Source code for dask.diagnostics.profile_visualize

import random
from bisect import bisect_left
from distutils.version import LooseVersion
from itertools import cycle
from operator import itemgetter, add

from ..utils import funcname, import_required, apply
from ..core import istask


_BOKEH_MISSING_MSG = "Diagnostics plots require `bokeh` to be installed"
_TOOLZ_MISSING_MSG = "Diagnostics plots require `toolz` to be installed"


def unquote(expr):
    if istask(expr):
        if expr[0] in (tuple, list, set):
            return expr[0](map(unquote, expr[1]))
        elif (
            expr[0] == dict
            and isinstance(expr[1], list)
            and isinstance(expr[1][0], list)
        ):
            return dict(map(unquote, expr[1]))
    return expr


def pprint_task(task, keys, label_size=60):
    """Return a nicely formatted string for a task.

    Parameters
    ----------
    task:
        Value within dask graph to render as text
    keys: iterable
        List of keys within dask graph
    label_size: int (optional)
        Maximum size of output label, defaults to 60

    Examples
    --------
    >>> from operator import add, mul
    >>> dsk = {'a': 1,
    ...        'b': 2,
    ...        'c': (add, 'a', 'b'),
    ...        'd': (add, (mul, 'a', 'b'), 'c'),
    ...        'e': (sum, ['a', 'b', 5]),
    ...        'f': (add,),
    ...        'g': []}

    >>> pprint_task(dsk['c'], dsk)
    'add(_, _)'
    >>> pprint_task(dsk['d'], dsk)
    'add(mul(_, _), _)'
    >>> pprint_task(dsk['e'], dsk)
    'sum([_, _, *])'
    >>> pprint_task(dsk['f'], dsk)
    'add()'
    >>> pprint_task(dsk['g'], dsk)
    '[]'
    """
    if istask(task):
        func = task[0]
        if func is apply:
            head = funcname(task[1])
            tail = ")"
            args = unquote(task[2]) if len(task) > 2 else ()
            kwargs = unquote(task[3]) if len(task) > 3 else {}
        else:
            if hasattr(func, "funcs"):
                head = "(".join(funcname(f) for f in func.funcs)
                tail = ")" * len(func.funcs)
            else:
                head = funcname(task[0])
                tail = ")"
            args = task[1:]
            kwargs = {}
        if args or kwargs:
            label_size2 = int(
                (label_size - len(head) - len(tail)) // (len(args) + len(kwargs))
            )
            pprint = lambda t: pprint_task(t, keys, label_size2)
        if args:
            if label_size2 > 5:
                args = ", ".join(pprint(t) for t in args)
            else:
                args = "..."
        else:
            args = ""
        if kwargs:
            if label_size2 > 5:
                kwargs = ", " + ", ".join(
                    "{0}={1}".format(k, pprint(v)) for k, v in sorted(kwargs.items())
                )
            else:
                kwargs = ", ..."
        else:
            kwargs = ""
        return "{0}({1}{2}{3}".format(head, args, kwargs, tail)
    elif isinstance(task, list):
        if not task:
            return "[]"
        elif len(task) > 3:
            result = pprint_task(task[:3], keys, label_size)
            return result[:-1] + ", ...]"
        else:
            label_size2 = int((label_size - 2 - 2 * len(task)) // len(task))
            args = ", ".join(pprint_task(t, keys, label_size2) for t in task)
            return "[{0}]".format(args)
    else:
        try:
            if task in keys:
                return "_"
            else:
                return "*"
        except TypeError:
            return "*"


def get_colors(palette, funcs):
    """Get a dict mapping funcs to colors from palette.

    Parameters
    ----------
    palette : string
        Name of the bokeh palette to use, must be a member of
        bokeh.palettes.all_palettes.
    funcs : iterable
        Iterable of function names
    """
    palettes = import_required("bokeh.palettes", _BOKEH_MISSING_MSG)
    tz = import_required("tlz", _TOOLZ_MISSING_MSG)

    unique_funcs = list(sorted(tz.unique(funcs)))
    n_funcs = len(unique_funcs)
    palette_lookup = palettes.all_palettes[palette]
    keys = list(sorted(palette_lookup.keys()))
    index = keys[min(bisect_left(keys, n_funcs), len(keys) - 1)]
    palette = palette_lookup[index]
    # Some bokeh palettes repeat colors, we want just the unique set
    palette = list(tz.unique(palette))
    if len(palette) > n_funcs:
        # Consistently shuffle palette - prevents just using low-range
        random.Random(42).shuffle(palette)
    color_lookup = dict(zip(unique_funcs, cycle(palette)))
    return [color_lookup[n] for n in funcs]


[docs]def visualize(profilers, file_path=None, show=True, save=True, **kwargs): """Visualize the results of profiling in a bokeh plot. If multiple profilers are passed in, the plots are stacked vertically. Parameters ---------- profilers : profiler or list Profiler or list of profilers. file_path : string, optional Name of the plot output file. show : boolean, optional If True (default), the plot is opened in a browser. save : boolean, optional If True (default), the plot is saved to disk. **kwargs Other keyword arguments, passed to bokeh.figure. These will override all defaults set by visualize. Returns ------- The completed bokeh plot object. """ bp = import_required("bokeh.plotting", _BOKEH_MISSING_MSG) import bokeh if LooseVersion(bokeh.__version__) >= "0.12.10": from bokeh.io import state in_notebook = state.curstate().notebook else: from bokeh.io import _state in_notebook = _state._notebook if not in_notebook: file_path = file_path or "profile.html" bp.output_file(file_path) if not isinstance(profilers, list): profilers = [profilers] figs = [prof._plot(**kwargs) for prof in profilers] # Stack the plots if len(figs) == 1: p = figs[0] else: top = figs[0] for f in figs[1:]: f.x_range = top.x_range f.title = None f.min_border_top = 20 f.plot_height -= 30 for f in figs[:-1]: f.xaxis.axis_label = None f.min_border_bottom = 20 f.plot_height -= 30 for f in figs: f.min_border_left = 75 f.min_border_right = 75 p = bp.gridplot([[f] for f in figs]) if show: bp.show(p) if file_path and save: bp.save(p) return p
def _get_figure_keywords(): bp = import_required("bokeh.plotting", _BOKEH_MISSING_MSG) o = bp.Figure.properties() o.add("tools") return o def plot_tasks(results, dsk, palette="Viridis", label_size=60, **kwargs): """Visualize the results of profiling in a bokeh plot. Parameters ---------- results : sequence Output of Profiler.results dsk : dict The dask graph being profiled. palette : string, optional Name of the bokeh palette to use, must be a member of bokeh.palettes.all_palettes. label_size: int (optional) Maximum size of output labels in plot, defaults to 60 **kwargs Other keyword arguments, passed to bokeh.figure. These will override all defaults set by visualize. Returns ------- The completed bokeh plot object. """ bp = import_required("bokeh.plotting", _BOKEH_MISSING_MSG) from bokeh.models import HoverTool tz = import_required("tlz", _TOOLZ_MISSING_MSG) defaults = dict( title="Profile Results", tools="hover,save,reset,xwheel_zoom,xpan", toolbar_location="above", plot_width=800, plot_height=300, ) defaults.update((k, v) for (k, v) in kwargs.items() if k in _get_figure_keywords()) if results: keys, tasks, starts, ends, ids = zip(*results) id_group = tz.groupby(itemgetter(4), results) timings = dict( (k, [i.end_time - i.start_time for i in v]) for (k, v) in id_group.items() ) id_lk = dict( (t[0], n) for (n, t) in enumerate( sorted(timings.items(), key=itemgetter(1), reverse=True) ) ) left = min(starts) right = max(ends) p = bp.figure( y_range=[str(i) for i in range(len(id_lk))], x_range=[0, right - left], **defaults ) data = {} data["width"] = width = [e - s for (s, e) in zip(starts, ends)] data["x"] = [w / 2 + s - left for (w, s) in zip(width, starts)] data["y"] = [id_lk[i] + 1 for i in ids] data["function"] = funcs = [pprint_task(i, dsk, label_size) for i in tasks] data["color"] = get_colors(palette, funcs) data["key"] = [str(i) for i in keys] source = bp.ColumnDataSource(data=data) p.rect( source=source, x="x", y="y", height=1, width="width", color="color", line_color="gray", ) else: p = bp.figure(y_range=[str(i) for i in range(8)], x_range=[0, 10], **defaults) p.grid.grid_line_color = None p.axis.axis_line_color = None p.axis.major_tick_line_color = None p.yaxis.axis_label = "Worker ID" p.xaxis.axis_label = "Time (s)" hover = p.select(HoverTool) hover.tooltips = """ <div> <span style="font-size: 14px; font-weight: bold;">Key:</span>&nbsp; <span style="font-size: 10px; font-family: Monaco, monospace;">@key</span> </div> <div> <span style="font-size: 14px; font-weight: bold;">Task:</span>&nbsp; <span style="font-size: 10px; font-family: Monaco, monospace;">@function</span> </div> """ hover.point_policy = "follow_mouse" return p def plot_resources(results, palette="Viridis", **kwargs): """Plot resource usage in a bokeh plot. Parameters ---------- results : sequence Output of ResourceProfiler.results palette : string, optional Name of the bokeh palette to use, must be a member of bokeh.palettes.all_palettes. **kwargs Other keyword arguments, passed to bokeh.figure. These will override all defaults set by plot_resources. Returns ------- The completed bokeh plot object. """ bp = import_required("bokeh.plotting", _BOKEH_MISSING_MSG) import bokeh from bokeh import palettes from bokeh.models import LinearAxis, Range1d defaults = dict( title="Profile Results", tools="save,reset,xwheel_zoom,xpan", toolbar_location="above", plot_width=800, plot_height=300, ) defaults.update((k, v) for (k, v) in kwargs.items() if k in _get_figure_keywords()) if results: t, mem, cpu = zip(*results) left, right = min(t), max(t) t = [i - left for i in t] p = bp.figure( y_range=fix_bounds(0, max(cpu), 100), x_range=fix_bounds(0, right - left, 1), **defaults ) else: t = mem = cpu = [] p = bp.figure(y_range=(0, 100), x_range=(0, 1), **defaults) colors = palettes.all_palettes[palette][6] p.line( t, cpu, color=colors[0], line_width=4, **{ "legend_label" if LooseVersion(bokeh.__version__) >= "1.4" else "legend": "% CPU" } ) p.yaxis.axis_label = "% CPU" p.extra_y_ranges = { "memory": Range1d( *fix_bounds(min(mem) if mem else 0, max(mem) if mem else 100, 100) ) } p.line( t, mem, color=colors[2], y_range_name="memory", line_width=4, **{ "legend_label" if LooseVersion(bokeh.__version__) >= "1.4" else "legend": "Memory" } ) p.add_layout(LinearAxis(y_range_name="memory", axis_label="Memory (MB)"), "right") p.xaxis.axis_label = "Time (s)" return p def fix_bounds(start, end, min_span): """Adjust end point to ensure span of at least `min_span`""" return start, max(end, start + min_span) def plot_cache( results, dsk, start_time, metric_name, palette="Viridis", label_size=60, **kwargs ): """Visualize the results of profiling in a bokeh plot. Parameters ---------- results : sequence Output of CacheProfiler.results dsk : dict The dask graph being profiled. start_time : float Start time of the profile. metric_name : string Metric used to measure cache size palette : string, optional Name of the bokeh palette to use, must be a member of bokeh.palettes.all_palettes. label_size: int (optional) Maximum size of output labels in plot, defaults to 60 **kwargs Other keyword arguments, passed to bokeh.figure. These will override all defaults set by visualize. Returns ------- The completed bokeh plot object. """ bp = import_required("bokeh.plotting", _BOKEH_MISSING_MSG) from bokeh.models import HoverTool tz = import_required("tlz", _TOOLZ_MISSING_MSG) defaults = dict( title="Profile Results", tools="hover,save,reset,wheel_zoom,xpan", toolbar_location="above", plot_width=800, plot_height=300, ) defaults.update((k, v) for (k, v) in kwargs.items() if k in _get_figure_keywords()) if results: starts, ends = list(zip(*results))[3:] tics = list(sorted(tz.unique(starts + ends))) groups = tz.groupby(lambda d: pprint_task(d[1], dsk, label_size), results) data = {} for k, vals in groups.items(): cnts = dict.fromkeys(tics, 0) for v in vals: cnts[v.cache_time] += v.metric cnts[v.free_time] -= v.metric data[k] = [0] + list(tz.accumulate(add, tz.pluck(1, sorted(cnts.items())))) tics = [0] + [i - start_time for i in tics] p = bp.figure(x_range=[0, max(tics)], **defaults) for (key, val), color in zip(data.items(), get_colors(palette, data.keys())): p.line( "x", "y", line_color=color, line_width=3, source=bp.ColumnDataSource( {"x": tics, "y": val, "label": [key for i in val]} ), ) else: p = bp.figure(y_range=[0, 10], x_range=[0, 10], **defaults) p.yaxis.axis_label = "Cache Size ({0})".format(metric_name) p.xaxis.axis_label = "Time (s)" hover = p.select(HoverTool) hover.tooltips = """ <div> <span style="font-size: 14px; font-weight: bold;">Task:</span>&nbsp; <span style="font-size: 10px; font-family: Monaco, monospace;">@label</span> </div> """ return p