Add version 2.0 with benchmark name, version, and timestamp

README.md

@@ -1,54 +1,54 @@
----
-license: apache-2.0
----
-
-# Preparing OpenMLPerf dataset
-
-To process the semi-raw MLPerf data into the OpenMLPerf dataset, run the following command:
-
-```bash
-# Untar raw files
-
-bzip2 -d semi-raw-mlperf-data.tar.bz2
-tar xvf semi-raw-mlperf-data.tar
-
-# Create a virtual environment
-python -m venv .venv
-
-# Activate the virtual environment
-source .venv/bin/activate
-
-# Install the required packages
-pip install -r requirements.txt
-
-# Run the processing script
-python process.py
-```
-
-The processed dataset will be saved both as `data.json` and `data.parquet` in the `OpenMLPerf-dataset` directory.
-The `data.json` file is a JSON file containing the processed data, while the `data.parquet` file is a Parquet file containing the same data in a more efficient format for storage and processing.
-
-# Preprocessing raw MLPerf results using MLCommons CMX
-
-We preprocess official raw MLPerf data, such as [inference v5.0](https://github.com/mlcommons/inference_results_v5.0),
-into semi-raw format compatible with the `process.py` script, using the [MLCommons CM/CMX automation framework](https://arxiv.org/abs/2406.16791).
-This is done using through the ["import mlperf results"](https://github.com/mlcommons/ck/tree/master/cmx4mlops/repo/flex.task/import-mlperf-results) 
-automation action, which we plan to document in more detail soon.
-
-# License and Copyright
-
-This project is licensed under the [Apache License 2.0](LICENSE.md).
-
-© 2025 FlexAI
-
-Portions of the data were adapted from the following MLCommons repositories, 
-which are also licensed under the Apache 2.0 license:
-
-* [mlcommons@inference_results_v5.0](https://github.com/mlcommons/inference_results_v5.0)
-* [mlcommons@inference_results_v4.1](https://github.com/mlcommons/inference_results_v4.1)
-* [mlcommons@inference_results_v4.0](https://github.com/mlcommons/inference_results_v4.0)
-* [mlcommons@inference_results_v3.1](https://github.com/mlcommons/inference_results_v3.1)
-
-# Authors and maintaners
-
-[Daniel Altunay](https://www.linkedin.com/in/daltunay) and [Grigori Fursin](https://cKnowledge.org/gfursin) (FCS Labs)
+---
+license: apache-2.0
+---
+
+# Preparing OpenMLPerf dataset
+
+To process the semi-raw MLPerf data into the OpenMLPerf dataset, run the following command:
+
+```bash
+# Untar raw files
+
+bzip2 -d semi-raw-mlperf-data.tar.bz2
+tar xvf semi-raw-mlperf-data.tar
+
+# Create a virtual environment
+python -m venv .venv
+
+# Activate the virtual environment
+source .venv/bin/activate
+
+# Install the required packages
+pip install -r requirements.txt
+
+# Run the processing script
+python process.py
+```
+
+The processed dataset will be saved both as `data.json` and `data.parquet` in the `OpenMLPerf-dataset` directory.
+The `data.json` file is a JSON file containing the processed data, while the `data.parquet` file is a Parquet file containing the same data in a more efficient format for storage and processing.
+
+# Preprocessing raw MLPerf results using MLCommons CMX
+
+We preprocess official raw MLPerf data, such as [inference v5.0](https://github.com/mlcommons/inference_results_v5.0),
+into semi-raw format compatible with the `process.py` script, using the [MLCommons CM/CMX automation framework](https://arxiv.org/abs/2406.16791).
+This is done using through the ["import mlperf results"](https://github.com/mlcommons/ck/tree/master/cmx4mlops/repo/flex.task/import-mlperf-results) 
+automation action, which we plan to document in more detail soon.
+
+# License and Copyright
+
+This project is licensed under the [Apache License 2.0](LICENSE.md).
+
+© 2025 FlexAI
+
+Portions of the data were adapted from the following MLCommons repositories, 
+which are also licensed under the Apache 2.0 license:
+
+* [mlcommons@inference_results_v5.0](https://github.com/mlcommons/inference_results_v5.0)
+* [mlcommons@inference_results_v4.1](https://github.com/mlcommons/inference_results_v4.1)
+* [mlcommons@inference_results_v4.0](https://github.com/mlcommons/inference_results_v4.0)
+* [mlcommons@inference_results_v3.1](https://github.com/mlcommons/inference_results_v3.1)
+
+# Authors and maintaners
+
+[Daniel Altunay](https://www.linkedin.com/in/daltunay) and [Grigori Fursin](https://cKnowledge.org/gfursin) (FCS Labs)

data.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:845063b17e66072eb21e17c650f7745b49da8f12eb2e591827c823c041121588
-size 44318
+oid sha256:a2d83fecee4037e9a4e10db98d79096c383319739d9a86b7d5e38b29e0fa054b
+size 44347

processor.py → process.py

@@ -1,4 +1,6 @@
-"""Data processing module for MLPerf benchmark data."""
+"""
+Data processing module for MLPerf benchmark data.
+"""
 
 import glob
 import json
@@ -6,6 +8,7 @@ import logging
 import os
 import re
 from collections import defaultdict
+from datetime import datetime
 
 import polars as pl
 from datasets import Dataset
@@ -135,6 +138,10 @@ def load_raw_data(base_path: str = "semi-raw-mlperf-data") -> pl.DataFrame:
         "system.host_processor_frequency": "system.cpu.frequency",
         "system.host_processor_caches": "system.cpu.caches",
         "system.host_processor_vcpu_count": "system.cpu.vcpu_count",
+        "benchmark_name": "benchmark.name",
+        "benchmark_version": "benchmark.version",
+        "datetime_last_commit": "datetime",
+        "debug_uid": "debug_uid",
     }
 
     for old_name, new_name in rename_map.items():
@@ -183,6 +190,39 @@ def find_similar_configurations(
     return df.filter(mask)
 
 
+def convert_datetime_to_iso(value: str) -> str | None:
+    """Convert datetime string to ISO 8601 format."""
+    if not value or value in ["", "N/A", "null"]:
+        MISSING_VALUES["datetime_values"].add(str(value))
+        return None
+
+    try:
+        # Handle format like "2025/04/03_22:56:53"
+        if "/" in value and "_" in value:
+            # Replace / with - and _ with T for ISO format
+            iso_value = value.replace("/", "-").replace("_", "T")
+            # Validate by parsing
+            datetime.fromisoformat(iso_value)
+            return iso_value
+
+        # Try to parse other common formats and convert to ISO
+        # Add more format patterns as needed
+        for fmt in ["%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y-%m-%dT%H:%M:%S"]:
+            try:
+                dt = datetime.strptime(value, fmt)
+                return dt.isoformat()
+            except ValueError:
+                continue
+
+        # If no format matches, log as missing value
+        MISSING_VALUES["datetime_values"].add(str(value))
+        return None
+
+    except Exception as e:
+        MISSING_VALUES["datetime_values"].add(str(value))
+        return None
+
+
 def convert_memory_to_gb(value: str) -> float | None:
     """Convert memory string to GB."""
     if value is None:
@@ -382,6 +422,17 @@ def normalize_memory_values(df: pl.DataFrame) -> pl.DataFrame:
     )
 
 
+def normalize_datetime_values(df: pl.DataFrame) -> pl.DataFrame:
+    """Convert datetime values to ISO 8601 format."""
+    if "datetime" in df.columns:
+        return df.with_columns(
+            pl.col("datetime")
+            .map_elements(convert_datetime_to_iso, return_dtype=str)
+            .alias("datetime")
+        )
+    return df
+
+
 def add_vendor_columns(df: pl.DataFrame) -> pl.DataFrame:
     """Add vendor columns based on model names."""
     return df.with_columns(
@@ -575,6 +626,7 @@ def process_data(base_path: str = "semi-raw-mlperf-data") -> pl.DataFrame:
         load_raw_data(base_path)
         .pipe(clean_string_values)
         .pipe(normalize_memory_values)
+        .pipe(normalize_datetime_values)
         .pipe(cast_columns)
         .pipe(add_vendor_columns)
         .pipe(normalize_interconnect_values)