Upload source/upload_crawler_source.py with huggingface_hub

source/upload_crawler_source.py

@@ -0,0 +1,148 @@
+from huggingface_hub import HfApi
+import tempfile, os
+
+api = HfApi(token='HF_TOKEN_REDACTED')
+REPO = 'OpenTransformer/web-crawl-2026'
+
+# Upload main.rs
+print('Uploading main.rs...')
+api.upload_file(
+    path_or_fileobj='/workspace/rust_crawler/src/main.rs',
+    path_in_repo='crawler/rust/src/main.rs',
+    repo_id=REPO,
+    repo_type='dataset',
+    commit_message='Add Rust web crawler source (v3, 150-300 docs/s)'
+)
+
+# Upload Cargo.toml
+print('Uploading Cargo.toml...')
+api.upload_file(
+    path_or_fileobj='/workspace/rust_crawler/Cargo.toml',
+    path_in_repo='crawler/rust/Cargo.toml',
+    repo_id=REPO,
+    repo_type='dataset',
+    commit_message='Add Rust crawler Cargo.toml'
+)
+
+# Create and upload README
+readme = '''---
+license: apache-2.0
+task_categories:
+  - text-generation
+language:
+  - en
+tags:
+  - web-crawl
+  - pretraining
+  - nlp
+  - text-corpus
+pretty_name: Web Crawl 2026
+size_categories:
+  - 10B<n<100B
+---
+
+# Web Crawl 2026
+
+A large-scale web crawl dataset for language model pretraining, collected by the OpenTransformer project.
+
+## Dataset Description
+
+This dataset contains text extracted from web pages crawled directly from the internet using custom high-throughput crawlers. All data is freshly scraped — **not** re-uploaded from existing datasets like FineWeb or C4.
+
+### Data Format
+
+Each record is a JSON line with fields:
+-  — extracted text content (200–200,000 chars)
+-  — source URL
+-  — source domain
+-  — crawl timestamp (ISO 8601)
+-  — crawler identifier (, , )
+
+### Collection Methods
+
+Three crawlers run in parallel on a Vast.ai GPU box (Titan Xp, /usr/bin/bash.06/hr):
+
+| Crawler | Language | Throughput | 1.2GB Chunk Time | Architecture |
+|---------|----------|------------|-------------------|-------------|
+| **crawl_rust** | Rust | **150–300 docs/s** | **5–6 min** | 500 async workers, tokio |
+| crawl_go | Go | 11 docs/s | ~2 hrs | 150 goroutines |
+| crawl_v5.py | Python | 0.8 docs/s | ~25 hrs | 20 async workers |
+
+The Rust crawler is **27x faster than Go** and **375x faster than Python**.
+
+### Rust Crawler Architecture
+
+Source: 
+
+**Key design decisions:**
+- **500 concurrent async workers** via tokio + semaphore-based backpressure
+- **Background queue refiller** — seed fetching runs in a separate task, never blocks crawling
+- **Pre-generated seed file** — 593K URLs from Common Crawl index (12 crawl versions × 20 TLD patterns)
+- **Link discovery** — extracts up to 50 links per crawled page, shuffled for domain diversity
+- **Content dedup** — MD5 hash of first 500 chars, stored in DashMap (lock-free concurrent hashmap)
+- **Domain throttling** — max 1000 pages per domain to ensure diversity
+- **Streaming gzip** — writes compressed JSONL chunks (~1.2GB raw → ~350MB compressed)
+- **Auto-upload** — each completed chunk is uploaded to HuggingFace Hub via Python subprocess
+
+**Seed sources:**
+1. Common Crawl URL index (CC-MAIN-2024-10 through CC-MAIN-2025-08)
+2. Wikipedia random articles API (20K articles)
+3. Sitemaps from 34 major sites (Reuters, BBC, Nature, StackOverflow, etc.)
+4. Hacker News top/new/best stories
+
+**Performance on Titan Xp box (/usr/bin/bash.06/hr):**
+- Phase 1: 562K seeds loaded in 28 seconds
+- Phase 2: 150–300 docs/s sustained throughput
+- ~1.2GB chunk every 5–6 minutes
+- ~12–15 GB/hour of raw crawled text
+- Cost: ~/usr/bin/bash.004 per GB of crawled text
+
+### Building & Running
+
+
+  stable-x86_64-pc-windows-gnu installed - (timeout reading rustc version)
+
+
+Rust is installed now. Great!
+
+To get started you may need to restart your current shell.
+This would reload its PATH environment variable to include
+Cargo's bin directory (%USERPROFILE%\.cargo\bin).
+
+### Dependencies
+
+- Rust 1.75+
+- Python 3 with  (for upload)
+-  hardcoded (or modify to use env var)
+
+### Quality Filtering
+
+- HTML text extraction via  crate (article/main/body selectors)
+- Minimum 200 chars, maximum 200K chars
+- Content-type filtering (only text/html)
+- URL filtering: blocks social media, login pages, media files, admin pages
+- Deduplication via MD5 content hash
+
+## Intended Use
+
+Pretraining data for the AGILLM-3 language model (698M params, joint AR+SAT architecture).
+
+## License
+
+Apache 2.0
+'''
+
+with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False) as f:
+    f.write(readme)
+    readme_path = f.name
+
+print('Uploading README.md...')
+api.upload_file(
+    path_or_fileobj=readme_path,
+    path_in_repo='README.md',
+    repo_id=REPO,
+    repo_type='dataset',
+    commit_message='Update README with crawler documentation and performance benchmarks'
+)
+os.unlink(readme_path)
+print('All uploads complete!')