (#18465) * use mp Manager to handle logging queues A Python bug (https://github.com/python/cpython/issues/91555) was preventing logs from the embeddings maintainer process from printing. The bug is fixed in Python 3.14, but a viable workaround is to use the multiprocessing Manager, which better manages mp queues and causes the logging to work correctly. * consolidate * fix typing * Fix typing * Use global log queue * Move to using process for logging * Convert camera tracking to process * Add more processes * Finalize process * Cleanup * Cleanup typing * Formatting * Remove daemon --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
2025-06-12 21:12:34 +03:00
global log_listener
if log_listener is not None:
log_listener.stop()
log_listener = None
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def apply_log_levels(default: str, log_levels: dict[str, LogLevel]) -> None:
logging.getLogger().setLevel(default)
log_levels = {
"absl": LogLevel.error,
"httpx": LogLevel.error,
"matplotlib": LogLevel.error,
"tensorflow": LogLevel.error,
"werkzeug": LogLevel.error,
"ws4py": LogLevel.error,
**log_levels,
}
for log, level in log_levels.items():
logging.getLogger(log).setLevel(level.value.upper())
# When a multiprocessing.Process exits, python tries to flush stdout and stderr. However, if the
# process is created after a thread (for example a logging thread) is created and the process fork
# happens while an internal lock is held, the stdout/err flush can cause a deadlock.
#
# https://github.com/python/cpython/issues/91776
def reopen_std_streams() -> None:
sys.stdout = os.fdopen(1, "w")
sys.stderr = os.fdopen(2, "w")
os.register_at_fork(after_in_child=reopen_std_streams)
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# based on https://codereview.stackexchange.com/a/17959
class LogPipe(threading.Thread):
def __init__(self, log_name: str, level: int = logging.ERROR):
"""Setup the object with a logger and start the thread"""
super().__init__(daemon=False)
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self.logger = logging.getLogger(log_name)
self.level = level
self.deque: Deque[str] = deque(maxlen=100)
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self.fdRead, self.fdWrite = os.pipe()
self.pipeReader = os.fdopen(self.fdRead)
self.start()
def cleanup_log(self, log: str) -> str:
"""Cleanup the log line to remove sensitive info and string tokens."""
log = clean_camera_user_pass(log).strip("\n")
return log
def fileno(self) -> int:
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"""Return the write file descriptor of the pipe"""
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return self.fdWrite
def run(self) -> None:
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"""Run the thread, logging everything."""
for line in iter(self.pipeReader.readline, ""):
self.deque.append(self.cleanup_log(line))
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self.pipeReader.close()
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def dump(self) -> None:
while len(self.deque) > 0:
self.logger.log(self.level, self.deque.popleft())
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def close(self) -> None:
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"""Close the write end of the pipe."""
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os.close(self.fdWrite)
class LogRedirect(io.StringIO):
"""
A custom file-like object to capture stdout and process it.
It extends io.StringIO to capture output and then processes it
line by line.
"""
def __init__(self, logger_instance: logging.Logger, level: int):
super().__init__()
self.logger = logger_instance
self.log_level = level
self._line_buffer: list[str] = []
def write(self, s: Any) -> int:
if not isinstance(s, str):
s = str(s)
self._line_buffer.append(s)
# Process output line by line if a newline is present
if "\n" in s:
full_output = "".join(self._line_buffer)
lines = full_output.splitlines(keepends=True)
self._line_buffer = []
for line in lines:
if line.endswith("\n"):
self._process_line(line.rstrip("\n"))
else:
self._line_buffer.append(line)
return len(s)
def _process_line(self, line: str) -> None:
self.logger.log(self.log_level, line)
def flush(self) -> None:
if self._line_buffer:
full_output = "".join(self._line_buffer)
self._line_buffer = []
if full_output: # Only process if there's content
self._process_line(full_output)
def __enter__(self) -> "LogRedirect":
"""Context manager entry point."""
return self
def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
"""Context manager exit point. Ensures buffered content is flushed."""
self.flush()
@contextmanager
def __redirect_fd_to_queue(queue: Queue[str]) -> Generator[None, None, None]:
"""Redirect file descriptor 1 (stdout) to a pipe and capture output in a queue."""
stdout_fd = os.dup(1)
read_fd, write_fd = os.pipe()
os.dup2(write_fd, 1)
os.close(write_fd)
stop_event = threading.Event()
def reader() -> None:
"""Read from pipe and put lines in queue until stop_event is set."""
try:
with os.fdopen(read_fd, "r") as pipe:
while not stop_event.is_set():
line = pipe.readline()
if not line: # EOF
break
queue.put(line.strip())
except OSError as e:
queue.put(f"Reader error: {e}")
finally:
if not stop_event.is_set():
stop_event.set()
reader_thread = threading.Thread(target=reader, daemon=False)
reader_thread.start()
try:
yield
finally:
os.dup2(stdout_fd, 1)
os.close(stdout_fd)
stop_event.set()
reader_thread.join(timeout=1.0)
try:
os.close(read_fd)
except OSError:
pass
def redirect_output_to_logger(logger: logging.Logger, level: int) -> Any:
"""Decorator to redirect both Python sys.stdout/stderr and C-level stdout to logger."""
def decorator(func: Callable) -> Callable:
@wraps(func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
queue: Queue[str] = Queue()
log_redirect = LogRedirect(logger, level)
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = log_redirect
sys.stderr = log_redirect
try:
# Redirect C-level stdout
with __redirect_fd_to_queue(queue):
result = func(*args, **kwargs)
finally:
# Restore Python stdout/stderr
sys.stdout = old_stdout
sys.stderr = old_stderr
log_redirect.flush()
# Log C-level output from queue
while True:
try:
logger.log(level, queue.get_nowait())
except Empty:
break
return result
return wrapper
return decorator
def suppress_os_output(func: Callable) -> Callable:
"""
A decorator that suppresses all output (stdout and stderr)
at the operating system file descriptor level for the decorated function.
This is useful for silencing noisy C/C++ libraries.
Note: This is a Unix-specific solution using os.dup2 and os.pipe.
It temporarily redirects file descriptors 1 (stdout) and 2 (stderr)
to a non-read pipe, effectively discarding their output.
"""
@wraps(func)
def wrapper(*args: tuple, **kwargs: dict[str, Any]) -> Any:
# Save the original file descriptors for stdout (1) and stderr (2)
original_stdout_fd = os.dup(1)
original_stderr_fd = os.dup(2)
# Create dummy pipes. We only need the write ends to redirect to.
# The data written to these pipes will be discarded as nothing
# will read from the read ends.
devnull_read_fd, devnull_write_fd = os.pipe()
try:
# Redirect stdout (FD 1) and stderr (FD 2) to the write end of our dummy pipe
os.dup2(devnull_write_fd, 1) # Redirect stdout to devnull pipe
os.dup2(devnull_write_fd, 2) # Redirect stderr to devnull pipe
# Execute the original function
result = func(*args, **kwargs)
finally:
# Restore original stdout and stderr file descriptors (1 and 2)
# This is crucial to ensure normal printing resumes after the decorated function.
os.dup2(original_stdout_fd, 1)
os.dup2(original_stderr_fd, 2)
# Close all duplicated and pipe file descriptors to prevent resource leaks.
# It's important to close the read end of the dummy pipe too,
# as nothing is explicitly reading from it.
os.close(original_stdout_fd)
os.close(original_stderr_fd)
os.close(devnull_read_fd)
os.close(devnull_write_fd)
return result
return wrapper