e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import datetime
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import json
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import os
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from pathlib import Path
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import pandas as pd
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from tabulate import tabulate
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results_folder = Path("results/")
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# serving results and the keys that will be printed into markdown
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serving_results = []
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serving_column_mapping = {
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"test_name": "Test name",
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"gpu_type": "GPU",
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"completed": "Successful req.",
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"request_throughput": "Tput (req/s)",
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"mean_ttft_ms": "Mean TTFT (ms)",
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"std_ttft_ms": "Std TTFT (ms)",
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"median_ttft_ms": "Median TTFT (ms)",
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"mean_itl_ms": "Mean ITL (ms)",
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"std_itl_ms": "Std ITL (ms)",
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"median_itl_ms": "Median ITL (ms)",
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"mean_tpot_ms": "Mean TPOT (ms)",
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"std_tpot_ms": "Std TPOT (ms)",
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"median_tpot_ms": "Median TPOT (ms)",
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"total_token_throughput": "Total Token Tput (tok/s)",
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"output_throughput": "Output Tput (tok/s)",
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"total_input_tokens": "Total input tokens",
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"total_output_tokens": "Total output tokens",
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"engine": "Engine",
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}
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if __name__ == "__main__":
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# collect results
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for test_file in results_folder.glob("*.json"):
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with open(test_file) as f:
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raw_result = json.loads(f.read())
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# attach the benchmarking command to raw_result
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with open(test_file.with_suffix(".commands")) as f:
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command = json.loads(f.read())
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raw_result.update(command)
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# update the test name of this result
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raw_result.update({"test_name": test_file.stem})
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# add the result to raw_result
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serving_results.append(raw_result)
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continue
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serving_results = pd.DataFrame.from_dict(serving_results)
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if not serving_results.empty:
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serving_results = serving_results[list(
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serving_column_mapping.keys())].rename(
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columns=serving_column_mapping)
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serving_md_table_with_headers = tabulate(serving_results,
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headers='keys',
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tablefmt='pipe',
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showindex=False)
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# remove the first line of header
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serving_md_table_lines = serving_md_table_with_headers.split('\n')
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serving_md_table_without_header = '\n'.join(serving_md_table_lines[2:])
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prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
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# document benchmarking results in markdown
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with open(results_folder / f"{prefix}_nightly_results.md", "w") as f:
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# document results with header.
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# for those who wants to reproduce our benchmark.
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f.write(serving_md_table_with_headers)
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f.write('\n')
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# document benchmarking results in json
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with open(results_folder / f"{prefix}_nightly_results.json", "w") as f:
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results = serving_results.to_dict(orient='records')
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f.write(json.dumps(results))
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