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Reference for ultralytics/utils/logger.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/logger.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.utils.logger.ConsoleLogger 

ConsoleLogger(destination)

Console output capture with API/file streaming and deduplication.

Captures stdout/stderr output and streams it to either an API endpoint or local file, with intelligent deduplication to reduce noise from repetitive console output.

Attributes:

Name Type Description
destination str | Path

Target destination for streaming (URL or Path object).
is_api bool

Whether destination is an API endpoint (True) or local file (False).
original_stdout

Reference to original sys.stdout for restoration.
original_stderr

Reference to original sys.stderr for restoration.
log_queue Queue

Thread-safe queue for buffering log messages.
active bool

Whether console capture is currently active.
worker_thread Thread

Background thread for processing log queue.
last_line str

Last processed line for deduplication.
last_time float

Timestamp of last processed line.
last_progress_line str

Last progress bar line for progress deduplication.
last_was_progress bool

Whether the last line was a progress bar.

Examples:

Basic file logging:

>>> logger = ConsoleLogger("training.log")
>>> logger.start_capture()
>>> print("This will be logged")
>>> logger.stop_capture()

API streaming:

>>> logger = ConsoleLogger("https://api.example.com/logs")
>>> logger.start_capture()
>>> # All output streams to API
>>> logger.stop_capture()

Parameters:

Name Type Description Default
destination str | Path

API endpoint URL (http/https) or local file path for streaming output.

 required
Source code in ultralytics/utils/logger.py 
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def __init__(self, destination):
    """
    Initialize with API endpoint or local file path.

    Args:
        destination (str | Path): API endpoint URL (http/https) or local file path for streaming output.
    """
    self.destination = destination
    self.is_api = isinstance(destination, str) and destination.startswith(("http://", "https://"))
    if not self.is_api:
        self.destination = Path(destination)

    # Console capture
    self.original_stdout = sys.stdout
    self.original_stderr = sys.stderr
    self.log_queue = queue.Queue(maxsize=1000)
    self.active = False
    self.worker_thread = None

    # State tracking
    self.last_line = ""
    self.last_time = 0.0
    self.last_progress_line = ""  # Track last progress line for deduplication
    self.last_was_progress = False  # Track if last line was a progress bar

start_capture 

start_capture()

Start capturing console output and redirect stdout/stderr to custom capture objects.

Source code in ultralytics/utils/logger.py 
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def start_capture(self):
    """Start capturing console output and redirect stdout/stderr to custom capture objects."""
    if self.active:
        return

    self.active = True
    sys.stdout = self._ConsoleCapture(self.original_stdout, self._queue_log)
    sys.stderr = self._ConsoleCapture(self.original_stderr, self._queue_log)

    # Hook Ultralytics logger
    try:
        handler = self._LogHandler(self._queue_log)
        logging.getLogger("ultralytics").addHandler(handler)
    except Exception:
        pass

    self.worker_thread = threading.Thread(target=self._stream_worker, daemon=True)
    self.worker_thread.start()

stop_capture 

stop_capture()

Stop capturing console output and restore original stdout/stderr.

Source code in ultralytics/utils/logger.py 
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def stop_capture(self):
    """Stop capturing console output and restore original stdout/stderr."""
    if not self.active:
        return

    self.active = False
    sys.stdout = self.original_stdout
    sys.stderr = self.original_stderr
    self.log_queue.put(None)




ultralytics.utils.logger.SystemLogger 

SystemLogger()

Log dynamic system metrics for training monitoring.

Captures real-time system metrics including CPU, RAM, disk I/O, network I/O, and NVIDIA GPU statistics for training performance monitoring and analysis.

Attributes:

Name Type Description
pynvml

NVIDIA pynvml module instance if successfully imported, None otherwise.
nvidia_initialized bool

Whether NVIDIA GPU monitoring is available and initialized.
net_start

Initial network I/O counters for calculating cumulative usage.
disk_start

Initial disk I/O counters for calculating cumulative usage.

Examples:

Basic usage:

>>> logger = SystemLogger()
>>> metrics = logger.get_metrics()
>>> print(f"CPU: {metrics['cpu']}%, RAM: {metrics['ram']}%")
>>> if metrics["gpus"]:
...     gpu0 = metrics["gpus"]["0"]
...     print(f"GPU0: {gpu0['usage']}% usage, {gpu0['temp']}°C")

Training loop integration:

>>> system_logger = SystemLogger()
>>> for epoch in range(epochs):
...     # Training code here
...     metrics = system_logger.get_metrics()
...     # Log to database/file
Source code in ultralytics/utils/logger.py 
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def __init__(self):
    """Initialize the system logger."""
    import psutil  # scoped as slow import

    self.pynvml = None
    self.nvidia_initialized = self._init_nvidia()
    self.net_start = psutil.net_io_counters()
    self.disk_start = psutil.disk_io_counters()

get_metrics 

get_metrics()

Get current system metrics.

Collects comprehensive system metrics including CPU usage, RAM usage, disk I/O statistics, network I/O statistics, and GPU metrics (if available). Example output:

metrics = {
    "cpu": 45.2,
    "ram": 78.9,
    "disk": {"read_mb": 156.7, "write_mb": 89.3, "used_gb": 256.8},
    "network": {"recv_mb": 157.2, "sent_mb": 89.1},
    "gpus": {
        0: {"usage": 95.6, "memory": 85.4, "temp": 72, "power": 285},
        1: {"usage": 94.1, "memory": 82.7, "temp": 70, "power": 278},
    },
}
  • cpu (float): CPU usage percentage (0-100%)
  • ram (float): RAM usage percentage (0-100%)
  • disk (dict):
    • read_mb (float): Cumulative disk read in MB since initialization
    • write_mb (float): Cumulative disk write in MB since initialization
    • used_gb (float): Total disk space used in GB
  • network (dict):
    • recv_mb (float): Cumulative network received in MB since initialization
    • sent_mb (float): Cumulative network sent in MB since initialization
  • gpus (dict): GPU metrics by device index (e.g., 0, 1) containing:
    • usage (int): GPU utilization percentage (0-100%)
    • memory (float): CUDA memory usage percentage (0-100%)
    • temp (int): GPU temperature in degrees Celsius
    • power (int): GPU power consumption in watts

Returns:

Name Type Description
metrics dict

System metrics containing 'cpu', 'ram', 'disk', 'network', 'gpus' with respective usage data.
Source code in ultralytics/utils/logger.py 
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def get_metrics(self):
    """
    Get current system metrics.

    Collects comprehensive system metrics including CPU usage, RAM usage, disk I/O statistics,
    network I/O statistics, and GPU metrics (if available). Example output:

    ```python
    metrics = {
        "cpu": 45.2,
        "ram": 78.9,
        "disk": {"read_mb": 156.7, "write_mb": 89.3, "used_gb": 256.8},
        "network": {"recv_mb": 157.2, "sent_mb": 89.1},
        "gpus": {
            0: {"usage": 95.6, "memory": 85.4, "temp": 72, "power": 285},
            1: {"usage": 94.1, "memory": 82.7, "temp": 70, "power": 278},
        },
    }
    ```

    - cpu (float): CPU usage percentage (0-100%)
    - ram (float): RAM usage percentage (0-100%)
    - disk (dict):
        - read_mb (float): Cumulative disk read in MB since initialization
        - write_mb (float): Cumulative disk write in MB since initialization
        - used_gb (float): Total disk space used in GB
    - network (dict):
        - recv_mb (float): Cumulative network received in MB since initialization
        - sent_mb (float): Cumulative network sent in MB since initialization
    - gpus (dict): GPU metrics by device index (e.g., 0, 1) containing:
        - usage (int): GPU utilization percentage (0-100%)
        - memory (float): CUDA memory usage percentage (0-100%)
        - temp (int): GPU temperature in degrees Celsius
        - power (int): GPU power consumption in watts

    Returns:
        metrics (dict): System metrics containing 'cpu', 'ram', 'disk', 'network', 'gpus' with respective usage data.
    """
    import psutil  # scoped as slow import

    net = psutil.net_io_counters()
    disk = psutil.disk_io_counters()
    memory = psutil.virtual_memory()
    disk_usage = shutil.disk_usage("/")

    metrics = {
        "cpu": round(psutil.cpu_percent(), 3),
        "ram": round(memory.percent, 3),
        "disk": {
            "read_mb": round((disk.read_bytes - self.disk_start.read_bytes) / (1 << 20), 3),
            "write_mb": round((disk.write_bytes - self.disk_start.write_bytes) / (1 << 20), 3),
            "used_gb": round(disk_usage.used / (1 << 30), 3),
        },
        "network": {
            "recv_mb": round((net.bytes_recv - self.net_start.bytes_recv) / (1 << 20), 3),
            "sent_mb": round((net.bytes_sent - self.net_start.bytes_sent) / (1 << 20), 3),
        },
        "gpus": {},
    }

    # Add GPU metrics (NVIDIA only)
    if self.nvidia_initialized:
        metrics["gpus"].update(self._get_nvidia_metrics())

    return metrics





📅 Created 1 month ago ✏️ Updated 1 month ago
glenn-jocher