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5e7adc5c3f
* Add cuda architectures to build wheel for * Package scripts in wheel * Separate cuda major version extraction to fix architecutre selection logic * Add back statement printing cuda version * [pre-commit.ci] auto code formatting --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
128 lines
3.2 KiB
Python
128 lines
3.2 KiB
Python
#!/usr/bin/env python
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import argparse
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import os
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import sys
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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try:
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from nvbench_json import reader
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except ImportError:
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from scripts.nvbench_json import reader
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def parse_files():
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help_text = "%(prog)s [nvbench.out.json | dir/] ..."
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parser = argparse.ArgumentParser(prog="nvbench_histogram", usage=help_text)
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args, files_or_dirs = parser.parse_known_args()
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filenames = []
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for file_or_dir in files_or_dirs:
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if os.path.isdir(file_or_dir):
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for f in os.listdir(file_or_dir):
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if os.path.splitext(f)[1] != ".json":
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continue
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filename = os.path.join(file_or_dir, f)
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if os.path.isfile(filename) and os.path.getsize(filename) > 0:
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filenames.append(filename)
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else:
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filenames.append(file_or_dir)
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filenames.sort()
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if not filenames:
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parser.print_help()
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exit(0)
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return filenames
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def extract_filename(summary):
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summary_data = summary["data"]
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value_data = next(filter(lambda v: v["name"] == "filename", summary_data))
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assert value_data["type"] == "string"
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return value_data["value"]
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def extract_size(summary):
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summary_data = summary["data"]
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value_data = next(filter(lambda v: v["name"] == "size", summary_data))
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assert value_data["type"] == "int64"
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return int(value_data["value"])
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def parse_samples_meta(filename, state):
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summaries = state["summaries"]
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if not summaries:
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return None, None
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summary = next(
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filter(lambda s: s["tag"] == "nv/json/bin:nv/cold/sample_times", summaries),
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None,
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)
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if not summary:
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return None, None
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sample_filename = extract_filename(summary)
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# If not absolute, the path is relative to the associated .json file:
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if not os.path.isabs(sample_filename):
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sample_filename = os.path.join(os.path.dirname(filename), sample_filename)
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sample_count = extract_size(summary)
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return sample_count, sample_filename
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def parse_samples(filename, state):
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sample_count, samples_filename = parse_samples_meta(filename, state)
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if not sample_count or not samples_filename:
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return []
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with open(samples_filename, "rb") as f:
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samples = np.fromfile(f, "<f4")
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assert sample_count == len(samples)
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return samples
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def to_df(data):
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return pd.DataFrame.from_dict(dict([(k, pd.Series(v)) for k, v in data.items()]))
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def parse_json(filename):
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json_root = reader.read_file(filename)
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samples_data = {}
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for bench in json_root["benchmarks"]:
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print("Benchmark: {}".format(bench["name"]))
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for state in bench["states"]:
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print("State: {}".format(state["name"]))
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samples = parse_samples(filename, state)
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if len(samples) == 0:
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continue
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samples_data["{} {}".format(bench["name"], state["name"])] = samples
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return to_df(samples_data)
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def main():
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filenames = parse_files()
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dfs = [parse_json(filename) for filename in filenames]
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df = pd.concat(dfs, ignore_index=True)
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sns.displot(df, rug=True, kind="kde", fill=True)
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plt.show()
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if __name__ == "__main__":
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sys.exit(main())
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