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first version of something that seems usable by others
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,145 +1,164 @@ | ||
| import os | ||
| from kernel_tuner import read_cache # actually kernel_tuner.util read_cache | ||
|
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| import panel as pn | ||
| import panel.widgets as pnw | ||
| import pandas as pd | ||
| from bokeh.models import HoverTool, LinearColorMapper | ||
| from bokeh.plotting import ColumnDataSource, figure | ||
| pn.extension() | ||
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| class KTdashboard: | ||
| """ Main object to instantiate to hold everything related to a running dashboard""" | ||
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| def __init__(self, cache_file, demo=False): | ||
| self.demo = demo | ||
| self.cache_file = cache_file | ||
| # read in the cachefile | ||
| self.cache_last_modified = os.stat(cache_file).st_mtime | ||
| cached_data = read_cache(cache_file, open_cache=False) | ||
| self.kernel_name = cached_data["kernel_name"] | ||
| self.device_name = cached_data["device_name"] | ||
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| # get the performance data | ||
| data = list(cached_data["cache"].values()) | ||
| data = [d for d in data if d["time"] != 1e20] | ||
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| # use all data or just the first 1000 in demo mode | ||
| self.index = len(data) | ||
| if self.demo: | ||
| self.index = 1000 | ||
|
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||
| # figure out which keys are interesting | ||
| single_value_tune_param_keys = [key for key in cached_data["tune_params_keys"] if len(cached_data["tune_params"][key]) == 1] | ||
| tune_param_keys = [key for key in cached_data["tune_params_keys"] if key not in single_value_tune_param_keys] | ||
| single_value_keys = [key for key in data[0].keys() if not isinstance(data[0][key],list) and key not in single_value_tune_param_keys] | ||
| output_keys = [key for key in single_value_keys if key not in tune_param_keys] | ||
| float_keys = [key for key in output_keys if isinstance(data[0][key], float)] | ||
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| self.single_value_tune_param_keys = single_value_tune_param_keys | ||
| self.tune_param_keys = tune_param_keys | ||
| self.single_value_keys = single_value_keys | ||
| self.output_keys = output_keys | ||
| self.float_keys = float_keys | ||
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| self.data_df = pd.DataFrame(data[:self.index])[single_value_keys] | ||
| self.source = ColumnDataSource(data=self.data_df) | ||
| self.data = data | ||
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| plot_options=dict(plot_height=600, plot_width=900) | ||
| plot_options['tools'] = [HoverTool(tooltips=[(k, "@{"+k+"}" + ("{0.00}" if k in float_keys else "")) for k in single_value_keys]), "box_select,box_zoom,save,reset"] | ||
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| self.plot_options = plot_options | ||
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| # setup widgets | ||
| self.yvariable = pnw.Select(name='Y', value='GFLOP/s', options=single_value_keys) | ||
| self.xvariable = pnw.Select(name='X', value='index', options=['index']+single_value_keys) | ||
| self.colorvariable = pnw.Select(name='Color By', value='GFLOP/s', options=single_value_keys) | ||
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| self.scatter = pn.bind(self.make_scatter, xvariable=self.xvariable, yvariable=self.yvariable, color_by=self.colorvariable) | ||
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| self.dashboard = pn.template.BootstrapTemplate(title='Kernel Tuner Dashboard') | ||
| self.dashboard.sidebar.append(pn.Column(self.yvariable, self.xvariable, self.colorvariable)) | ||
| self.dashboard.main.append(self.scatter) | ||
| self.dashboard.servable() | ||
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| def update_colors(self, color_by): | ||
| color_mapper = LinearColorMapper(palette='Viridis256', low=min(self.data_df[color_by]), | ||
| high=max(self.data_df[color_by])) | ||
| color = {'field': color_by, 'transform': color_mapper} | ||
| return color | ||
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| def make_scatter(self, xvariable, yvariable, color_by): | ||
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| color = self.update_colors(color_by) | ||
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| x = xvariable | ||
| y = yvariable | ||
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| f = figure(**self.plot_options) | ||
| f.circle(x, y, size=5, color=color, alpha=0.5, source=self.source) | ||
| f.xaxis.axis_label = x | ||
| f.yaxis.axis_label = y | ||
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| pane = pn.Column(pn.pane.Markdown(f"## Auto-tuning {self.kernel_name} on {self.device_name}"), pn.pane.Bokeh(f)) | ||
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| return pane | ||
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| def update_plot(self, i): | ||
| stream_dict = {k:[v] for k,v in dict(self.data[i], index=i).items() if k in ['index']+self.single_value_keys} | ||
| self.source.stream(stream_dict) | ||
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| def update_data(self): | ||
| if os.stat(self.cache_file).st_mtime > self.cache_last_modified: | ||
| cached_data = read_cache(self.cache_file, open_cache=False) | ||
|
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| data = list(cached_data["cache"].values()) | ||
| data = [d for d in data if d["time"] != 1e20] | ||
|
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| # stream the data that has not been streamed yet | ||
| self.source.stream(pd.DataFrame([data[self.index:]])) # this may not work, we might need to convert it into a dict like in update_plot | ||
| self.index = len(self.data) | ||
| if self.demo: | ||
| if self.index < (len(self.data)-1): | ||
| self.update_plot(self.index) | ||
| self.index += 1 | ||
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| def print_usage(): | ||
| print("Usage: ./dashboard.py [-demo] filename") | ||
| print(" -demo option to enable demo mode that mimicks a running Kernel Tuner session") | ||
| print(" filename name of the cachefile") | ||
| exit(0) | ||
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| if __name__ == "__main__": | ||
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| if len(sys.argv) < 2: | ||
| print_usage() | ||
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| filename = "" | ||
| demo = False | ||
| if len(sys.argv) == 2: | ||
| if os.path.isfile(sys.argv[1]): | ||
| filename = sys.argv[1] | ||
| else: | ||
| print("Cachefile not found") | ||
| exit(1) | ||
| elif len(sys.argv) == 3: | ||
| if sys.argv[1] == "-demo": | ||
| demo = True | ||
| else: | ||
| print_usage() | ||
| if os.path.isfile(sys.argv[2]): | ||
| filename = sys.argv[2] | ||
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| db = KTdashboard(filename, demo=demo) | ||
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| def dashboard_f(): | ||
| pn.state.add_periodic_callback(db.update_data, 1000) | ||
| return db.dashboard | ||
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| server = pn.serve(dashboard_f) | ||
| #!/usr/bin/env python | ||
| import json | ||
| import sys | ||
| import os | ||
|
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||
| import panel as pn | ||
| import panel.widgets as pnw | ||
| import pandas as pd | ||
| from bokeh.models import HoverTool, LinearColorMapper | ||
| from bokeh.plotting import ColumnDataSource, figure | ||
|
|
||
|
|
||
| class KTdashboard: | ||
| """ Main object to instantiate to hold everything related to a running dashboard""" | ||
|
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||
| def __init__(self, cache_file, demo=False): | ||
| self.demo = demo | ||
| self.cache_file = cache_file | ||
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| # read in the cachefile | ||
| self.cache_file_handle = open(cache_file, "r") | ||
| filestr = self.cache_file_handle.read().strip() | ||
| # if file was not properly closed, pretend it was properly closed | ||
| if not filestr[-3:] == "}\n}": | ||
| # remove the trailing comma if any, and append closing brackets | ||
| if filestr[-1] == ",": | ||
| filestr = filestr[:-1] | ||
| filestr = filestr + "}\n}" | ||
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| cached_data = json.loads(filestr) | ||
| self.kernel_name = cached_data["kernel_name"] | ||
| self.device_name = cached_data["device_name"] | ||
|
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||
| # get the performance data | ||
| data = list(cached_data["cache"].values()) | ||
| data = [d for d in data if d["time"] != 1e20] | ||
|
|
||
| # use all data or just the first 1000 in demo mode | ||
| self.index = len(data) | ||
| if self.demo: | ||
| self.index = 1000 | ||
|
|
||
| # figure out which keys are interesting | ||
| single_value_tune_param_keys = [key for key in cached_data["tune_params_keys"] if len(cached_data["tune_params"][key]) == 1] | ||
| tune_param_keys = [key for key in cached_data["tune_params_keys"] if key not in single_value_tune_param_keys] | ||
| single_value_keys = [key for key in data[0].keys() if not isinstance(data[0][key],list) and key not in single_value_tune_param_keys] | ||
| output_keys = [key for key in single_value_keys if key not in tune_param_keys] | ||
| float_keys = [key for key in output_keys if isinstance(data[0][key], float)] | ||
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| self.single_value_tune_param_keys = single_value_tune_param_keys | ||
| self.tune_param_keys = tune_param_keys | ||
| self.single_value_keys = single_value_keys | ||
| self.output_keys = output_keys | ||
| self.float_keys = float_keys | ||
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| self.data_df = pd.DataFrame(data[:self.index])[single_value_keys] | ||
| self.source = ColumnDataSource(data=self.data_df) | ||
| self.data = data | ||
|
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||
| plot_options=dict(plot_height=600, plot_width=900) | ||
| plot_options['tools'] = [HoverTool(tooltips=[(k, "@{"+k+"}" + ("{0.00}" if k in float_keys else "")) for k in single_value_keys]), "box_select,box_zoom,save,reset"] | ||
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| self.plot_options = plot_options | ||
|
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| # setup widgets | ||
| self.yvariable = pnw.Select(name='Y', value='GFLOP/s', options=single_value_keys) | ||
| self.xvariable = pnw.Select(name='X', value='index', options=['index']+single_value_keys) | ||
| self.colorvariable = pnw.Select(name='Color By', value='GFLOP/s', options=single_value_keys) | ||
|
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||
| # connect widgets with the function that draws the scatter plot | ||
| self.scatter = pn.bind(self.make_scatter, xvariable=self.xvariable, yvariable=self.yvariable, color_by=self.colorvariable) | ||
|
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||
| # actually build up the dashboard | ||
| self.dashboard = pn.template.BootstrapTemplate(title='Kernel Tuner Dashboard') | ||
| self.dashboard.sidebar.append(pn.Column(self.yvariable, self.xvariable, self.colorvariable)) | ||
| self.dashboard.main.append(self.scatter) | ||
| self.dashboard.servable() | ||
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| def __del__(self): | ||
| self.cache_file_handle.close() | ||
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| def update_colors(self, color_by): | ||
| color_mapper = LinearColorMapper(palette='Viridis256', low=min(self.data_df[color_by]), | ||
| high=max(self.data_df[color_by])) | ||
| color = {'field': color_by, 'transform': color_mapper} | ||
| return color | ||
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| def make_scatter(self, xvariable, yvariable, color_by): | ||
| color = self.update_colors(color_by) | ||
|
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| x = xvariable | ||
| y = yvariable | ||
|
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| f = figure(**self.plot_options) | ||
| f.circle(x, y, size=5, color=color, alpha=0.5, source=self.source) | ||
| f.xaxis.axis_label = x | ||
| f.yaxis.axis_label = y | ||
|
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| pane = pn.Column(pn.pane.Markdown(f"## Auto-tuning {self.kernel_name} on {self.device_name}"), pn.pane.Bokeh(f)) | ||
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| return pane | ||
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| def update_plot(self, i): | ||
| stream_dict = {k:[v] for k,v in dict(self.data[i], index=i).items() if k in ['index']+self.single_value_keys} | ||
| self.source.stream(stream_dict) | ||
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| def update_data(self): | ||
| if not self.demo: | ||
| new_contents = self.cache_file_handle.read().strip() | ||
| if new_contents: | ||
|
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| # process new contents (parse as JSON, make into dict that goes into source.stream) | ||
| new_contents_json = "{" + new_contents[:-1] + "}" | ||
| new_data = list(json.loads(new_contents_json).values()) | ||
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| for i,element in enumerate(new_data): | ||
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| stream_dict = {k:[v] for k,v in dict(element, index=self.index+i).items() if k in ['index']+self.single_value_keys} | ||
| self.source.stream(stream_dict) | ||
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| self.index += len(new_data) | ||
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| if self.demo: | ||
| if self.index < (len(self.data)-1): | ||
| self.update_plot(self.index) | ||
| self.index += 1 | ||
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| def print_usage(): | ||
| print("Usage: ./dashboard.py [-demo] filename") | ||
| print(" -demo option to enable demo mode that mimicks a running Kernel Tuner session") | ||
| print(" filename name of the cachefile") | ||
| exit(0) | ||
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| if __name__ == "__main__": | ||
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| if len(sys.argv) < 2: | ||
| print_usage() | ||
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| filename = "" | ||
| demo = False | ||
| if len(sys.argv) == 2: | ||
| if os.path.isfile(sys.argv[1]): | ||
| filename = sys.argv[1] | ||
| else: | ||
| print("Cachefile not found") | ||
| exit(1) | ||
| elif len(sys.argv) == 3: | ||
| if sys.argv[1] == "-demo": | ||
| demo = True | ||
| else: | ||
| print_usage() | ||
| if os.path.isfile(sys.argv[2]): | ||
| filename = sys.argv[2] | ||
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| db = KTdashboard(filename, demo=demo) | ||
|
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| def dashboard_f(): | ||
| """ wrapper function to add the callback, doesn't work without this construct """ | ||
| pn.state.add_periodic_callback(db.update_data, 1000) | ||
| return db.dashboard | ||
| server = pn.serve(dashboard_f) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,34 @@ | ||
| #!/usr/bin/env python | ||
| import shutil | ||
| import time | ||
| import json | ||
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| from kernel_tuner.util import read_cache | ||
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| if __name__ == "__main__": | ||
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| target_file = "test_cache.json" | ||
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| # reset target_file for appending | ||
| shutil.copy("test_cache_template.json", target_file) | ||
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| source_cache = read_cache("test_cache_1000.json", open_cache=False)["cache"] | ||
| source = list(source_cache.values()) | ||
| source_keys = list(source_cache.keys()) | ||
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| index = 0 | ||
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| with open(target_file, 'a') as fh: | ||
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| while index < len(source): | ||
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| time.sleep(5) # seconds | ||
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| fh.write("\n" + json.dumps({source_keys[index]: source[index]})[1:-1] + ",") | ||
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| index += 1 | ||
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| fh.flush() |
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