Set USE_COMPILE=0 in RTX 5090 runner to bypass Triton gcc dependency

runners/RTX_5090_BLACKWELL_PRETRAIN.txt

@@ -1,26 +1,28 @@
 # ==============================================================================
 # 🚀 ViuAI Sarus-500M — NVIDIA RTX 5090 Blackwell Turbo Pretraining (32GB VRAM)
 # ==============================================================================
-# Instructions for JupyterLab on RTX 5090 (Blackwell sm_120):
+# Instructions:
+# Open JupyterLab on your RTX 5090 pod, paste this cell, and run (Shift + Enter).
 # 
-# 📌 CELL 1: Run once to install CUDA 12.8 PyTorch with native sm_120 Blackwell support.
-# ------------------------------------------------------------------------------
-# !pip install --force-reinstall --no-deps -q torch --index-url https://download.pytorch.org/whl/cu128
-# !pip install -q transformers datasets tokenizers huggingface_hub numpy hf_transfer
-# 
-# 📌 CELL 2: Run to launch continuation pretraining to 60,000 steps!
-# ------------------------------------------------------------------------------
+# ✅ Blackwell sm_120 Ready: Runs natively via PyTorch cu128 + Native BF16 SDPA
+# ✅ No C Compiler Needed: USE_COMPILE=0 avoids Triton gcc dependencies
+# ✅ Auto-resumes from Step 42,750 (already on disk, no re-downloading!)
+# ✅ Streams 200 shards (10B tokens) from ViuAI/viuai-500m-data
+# ==============================================================================
 
 import os, sys, shutil
 import torch
 from huggingface_hub import HfApi, hf_hub_download
 
-# Real-time unbuffered logging, Rust-accelerated downloads & Blackwell compatibility
+# Real-time unbuffered logging & Blackwell configuration
 os.environ["PYTHONUNBUFFERED"] = "1"
 os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 os.environ["TORCH_CUDA_ARCH_LIST"] = "12.0;9.0+PTX"
 
+# Disable torch.compile to bypass missing C compiler (gcc) — uses native high-speed BF16 SDPA
+os.environ["USE_COMPILE"] = "0"
+
 HF_TOKEN = os.environ.get("HF_TOKEN") or ''.join(['hf_','ssyCVhuny','XxjGdqKp','VLPpkmWK','FrrMOIFbg'])
 os.environ["HF_TOKEN"] = HF_TOKEN