fix: Add memory controls to prevent OOM on HF Jobs

- Add pl.Config.set_streaming_chunk_size(1000) for large text docs
- Add chunk_size parameter to polars_to_generator (default: 100)
- Add --chunk-size CLI argument for user tuning
- Update HF Jobs command syntax in docstring

Fixes exit code 137 (OOM) when processing large text documents.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <[email protected]>

long-context-pdfs.py

@@ -21,10 +21,11 @@ Examples:
     # All Latin scripts
     uv run long-context-pdfs.py --lang "*_Latn" --output user/finepdfs-long-context
 
-    # HF Jobs
-    hf jobs run uv --flavor cpu-basic -- run \\
-        https://huggingface.co/.../long-context-pdfs.py \\
-        --lang eng_Latn --output user/finepdfs-eng-long
+    # HF Jobs (memory-efficient with small chunk size)
+    hf jobs uv run \\
+        -s HF_TOKEN \\
+        https://huggingface.co/datasets/uv-scripts/dataset-stats/raw/main/long-context-pdfs.py \\
+        -- --lang eng_Latn --output user/finepdfs-eng-long
 """
 
 import argparse
@@ -32,36 +33,64 @@ import polars as pl
 from datasets import Dataset
 
 
-def polars_to_generator(lf: pl.LazyFrame):
-    """Stream LazyFrame as row generator."""
-    for batch_df in lf.collect_batches():
+def polars_to_generator(lf: pl.LazyFrame, chunk_size: int = 100):
+    """Stream LazyFrame as row generator with controlled memory.
+
+    Args:
+        lf: LazyFrame to stream
+        chunk_size: Rows per batch (lower = less memory). Default 100 for large text docs.
+    """
+    for batch_df in lf.collect_batches(chunk_size=chunk_size):
         yield from batch_df.iter_rows(named=True)
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Extract long-context high-quality PDFs")
-    parser.add_argument("--lang", type=str, default="cym_Latn",
-                        help="Language code or glob pattern (default: cym_Latn, use '*_Latn' for all Latin)")
-    parser.add_argument("--min-tokens", type=int, default=10000,
-                        help="Minimum token count (default: 10000)")
-    parser.add_argument("--min-lid-score", type=float, default=0.8,
-                        help="Minimum language ID score (default: 0.8)")
+    parser = argparse.ArgumentParser(
+        description="Extract long-context high-quality PDFs"
+    )
+    parser.add_argument(
+        "--lang",
+        type=str,
+        default="cym_Latn",
+        help="Language code or glob pattern (default: cym_Latn, use '*_Latn' for all Latin)",
+    )
+    parser.add_argument(
+        "--min-tokens",
+        type=int,
+        default=10000,
+        help="Minimum token count (default: 10000)",
+    )
+    parser.add_argument(
+        "--min-lid-score",
+        type=float,
+        default=0.8,
+        help="Minimum language ID score (default: 0.8)",
+    )
     parser.add_argument("--limit", type=int, help="Limit rows")
     parser.add_argument("--output", type=str, help="Output dataset repo")
     parser.add_argument("--private", action="store_true")
+    parser.add_argument(
+        "--chunk-size",
+        type=int,
+        default=100,
+        help="Rows per batch (lower = less memory, default: 100)",
+    )
 
     args = parser.parse_args()
 
+    # Reduce streaming chunk size for large text documents (default can cause OOM)
+    pl.Config.set_streaming_chunk_size(1000)
+
     source = f"hf://datasets/HuggingFaceFW/finepdfs/data/{args.lang}/train/*.parquet"
 
     print("=" * 60)
     print("Long-Context High-Quality PDF Extraction")
     print("=" * 60)
     print(f"Source: {source}")
-    print(f"Filters:")
+    print("Filters:")
     print(f"  - token_count >= {args.min_tokens}")
     print(f"  - page_average_lid_score >= {args.min_lid_score}")
-    print(f"  - extractor == 'docling'")
+    print("  - extractor == 'docling'")
     if args.limit:
         print(f"  - limit: {args.limit}")
     print("=" * 60)
@@ -74,15 +103,17 @@ def main():
             & (pl.col("page_average_lid_score") >= args.min_lid_score)
             & (pl.col("extractor") == "docling")
         )
-        .select([
-            "id",
-            "url",
-            "text",
-            "language",
-            "token_count",
-            "dump",
-            "page_average_lid_score",
-        ])
+        .select(
+            [
+                "id",
+                "url",
+                "text",
+                "language",
+                "token_count",
+                "dump",
+                "page_average_lid_score",
+            ]
+        )
     )
 
     if args.limit:
@@ -110,21 +141,25 @@ def main():
             & (pl.col("page_average_lid_score") >= args.min_lid_score)
             & (pl.col("extractor") == "docling")
         )
-        .select([
-            "id",
-            "url",
-            "text",
-            "language",
-            "token_count",
-            "dump",
-            "page_average_lid_score",
-        ])
+        .select(
+            [
+                "id",
+                "url",
+                "text",
+                "language",
+                "token_count",
+                "dump",
+                "page_average_lid_score",
+            ]
+        )
     )
     if args.limit:
         lf = lf.limit(args.limit)
 
-    print(f"\nStreaming to dataset...")
-    ds = Dataset.from_generator(lambda: polars_to_generator(lf))
+    print(f"\nStreaming to dataset (chunk_size={args.chunk_size})...")
+    ds = Dataset.from_generator(
+        lambda: polars_to_generator(lf, chunk_size=args.chunk_size)
+    )
     print(f"Created dataset with {len(ds)} rows")
 
     print(f"\nPushing to {args.output}...")