lisa.analysis.load_tracking.LoadTrackingAnalysis#

class lisa.analysis.load_tracking.LoadTrackingAnalysis(trace, proxy=None)[source]#

Bases: TraceAnalysisBase

Support for scheduler load tracking analysis

Parameters:

trace (lisa.trace.Trace) – input Trace object

Attributes

name

Name of the analysis class.

Properties

logger inherited

Convenience short-hand for self.get_logger().

Methods

df_cpus_signal()

Get the load-tracking signals for the CPUs.

df_task_signal()

Same as df_tasks_signal() but for one task only.

df_tasks_signal()

Get the load-tracking signals for the tasks.

df_top_big_tasks()

Tasks which had ‘utilization’ samples bigger than the specified threshold.

plot_cpus_signals()

Plot the CPU-related load-tracking signals.

plot_task_placement()

Plot the CPU placement of the task.

plot_task_required_capacity()

Plot the minimum required capacity of a task.

plot_task_signals()

Plot the task-related load-tracking signals.

cache() inherited

Decorator to enable caching of the output of dataframe getter function in the trace cache.

call_on_trace() inherited

Call a method of a subclass on a given trace.

df_method() inherited

Dataframe function decorator.

get_all_events() inherited

Returns the set of all events used by any of the methods.

get_analysis_classes() inherited

get_default_plot_path() inherited

Return the default path to use to save plots for the analysis.

get_df_methods() inherited

get_logger() inherited

Provides a logging.Logger named after cls.

get_plot_methods() inherited

log_locals() inherited

Debugging aid: log the local variables of the calling function.

plot_method() inherited

Plot function decorator.

save_plot() inherited

Save a holoviews element or matplotlib.figure.Figure as an image file.

Attributes#

LoadTrackingAnalysis.name = 'load_tracking'#

Name of the analysis class.

Properties#

property LoadTrackingAnalysis.logger#

Inherited property, see lisa.utils.Loggable.logger

Convenience short-hand for self.get_logger().

Methods#

LoadTrackingAnalysis.df_cpus_signal(signal, cpus: Sequence[CPU] = None, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.load_tracking.df_cpus_signal()

Get the load-tracking signals for the CPUs

Returns:

a pandas.DataFrame with a column of the same name as the specified signal, and additional context columns such as cpu.

Parameters:

  • signal (str) –

    Signal name to get. Can be any of:

    • util

    • load

    • enqueued (util est enqueued)

    • capacity

  • cpus (list(lisa.trace.CPU) or None) – If specified, list of CPUs to select.

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • one group of: (sched_pelt_cfs or sched_load_cfs_rq or sched_load_avg_cpu) , sched_util_est_cfs , sched_cpu_capacity

LoadTrackingAnalysis.df_task_signal(task, signal, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.load_tracking.df_task_signal()

Same as df_tasks_signal() but for one task only.

Parameters:

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • one group of: (sched_pelt_se or sched_load_se or sched_load_avg_task) , sched_util_est_se , sched_util_est_se_unified

LoadTrackingAnalysis.df_tasks_signal(signal, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.load_tracking.df_tasks_signal()

Get the load-tracking signals for the tasks

Returns:

a pandas.DataFrame with a column of the same name as the specified signal, and additional context columns such as cpu.

Parameters:

signal (str) –

Signal name to get. Can be any of:

  • util

  • load

  • enqueued (util est enqueued)

  • ewma (util est ewma)

  • util_est_unified (unified util est)

  • required_capacity

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • one group of: (sched_pelt_se or sched_load_se or sched_load_avg_task) , sched_util_est_se , sched_util_est_se_unified

LoadTrackingAnalysis.df_top_big_tasks(util_threshold, min_samples=100, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.load_tracking.df_top_big_tasks()

Tasks which had ‘utilization’ samples bigger than the specified threshold

Parameters:

  • min_samples (int) – minumum number of samples over the min_utilization

  • min_utilization (int) – minimum utilization used to filter samples default: capacity of a little cluster

Returns:

a pandas.DataFrame with:

  • Task PIDs as index

  • A samples column (The number of util samples above the threshold)

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • one group of: (sched_pelt_se or sched_load_se or sched_load_avg_task) , sched_util_est_se , sched_util_est_se_unified

LoadTrackingAnalysis.plot_cpus_signals(cpus: Sequence[CPU] = None, signals: Sequence[str] = ['util', 'load'], *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

signals=['util', 'load']

Called on Trace instances as trace.ana.load_tracking.plot_cpus_signals()

Plot the CPU-related load-tracking signals

Parameters:

  • cpus (list(int)) – list of CPUs to be plotted

  • signals (list(str)) – List of signals to plot.

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • one group of: (sched_pelt_cfs or sched_load_cfs_rq or sched_load_avg_cpu) , sched_util_est_cfs , sched_cpu_capacity

  • optional: sched_overutilized

LoadTrackingAnalysis.plot_task_placement(task: TaskID, *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

task=big_0-0

Called on Trace instances as trace.ana.load_tracking.plot_task_placement()

Plot the CPU placement of the task

Parameters:

task (str or int or tuple) – The name or PID of the task, or a tuple (pid, comm)

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • one group of: (sched_pelt_se or sched_load_se or sched_load_avg_task) , sched_util_est_se , sched_util_est_se_unified

LoadTrackingAnalysis.plot_task_required_capacity(task: TaskID, *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

task=big_0-0

Called on Trace instances as trace.ana.load_tracking.plot_task_required_capacity()

Plot the minimum required capacity of a task

Parameters:

task (str or int or tuple) – The name or PID of the task, or a tuple (pid, comm)

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • one group of: (sched_pelt_se or sched_load_se or sched_load_avg_task) , sched_util_est_se , sched_util_est_se_unified

LoadTrackingAnalysis.plot_task_signals(task: TaskID, signals: Sequence[str] = ['util', 'load'], *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

signals=['util', 'load'], task=big_0-0

Called on Trace instances as trace.ana.load_tracking.plot_task_signals()

Plot the task-related load-tracking signals

Parameters:

  • task (str or int or tuple) – The name or PID of the task, or a tuple (pid, comm)

  • signals (list(str)) – List of signals to plot.

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • one group of: (sched_pelt_se or sched_load_se or sched_load_avg_task) , sched_util_est_se , sched_util_est_se_unified

classmethod LoadTrackingAnalysis.cache(f, fmt='parquet', ignored_params=None)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.cache()

Decorator to enable caching of the output of dataframe getter function in the trace cache.

classmethod LoadTrackingAnalysis.call_on_trace(meth, trace, meth_kwargs)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.call_on_trace()

Call a method of a subclass on a given trace.

classmethod LoadTrackingAnalysis.df_method(f, index=None)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.df_method()

Dataframe function decorator.

classmethod LoadTrackingAnalysis.get_all_events()#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.get_all_events()

Returns the set of all events used by any of the methods.

classmethod LoadTrackingAnalysis.get_analysis_classes()#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.get_analysis_classes()

LoadTrackingAnalysis.get_default_plot_path(**kwargs)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.get_default_plot_path()

Return the default path to use to save plots for the analysis.

classmethod LoadTrackingAnalysis.get_df_methods(*args, **kwargs)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.get_df_methods()

classmethod LoadTrackingAnalysis.get_logger(suffix=None)#

Inherited method, see lisa.utils.Loggable.get_logger()

Provides a logging.Logger named after cls.

classmethod LoadTrackingAnalysis.get_plot_methods(*args, **kwargs)#

Inherited method, see lisa.analysis.base.AnalysisHelpers.get_plot_methods()

classmethod LoadTrackingAnalysis.log_locals(var_names=None, level='debug')#

Inherited method, see lisa.utils.Loggable.log_locals()

Debugging aid: log the local variables of the calling function.

classmethod LoadTrackingAnalysis.plot_method(f)#

Inherited method, see lisa.analysis.base.AnalysisHelpers.plot_method()

Plot function decorator.

LoadTrackingAnalysis.save_plot(figure, filepath=None, img_format=None, backend=None)#

Inherited method, see lisa.analysis.base.AnalysisHelpers.save_plot()

Save a holoviews element or matplotlib.figure.Figure as an image file.

LoadTrackingAnalysis.df_cpus_signals(*, df_fmt=None)[source]#

Called on Trace instances as trace.ana.load_tracking.df_cpus_signals()

Get the load-tracking signals for the CPUs

Returns:

a pandas.DataFrame with:

  • A util column (the average utilization of a CPU at time t)

  • A load column (the average load of a CPU at time t)

Deprecated since version 2.0.

df_cpus_signals() is deprecated and will be removed in version 4.0, use lisa.analysis.load_tracking.LoadTrackingAnalysis.df_cpus_signal() instead

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • sched_pelt_cfs or sched_load_cfs_rq or sched_load_avg_cpu

LoadTrackingAnalysis.df_tasks_signals(*, df_fmt=None)[source]#

Called on Trace instances as trace.ana.load_tracking.df_tasks_signals()

Get the load-tracking signals for the tasks

Returns:

a pandas.DataFrame with:

  • A util column (the average utilization of a task at time t)

  • A load column (the average load of a task at time t)

If CPU capacity information is available:

  • A required_capacity column (the minimum available CPU capacity required to run this task without being CPU-bound)

Deprecated since version 2.0.

df_tasks_signals() is deprecated and will be removed in version 4.0, use lisa.analysis.load_tracking.LoadTrackingAnalysis.df_tasks_signal() instead

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • sched_pelt_se or sched_load_se or sched_load_avg_task