Upload source/scrape_and_upload.py with huggingface_hub

source/scrape_and_upload.py

@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+"""Download data from HuggingFace datasets and upload to OpenTransformer/web-crawl-2026
+V2: replaced broken sources, added looping and resume logic"""
+import os
+import json
+import gzip
+import time
+import traceback
+from datasets import load_dataset
+from huggingface_hub import HfApi, login
+
+HF_TOKEN = "HF_TOKEN_REDACTED"
+TARGET_REPO = "OpenTransformer/web-crawl-2026"
+OUTPUT_DIR = "/workspace/scraped_data"
+CHUNK_SIZE = 50000
+STATE_FILE = "/workspace/scrape_state.json"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+login(token=HF_TOKEN)
+api = HfApi(token=HF_TOKEN)
+
+# Sources that work with streaming and don't use deprecated dataset scripts
+SOURCES = [
+    # FineWeb already done (298 chunks), but FineWeb-Edu is separate
+    ("HuggingFaceFW/fineweb-edu", "sample-10BT", "train", "text"),
+    ("allenai/c4", "en", "train", "text"),
+    ("cerebras/SlimPajama-627B", None, "train", "text"),
+    ("uonlp/CulturaX", "en", "train", "text"),
+]
+
+def load_state():
+    if os.path.exists(STATE_FILE):
+        with open(STATE_FILE) as f:
+            return json.load(f)
+    return {}
+
+def save_state(state):
+    with open(STATE_FILE, "w") as f:
+        json.dump(state, f)
+
+def upload_chunk(filepath, remote_name):
+    for attempt in range(3):
+        try:
+            api.upload_file(
+                path_or_fileobj=filepath,
+                path_in_repo="data/" + remote_name,
+                repo_id=TARGET_REPO,
+                repo_type="dataset",
+            )
+            print("  Uploaded " + remote_name, flush=True)
+            return True
+        except Exception as e:
+            print("  Upload attempt %d failed: %s" % (attempt+1, e), flush=True)
+            time.sleep(10 * (attempt+1))
+    return False
+
+def process_source(name, config, split, text_field):
+    sep = "=" * 60
+    print("\n" + sep, flush=True)
+    print("Source: %s (%s)" % (name, config or "default"), flush=True)
+    print(sep, flush=True)
+
+    state = load_state()
+    source_tag = name.replace("/", "_")
+    if config:
+        source_tag += "_" + config.replace("-", "_")
+    state_key = source_tag
+
+    # Resume from last chunk
+    start_chunk = state.get(state_key, {}).get("next_chunk", 0)
+    skip_rows = start_chunk * CHUNK_SIZE
+    print("  Resuming from chunk %d (skipping %d rows)" % (start_chunk, skip_rows), flush=True)
+
+    try:
+        if config:
+            ds = load_dataset(name, config, split=split, streaming=True)
+        else:
+            ds = load_dataset(name, split=split, streaming=True)
+    except Exception as e:
+        print("  Failed to load: %s" % e, flush=True)
+        return False
+
+    batch = []
+    chunk_num = start_chunk
+    total_rows = 0
+    skipped = 0
+
+    for example in ds:
+        if skipped < skip_rows:
+            skipped += 1
+            if skipped % 500000 == 0:
+                print("  Skipping... %d/%d" % (skipped, skip_rows), flush=True)
+            continue
+
+        text = example.get(text_field) or example.get("text") or example.get("content") or ""
+        if len(text) < 100:
+            continue
+
+        row = {
+            "text": text,
+            "source": name,
+            "url": example.get("url", ""),
+        }
+        batch.append(row)
+        total_rows += 1
+
+        if len(batch) >= CHUNK_SIZE:
+            chunk_name = "%s_chunk%04d.jsonl.gz" % (source_tag, chunk_num)
+            chunk_path = os.path.join(OUTPUT_DIR, chunk_name)
+
+            with gzip.open(chunk_path, "wt", encoding="utf-8") as f:
+                for item in batch:
+                    f.write(json.dumps(item, ensure_ascii=False) + "\n")
+
+            print("  Chunk %d: %s rows total" % (chunk_num, "{:,}".format(total_rows + skip_rows)), flush=True)
+
+            if upload_chunk(chunk_path, chunk_name):
+                os.remove(chunk_path)
+                chunk_num += 1
+                state[state_key] = {"next_chunk": chunk_num}
+                save_state(state)
+            else:
+                print("  Upload failed, will retry next run", flush=True)
+                os.remove(chunk_path)
+                return False
+
+            batch = []
+
+    # Final partial batch
+    if batch:
+        chunk_name = "%s_chunk%04d.jsonl.gz" % (source_tag, chunk_num)
+        chunk_path = os.path.join(OUTPUT_DIR, chunk_name)
+        with gzip.open(chunk_path, "wt", encoding="utf-8") as f:
+            for item in batch:
+                f.write(json.dumps(item, ensure_ascii=False) + "\n")
+        if upload_chunk(chunk_path, chunk_name):
+            os.remove(chunk_path)
+            chunk_num += 1
+            state[state_key] = {"next_chunk": chunk_num, "done": True}
+            save_state(state)
+
+    print("  Done: %s rows from %s" % ("{:,}".format(total_rows + skip_rows), name), flush=True)
+    return False
+
+if __name__ == "__main__":
+    print("Web Crawl Data Collector V2", flush=True)
+    print("Target: %s" % TARGET_REPO, flush=True)
+    start = time.time()
+
+    for name, config, split, text_field in SOURCES:
+        state = load_state()
+        source_tag = name.replace("/", "_")
+        if config:
+            source_tag += "_" + config.replace("-", "_")
+        if state.get(source_tag, {}).get("done"):
+            print("Skipping %s (already done)" % name, flush=True)
+            continue
+        try:
+            process_source(name, config, split, text_field)
+        except Exception as e:
+            print("Error processing %s: %s" % (name, e), flush=True)
+            traceback.print_exc()
+            continue
+
+    elapsed = time.time() - start
+    print("\nFinished in %.1f hours" % (elapsed/3600), flush=True)