power

zeus.monitor.power

Monitor the power usage of GPUs.

PowerMonitor

Monitor power usage from GPUs.

This class acts as a lower level wrapper around a Python process that polls the power consumption of GPUs. This is primarily used by ZeusMonitor for older architecture GPUs that do not support the nvmlDeviceGetTotalEnergyConsumption API.

Warning

Since the monitor spawns a child process, it should not be instantiated as a global variable. Python puts a protection to prevent creating a process in global scope. Refer to the "Safe importing of main module" section in the Python documentation for more details.

Attributes:

Name	Type	Description
gpu_indices	list[int]	Indices of the GPUs to monitor.
update_period	int	Update period of the power monitor in seconds. Holds inferred update period if update_period was given as None.

Source code in zeus/monitor/power.py

class PowerMonitor:
    """Monitor power usage from GPUs.

    This class acts as a lower level wrapper around a Python process that polls
    the power consumption of GPUs. This is primarily used by
    [`ZeusMonitor`][zeus.monitor.ZeusMonitor] for older architecture GPUs that
    do not support the nvmlDeviceGetTotalEnergyConsumption API.

    !!! Warning
        Since the monitor spawns a child process, **it should not be instantiated as a global variable**.
        Python puts a protection to prevent creating a process in global scope.
        Refer to the "Safe importing of main module" section in the
        [Python documentation](https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods)
        for more details.

    Attributes:
        gpu_indices (list[int]): Indices of the GPUs to monitor.
        update_period (int): Update period of the power monitor in seconds.
            Holds inferred update period if `update_period` was given as `None`.
    """

    def __init__(
        self,
        gpu_indices: list[int] | None = None,
        update_period: float | None = None,
        power_csv_path: str | None = None,
    ) -> None:
        """Initialize the power monitor.

        Args:
            gpu_indices: Indices of the GPUs to monitor. If None, monitor all GPUs.
            update_period: Update period of the power monitor in seconds. If None,
                infer the update period by max speed polling the power counter for
                each GPU model.
            power_csv_path: If given, the power polling process will write measurements
                to this path. Otherwise, a temporary file will be used.
        """
        if gpu_indices is not None and not gpu_indices:
            raise ValueError("`gpu_indices` must be either `None` or non-empty")

        # Get GPUs
        gpus = get_gpus()

        # Set up logging.
        self.logger = get_logger(type(self).__name__)

        # Get GPUs
        self.gpu_indices = (
            gpu_indices if gpu_indices is not None else list(range(len(gpus)))
        )
        self.logger.info("Monitoring power usage of GPUs %s", self.gpu_indices)

        # Infer the update period if necessary.
        if update_period is None:
            update_period = infer_counter_update_period(self.gpu_indices)
        self.update_period = update_period

        # Create and open the CSV to record power measurements.
        if power_csv_path is None:
            power_csv_path = tempfile.mkstemp(suffix=".csv", text=True)[1]
        open(power_csv_path, "w").close()
        self.power_f = open(power_csv_path)
        self.power_df_columns = ["time"] + [f"power{i}" for i in self.gpu_indices]
        self.power_df = pd.DataFrame(columns=self.power_df_columns)

        # Spawn the power polling process.
        atexit.register(self._stop)
        self.process = mp.get_context("spawn").Process(
            target=_polling_process,
            args=(self.gpu_indices, power_csv_path, update_period),
        )
        self.process.start()

    def _stop(self) -> None:
        """Stop monitoring power usage."""
        if self.process is not None:
            self.process.terminate()
            self.process.join(timeout=1.0)
            self.process.kill()
            self.process = None

    def _update_df(self) -> None:
        """Add rows to the power dataframe from the CSV file."""
        try:
            additional_df = typing.cast(
                pd.DataFrame,
                pd.read_csv(self.power_f, header=None, names=self.power_df_columns),
            )
        except pd.errors.EmptyDataError:
            return

        if additional_df.empty:
            return

        if self.power_df.empty:
            self.power_df = additional_df
        else:
            self.power_df = pd.concat(
                [self.power_df, additional_df],
                axis=0,
                ignore_index=True,
                copy=False,
            )

    def get_energy(self, start_time: float, end_time: float) -> dict[int, float] | None:
        """Get the energy used by the GPUs between two times.

        Args:
            start_time: Start time of the interval, from time.time().
            end_time: End time of the interval, from time.time().

        Returns:
            A dictionary mapping GPU indices to the energy used by the GPU between the
            two times. GPU indices are from the DL framework's perspective after
            applying `CUDA_VISIBLE_DEVICES`.
            If there are no power readings, return None.
        """
        self._update_df()

        if self.power_df.empty:
            return None

        df = typing.cast(
            pd.DataFrame, self.power_df.query(f"{start_time} <= time <= {end_time}")
        )

        try:
            return {
                i: float(auc(df["time"], df[f"power{i}"])) for i in self.gpu_indices
            }
        except ValueError:
            return None

    def get_power(self, time: float | None = None) -> dict[int, float] | None:
        """Get the power usage of the GPUs at a specific time point.

        Args:
            time: Time point to get the power usage at. If None, get the power usage
                at the last recorded time point.

        Returns:
            A dictionary mapping GPU indices to the power usage of the GPU at the
            specified time point. GPU indices are from the DL framework's perspective
            after applying `CUDA_VISIBLE_DEVICES`.
            If there are no power readings (e.g., future timestamps), return None.
        """
        self._update_df()

        if self.power_df.empty:
            return None

        if time is None:
            row = self.power_df.iloc[-1]
        else:
            ind = self.power_df.time.searchsorted(time)
            try:
                row = self.power_df.iloc[ind]
            except IndexError:
                # This means that the time is after the last recorded power reading.
                row = self.power_df.iloc[-1]

        return {i: float(row[f"power{i}"]) for i in self.gpu_indices}

__init__

__init__(
    gpu_indices=None,
    update_period=None,
    power_csv_path=None,
)

Parameters:

Name	Type	Description	Default
gpu_indices	list[int] | None	Indices of the GPUs to monitor. If None, monitor all GPUs.	None
update_period	float | None	Update period of the power monitor in seconds. If None, infer the update period by max speed polling the power counter for each GPU model.	None
power_csv_path	str | None	If given, the power polling process will write measurements to this path. Otherwise, a temporary file will be used.	None

Source code in zeus/monitor/power.py

def __init__(
    self,
    gpu_indices: list[int] | None = None,
    update_period: float | None = None,
    power_csv_path: str | None = None,
) -> None:
    """Initialize the power monitor.

    Args:
        gpu_indices: Indices of the GPUs to monitor. If None, monitor all GPUs.
        update_period: Update period of the power monitor in seconds. If None,
            infer the update period by max speed polling the power counter for
            each GPU model.
        power_csv_path: If given, the power polling process will write measurements
            to this path. Otherwise, a temporary file will be used.
    """
    if gpu_indices is not None and not gpu_indices:
        raise ValueError("`gpu_indices` must be either `None` or non-empty")

    # Get GPUs
    gpus = get_gpus()

    # Set up logging.
    self.logger = get_logger(type(self).__name__)

    # Get GPUs
    self.gpu_indices = (
        gpu_indices if gpu_indices is not None else list(range(len(gpus)))
    )
    self.logger.info("Monitoring power usage of GPUs %s", self.gpu_indices)

    # Infer the update period if necessary.
    if update_period is None:
        update_period = infer_counter_update_period(self.gpu_indices)
    self.update_period = update_period

    # Create and open the CSV to record power measurements.
    if power_csv_path is None:
        power_csv_path = tempfile.mkstemp(suffix=".csv", text=True)[1]
    open(power_csv_path, "w").close()
    self.power_f = open(power_csv_path)
    self.power_df_columns = ["time"] + [f"power{i}" for i in self.gpu_indices]
    self.power_df = pd.DataFrame(columns=self.power_df_columns)

    # Spawn the power polling process.
    atexit.register(self._stop)
    self.process = mp.get_context("spawn").Process(
        target=_polling_process,
        args=(self.gpu_indices, power_csv_path, update_period),
    )
    self.process.start()

_stop

_stop()

Stop monitoring power usage.

Source code in zeus/monitor/power.py

def _stop(self) -> None:
    """Stop monitoring power usage."""
    if self.process is not None:
        self.process.terminate()
        self.process.join(timeout=1.0)
        self.process.kill()
        self.process = None

_update_df

_update_df()

Add rows to the power dataframe from the CSV file.

Source code in zeus/monitor/power.py

def _update_df(self) -> None:
    """Add rows to the power dataframe from the CSV file."""
    try:
        additional_df = typing.cast(
            pd.DataFrame,
            pd.read_csv(self.power_f, header=None, names=self.power_df_columns),
        )
    except pd.errors.EmptyDataError:
        return

    if additional_df.empty:
        return

    if self.power_df.empty:
        self.power_df = additional_df
    else:
        self.power_df = pd.concat(
            [self.power_df, additional_df],
            axis=0,
            ignore_index=True,
            copy=False,
        )

get_energy

get_energy(start_time, end_time)

Get the energy used by the GPUs between two times.

Parameters:

Name	Type	Description	Default
start_time	float	Start time of the interval, from time.time().	required
end_time	float	End time of the interval, from time.time().	required

Returns:

Type	Description
dict[int, float] | None	A dictionary mapping GPU indices to the energy used by the GPU between the
dict[int, float] | None	two times. GPU indices are from the DL framework's perspective after
dict[int, float] | None	applying CUDA_VISIBLE_DEVICES.
dict[int, float] | None	If there are no power readings, return None.

Source code in zeus/monitor/power.py

def get_energy(self, start_time: float, end_time: float) -> dict[int, float] | None:
    """Get the energy used by the GPUs between two times.

    Args:
        start_time: Start time of the interval, from time.time().
        end_time: End time of the interval, from time.time().

    Returns:
        A dictionary mapping GPU indices to the energy used by the GPU between the
        two times. GPU indices are from the DL framework's perspective after
        applying `CUDA_VISIBLE_DEVICES`.
        If there are no power readings, return None.
    """
    self._update_df()

    if self.power_df.empty:
        return None

    df = typing.cast(
        pd.DataFrame, self.power_df.query(f"{start_time} <= time <= {end_time}")
    )

    try:
        return {
            i: float(auc(df["time"], df[f"power{i}"])) for i in self.gpu_indices
        }
    except ValueError:
        return None

get_power

get_power(time=None)

Get the power usage of the GPUs at a specific time point.

Parameters:

Name	Type	Description	Default
time	float | None	Time point to get the power usage at. If None, get the power usage at the last recorded time point.	None

Returns:

Type	Description
dict[int, float] | None	A dictionary mapping GPU indices to the power usage of the GPU at the
dict[int, float] | None	specified time point. GPU indices are from the DL framework's perspective
dict[int, float] | None	after applying CUDA_VISIBLE_DEVICES.
dict[int, float] | None	If there are no power readings (e.g., future timestamps), return None.

Source code in zeus/monitor/power.py

def get_power(self, time: float | None = None) -> dict[int, float] | None:
    """Get the power usage of the GPUs at a specific time point.

    Args:
        time: Time point to get the power usage at. If None, get the power usage
            at the last recorded time point.

    Returns:
        A dictionary mapping GPU indices to the power usage of the GPU at the
        specified time point. GPU indices are from the DL framework's perspective
        after applying `CUDA_VISIBLE_DEVICES`.
        If there are no power readings (e.g., future timestamps), return None.
    """
    self._update_df()

    if self.power_df.empty:
        return None

    if time is None:
        row = self.power_df.iloc[-1]
    else:
        ind = self.power_df.time.searchsorted(time)
        try:
            row = self.power_df.iloc[ind]
        except IndexError:
            # This means that the time is after the last recorded power reading.
            row = self.power_df.iloc[-1]

    return {i: float(row[f"power{i}"]) for i in self.gpu_indices}

infer_counter_update_period

infer_counter_update_period(gpu_indicies)

Infer the update period of the NVML power counter.

NVML counters can update as slow as 10 Hz depending on the GPU model, so there's no need to poll them too faster than that. This function infers the update period for each unique GPU model and selects the fastest-updating period detected. Then, it returns half the period to ensure that the counter is polled at least twice per update period.

Source code in zeus/monitor/power.py

def infer_counter_update_period(gpu_indicies: list[int]) -> float:
    """Infer the update period of the NVML power counter.

    NVML counters can update as slow as 10 Hz depending on the GPU model, so
    there's no need to poll them too faster than that. This function infers the
    update period for each unique GPU model and selects the fastest-updating
    period detected. Then, it returns half the period to ensure that the
    counter is polled at least twice per update period.
    """
    logger = get_logger(__name__)

    # get gpus
    gpus = get_gpus()

    # For each unique GPU model, infer the update period.
    update_period = 0.0
    gpu_models_covered = set()
    for index in gpu_indicies:
        if (model := gpus.getName(index)) not in gpu_models_covered:
            logger.info(
                "Detected %s, inferring NVML power counter update period.", model
            )
            gpu_models_covered.add(model)
            detected_period = _infer_counter_update_period_single(index)
            logger.info(
                "Counter update period for %s is %.2f s",
                model,
                detected_period,
            )
            if update_period > detected_period:
                update_period = detected_period

    # Target half the update period to ensure that the counter is enough.
    update_period /= 2.0

    # Anything less than ten times a second is probably too slow.
    if update_period > 0.1:
        logger.warning(
            "Inferred update period (%.2f s) is too long. Using 0.1 s instead.",
            update_period,
        )
        update_period = 0.1
    return update_period

_infer_counter_update_period_single

_infer_counter_update_period_single(gpu_index)

Infer the update period of the NVML power counter for a single GPU.

Source code in zeus/monitor/power.py

def _infer_counter_update_period_single(gpu_index: int) -> float:
    """Infer the update period of the NVML power counter for a single GPU."""
    # get gpus
    gpus = get_gpus()
    # Collect 1000 samples of the power counter with timestamps.
    time_power_samples: list[tuple[float, int]] = [(0.0, 0) for _ in range(1000)]
    for i in range(len(time_power_samples)):
        time_power_samples[i] = (
            time(),
            gpus.getInstantPowerUsage(gpu_index),
        )

    # Find the timestamps when the power readings changed.
    time_power_samples = time_power_samples[10:]
    changed_times = []
    prev_power = time_power_samples[0][1]
    for t, p in time_power_samples:
        if p != prev_power:
            changed_times.append(t)
            prev_power = p

    # Compute the minimum time difference between power change timestamps.
    intervals = [
        time2 - time1 for time1, time2 in zip(changed_times, changed_times[1:])
    ]
    if len(intervals) == 0:
        return 0.1
    return min(intervals)

_polling_process

_polling_process(
    gpu_indices, power_csv_path, update_period
)

Run the power monitor.

Source code in zeus/monitor/power.py

def _polling_process(
    gpu_indices: list[int],
    power_csv_path: str,
    update_period: float,
) -> None:
    """Run the power monitor."""
    try:
        # Get GPUs
        gpus = get_gpus()

        # Use line buffering.
        with open(power_csv_path, "w", buffering=1) as power_f:
            while True:
                power: list[float] = []
                now = time()
                for index in gpu_indices:
                    power.append(gpus.getInstantPowerUsage(index))
                power_str = ",".join(map(lambda p: str(p / 1000), power))
                power_f.write(f"{now},{power_str}\n")
                if (sleep_time := update_period - (time() - now)) > 0:
                    sleep(sleep_time)
    except KeyboardInterrupt:
        return