feat(streaming): full-matrix SLURM submitter + results summarizer

slurm/run_streaming_matrix.sh fans the benchmark matrix (sources {hub,bucket,
warmed_bucket} x modes {single,sarm} x decode {cpu,cuda}) out as isolated single-GPU
SLURM jobs, so an OOM in one config is contained and reported per-job by SLURM. Worker
count and shuffle buffer are bounded (lower for cuda, which holds a CUDA context + NVDEC
session per worker) to avoid host/VRAM OOM. Source/mode/decode/workers/buffer/account/
partition are env-overridable; SOURCES/MODES/DECODES select subsets.

benchmarks/streaming/summarize_results.py collapses the per-run JSONs into one comparison
table + summary.csv (frames/s/node, first-batch + p50/p95/p99 latency, cache hit-rate).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

benchmarks/streaming/summarize_results.py

@@ -0,0 +1,79 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Collapse a directory of benchmark JSON results into one comparison table (and a combined CSV).
+
+python benchmarks/streaming/summarize_results.py benchmarks/streaming/results
+"""
+
+import csv
+import json
+import sys
+from pathlib import Path
+
+COLUMNS = [
+    ("source", "source"),
+    ("mode", "mode"),
+    ("video_decode_device", "decode"),
+    ("num_workers", "workers"),
+    ("batch_size", "bs"),
+    ("frames_per_s_node", "frames/s/node"),
+    ("first_batch_latency_s", "first_batch_s"),
+    ("p50_sample_latency_ms", "p50_ms"),
+    ("p95_sample_latency_ms", "p95_ms"),
+    ("p99_sample_latency_ms", "p99_ms"),
+]
+
+
+def main() -> None:
+    results_dir = Path(sys.argv[1] if len(sys.argv) > 1 else "benchmarks/streaming/results")
+    files = sorted(results_dir.rglob("*.json"))
+    if not files:
+        print(f"No JSON results under {results_dir}")
+        return
+
+    rows = []
+    for f in files:
+        d = json.loads(f.read_text())
+        d["hit_rate"] = d.get("video_decoder_cache", {}).get("hit_rate")
+        rows.append(d)
+
+    rows.sort(key=lambda r: (r.get("source", ""), r.get("mode", ""), r.get("video_decode_device", "")))
+
+    headers = [label for _, label in COLUMNS] + ["cache_hit_rate"]
+    widths = {h: len(h) for h in headers}
+    table = []
+    for r in rows:
+        row = {label: r.get(key, "") for key, label in COLUMNS}
+        row["cache_hit_rate"] = r.get("hit_rate", "")
+        table.append(row)
+        for h in headers:
+            widths[h] = max(widths[h], len(str(row[h])))
+
+    line = "  ".join(h.ljust(widths[h]) for h in headers)
+    print(line)
+    print("  ".join("-" * widths[h] for h in headers))
+    for row in table:
+        print("  ".join(str(row[h]).ljust(widths[h]) for h in headers))
+
+    combined = results_dir / "summary.csv"
+    with open(combined, "w", newline="") as fh:
+        writer = csv.DictWriter(fh, fieldnames=headers)
+        writer.writeheader()
+        writer.writerows(table)
+    print(f"\nWrote {combined}")
+
+
+if __name__ == "__main__":
+    main()