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Convert .buildkite to ruff format (#17656)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-05-13 10:28:31 +01:00
committed by GitHub
parent 23b3134eb5
commit 98fcba1575
11 changed files with 182 additions and 114 deletions
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89
.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py
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@@ -65,18 +65,18 @@ def read_markdown(file):
def results_to_json(latency, throughput, serving):
return json.dumps({
'latency': latency.to_dict(),
'throughput': throughput.to_dict(),
'serving': serving.to_dict()
})
return json.dumps(
{
"latency": latency.to_dict(),
"throughput": throughput.to_dict(),
"serving": serving.to_dict(),
}
)
if __name__ == "__main__":
# collect results
for test_file in results_folder.glob("*.json"):
with open(test_file) as f:
raw_result = json.loads(f.read())
@@ -120,7 +120,8 @@ if __name__ == "__main__":
for perc in [10, 25, 50, 75, 90, 99]:
# Multiply 1000 to convert the time unit from s to ms
raw_result.update(
{f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]})
{f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]}
)
raw_result["avg_latency"] = raw_result["avg_latency"] * 1000
# add the result to raw_result
@@ -153,26 +154,27 @@ if __name__ == "__main__":
serving_results = pd.DataFrame.from_dict(serving_results)
throughput_results = pd.DataFrame.from_dict(throughput_results)
raw_results_json = results_to_json(latency_results, throughput_results,
serving_results)
raw_results_json = results_to_json(
latency_results, throughput_results, serving_results
)
# remapping the key, for visualization purpose
if not latency_results.empty:
latency_results = latency_results[list(
latency_column_mapping.keys())].rename(
columns=latency_column_mapping)
latency_results = latency_results[list(latency_column_mapping.keys())].rename(
columns=latency_column_mapping
)
if not serving_results.empty:
serving_results = serving_results[list(
serving_column_mapping.keys())].rename(
columns=serving_column_mapping)
serving_results = serving_results[list(serving_column_mapping.keys())].rename(
columns=serving_column_mapping
)
if not throughput_results.empty:
throughput_results = throughput_results[list(
throughput_results_column_mapping.keys())].rename(
columns=throughput_results_column_mapping)
throughput_results = throughput_results[
list(throughput_results_column_mapping.keys())
].rename(columns=throughput_results_column_mapping)
processed_results_json = results_to_json(latency_results,
throughput_results,
serving_results)
processed_results_json = results_to_json(
latency_results, throughput_results, serving_results
)
for df in [latency_results, serving_results, throughput_results]:
if df.empty:
@@ -184,38 +186,39 @@ if __name__ == "__main__":
# The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
# we want to turn it into "8xGPUTYPE"
df["GPU"] = df["GPU"].apply(
lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}")
lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}"
)
# get markdown tables
latency_md_table = tabulate(latency_results,
headers='keys',
tablefmt='pipe',
showindex=False)
serving_md_table = tabulate(serving_results,
headers='keys',
tablefmt='pipe',
showindex=False)
throughput_md_table = tabulate(throughput_results,
headers='keys',
tablefmt='pipe',
showindex=False)
latency_md_table = tabulate(
latency_results, headers="keys", tablefmt="pipe", showindex=False
)
serving_md_table = tabulate(
serving_results, headers="keys", tablefmt="pipe", showindex=False
)
throughput_md_table = tabulate(
throughput_results, headers="keys", tablefmt="pipe", showindex=False
)
# document the result
with open(results_folder / "benchmark_results.md", "w") as f:
results = read_markdown("../.buildkite/nightly-benchmarks/" +
"performance-benchmarks-descriptions.md")
results = read_markdown(
"../.buildkite/nightly-benchmarks/"
+ "performance-benchmarks-descriptions.md"
)
results = results.format(
latency_tests_markdown_table=latency_md_table,
throughput_tests_markdown_table=throughput_md_table,
serving_tests_markdown_table=serving_md_table,
benchmarking_results_in_json_string=processed_results_json)
benchmarking_results_in_json_string=processed_results_json,
)
f.write(results)
# document benchmarking results in json
with open(results_folder / "benchmark_results.json", "w") as f:
results = latency_results.to_dict(
orient='records') + throughput_results.to_dict(
orient='records') + serving_results.to_dict(orient='records')
results = (
latency_results.to_dict(orient="records")
+ throughput_results.to_dict(orient="records")
+ serving_results.to_dict(orient="records")
)
f.write(json.dumps(results))