from __future__ import annotations import random import warnings from bisect import bisect_left from itertools import cycle from operator import add, itemgetter from tlz import accumulate, groupby, pluck, unique from dask._compatibility import import_optional_dependency from dask.core import istask from dask.utils import apply, funcname, import_required def BOKEH_VERSION(): bokeh = import_optional_dependency("bokeh") from packaging.version import Version return Version(bokeh.__version__) _BOKEH_MISSING_MSG = "Diagnostics plots require `bokeh` 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( f"{k}={pprint(v)}" for k, v in sorted(kwargs.items()) ) else: kwargs = ", ..." else: kwargs = "" return f"{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 f"[{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) unique_funcs = sorted(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(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] def visualize( profilers, filename="profile.html", show=True, save=None, mode=None, **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. filename : 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 when not in notebook), the plot is saved to disk. mode : str, optional Mode passed to bokeh.output_file() **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.io import state if "file_path" in kwargs: warnings.warn( "The file_path keyword argument is deprecated " "and will be removed in a future release. " "Please use filename instead.", category=FutureWarning, stacklevel=2, ) filename = kwargs.pop("file_path") if save is None: save = not state.curstate().notebook 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 if BOKEH_VERSION().major < 3: f.plot_height -= 30 else: f.height -= 30 for f in figs[:-1]: f.xaxis.axis_label = None f.min_border_bottom = 20 if BOKEH_VERSION().major < 3: f.plot_height -= 30 else: f.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 save: bp.output_file(filename, mode=mode) bp.save(p) return p def plot_tasks( results, dsk, start_time, end_time, 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. start_time : float Start time of the profile in seconds end_time : float End time of the profile in seconds 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 defaults = dict( title="Profile Results", tools="hover,save,reset,xwheel_zoom,xpan", toolbar_location="above", width=800, height=300, ) # Support plot_width and plot_height for backwards compatibility if "plot_width" in kwargs: kwargs["width"] = kwargs.pop("plot_width") if "plot_height" in kwargs: kwargs["height"] = kwargs.pop("plot_height") defaults.update(**kwargs) if results: keys, tasks, starts, ends, ids = zip(*results) id_group = groupby(itemgetter(4), results) timings = { k: [i.end_time - i.start_time for i in v] for (k, v) in id_group.items() } id_lk = { t[0]: n for (n, t) in enumerate( sorted(timings.items(), key=itemgetter(1), reverse=True) ) } p = bp.figure( y_range=[str(i) for i in range(len(id_lk))], x_range=[0, end_time - start_time], **defaults, ) data = {} data["width"] = width = [e - s for (s, e) in zip(starts, ends)] data["x"] = [w / 2 + s - start_time 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 = """
Key:  @key
Task:  @function
""" hover.point_policy = "follow_mouse" return p def plot_resources(results, start_time, end_time, palette="Viridis", **kwargs): """Plot resource usage in a bokeh plot. Parameters ---------- results : sequence Output of ResourceProfiler.results start_time : float Start time of the profile in seconds end_time : float End time of the profile in seconds 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) from bokeh import palettes from bokeh.models import LinearAxis, Range1d defaults = dict( title="Profile Results", tools="save,reset,xwheel_zoom,xpan", toolbar_location="above", width=800, height=300, ) # Support plot_width and plot_height for backwards compatibility if "plot_width" in kwargs: kwargs["width"] = kwargs.pop("plot_width") if BOKEH_VERSION().major >= 3: warnings.warn("Use width instead of plot_width with Bokeh >= 3") if "plot_height" in kwargs: kwargs["height"] = kwargs.pop("plot_height") if BOKEH_VERSION().major >= 3: warnings.warn("Use height instead of plot_height with Bokeh >= 3") # Drop `label_size` to match `plot_cache` and `plot_tasks` kwargs if "label_size" in kwargs: kwargs.pop("label_size") defaults.update(**kwargs) if results: t, mem, cpu = zip(*results) left = start_time right = end_time 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="% 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="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, end_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 in seconds end_time : float End time of the profile in seconds 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 defaults = dict( title="Profile Results", tools="hover,save,reset,wheel_zoom,xpan", toolbar_location="above", width=800, height=300, ) # Support plot_width and plot_height for backwards compatibility if "plot_width" in kwargs: kwargs["width"] = kwargs.pop("plot_width") if BOKEH_VERSION().major >= 3: warnings.warn("Use width instead of plot_width with Bokeh >= 3") if "plot_height" in kwargs: kwargs["height"] = kwargs.pop("plot_height") if BOKEH_VERSION().major >= 3: warnings.warn("Use height instead of plot_height with Bokeh >= 3") defaults.update(**kwargs) if results: starts, ends = list(zip(*results))[3:] tics = sorted(unique(starts + ends)) groups = 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(accumulate(add, pluck(1, sorted(cnts.items())))) tics = [0] + [i - start_time for i in tics] p = bp.figure(x_range=[0, end_time - start_time], **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 = f"Cache Size ({metric_name})" p.xaxis.axis_label = "Time (s)" hover = p.select(HoverTool) hover.tooltips = """
Task:  @label
""" return p