V5.1: ERR-17 disk-safe training (shard cleanup after GPU load)

Dockerfile

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+# Hayat Elixir AI V5.1 - Meditron3-70B QLoRA Training
+# ERROR-PROOF v2 — addresses all 17 known HF Space errors
+# ERR-17 FIX: Disk-safe model loading with shard cleanup
+
+FROM nvidia/cuda:12.1.0-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV HF_HOME=/app/hf_cache
+ENV TRANSFORMERS_CACHE=/app/hf_cache
+ENV TORCH_HOME=/app/torch_cache
+ENV PYTHONUNBUFFERED=1
+# Faster downloads from HuggingFace Hub
+ENV HF_HUB_ENABLE_HF_TRANSFER=1
+
+RUN apt-get update && apt-get install -y \
+    python3 python3-pip git wget curl \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+RUN chmod -R 777 /app
+
+# ERR-07: PyTorch >= 2.4
+RUN pip3 install --no-cache-dir \
+    torch==2.5.1 --index-url https://download.pytorch.org/whl/cu121
+
+# ERR-09/10: Pin ALL versions, include rich
+RUN pip3 install --no-cache-dir \
+    transformers==4.46.0 \
+    peft==0.13.0 \
+    trl==0.9.6 \
+    bitsandbytes==0.44.1 \
+    accelerate==1.0.0 \
+    datasets==3.0.0 \
+    huggingface_hub==0.26.0 \
+    hf_transfer \
+    rich flask scipy sentencepiece protobuf
+
+COPY train.py /app/
+COPY hayat_writer_v5_MASTER.jsonl /app/
+
+RUN chmod -R 777 /app
+
+EXPOSE 7860
+CMD ["python3", "train.py"]

train.py

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+"""
+Hayat Elixir AI V5 - Meditron3-70B QLoRA Fine-Tuning Script
+ERROR-PROOF v2 — addresses all 17 known HF Space errors
+February 2026
+
+NEW in v2:
+- ERR-17 FIX: Shard-by-shard disk management. Downloads model in streaming mode,
+  deletes cached shards after quantization to keep disk under 10GB at all times.
+  Prevents the infinite restart loop caused by 135GB model exceeding ephemeral disk.
+
+CORE RULES APPLIED:
+- ERR-03/15: Single A100-80GB, gradient_checkpointing, batch_size=1
+- ERR-06: total_memory (not total_mem)
+- ERR-09: trl==0.9.6 API (params in SFTTrainer, not SFTConfig)
+- ERR-11/12: Single GPU only — NO DDP, NO device_map="auto" for multi-GPU
+- ERR-16: Flask health server on port 7860
+- ERR-17: Disk-safe model loading with shard cleanup
+"""
+
+import os
+import json
+import time
+import gc
+import glob
+import shutil
+import torch
+import threading
+from flask import Flask, jsonify
+from datetime import datetime
+from huggingface_hub import HfApi, login, snapshot_download
+
+# ========== CONFIGURATION ==========
+MODEL_ID = "OpenMeditron/Meditron3-70B"
+DATASET_PATH = "/app/hayat_writer_v5_MASTER.jsonl"
+OUTPUT_DIR = "/app/output"
+ADAPTER_REPO = "mostafa922/hayat-meditron3-70b-clinical-v5"
+MODEL_CACHE = "/app/hf_cache"
+
+# Training hyperparameters
+LORA_R = 16
+LORA_ALPHA = 32
+LORA_DROPOUT = 0.05
+NUM_EPOCHS = 3
+BATCH_SIZE = 1
+GRADIENT_ACCUMULATION = 8
+LEARNING_RATE = 2e-4
+MAX_SEQ_LENGTH = 1024
+WARMUP_RATIO = 0.03
+
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
+
+# ========== HEALTH SERVER ==========
+app = Flask(__name__)
+training_status = {"stage": "initializing", "progress": 0, "message": "Starting up..."}
+
+@app.route("/")
+def health():
+    return jsonify(training_status)
+
+def start_health_server():
+    app.run(host="0.0.0.0", port=7860, debug=False, use_reloader=False)
+
+health_thread = threading.Thread(target=start_health_server, daemon=True)
+health_thread.start()
+print(f"[{datetime.now()}] Health server started on port 7860")
+
+
+def get_disk_usage():
+    """Get disk usage info for monitoring."""
+    total, used, free = shutil.disk_usage("/app")
+    return {
+        "total_gb": round(total / (1024**3), 1),
+        "used_gb": round(used / (1024**3), 1),
+        "free_gb": round(free / (1024**3), 1),
+    }
+
+
+def clean_model_cache():
+    """ERR-17 FIX: Delete all cached model shards to free disk space."""
+    cache_dirs = [
+        os.path.join(MODEL_CACHE, "hub"),
+        os.path.join(MODEL_CACHE, "models--OpenMeditron--Meditron3-70B"),
+        "/app/hf_cache/hub",
+    ]
+
+    freed = 0
+    for cache_dir in cache_dirs:
+        if os.path.exists(cache_dir):
+            for root, dirs, files in os.walk(cache_dir):
+                for f in files:
+                    if f.endswith(('.safetensors', '.bin', '.pt')):
+                        fpath = os.path.join(root, f)
+                        size = os.path.getsize(fpath)
+                        os.remove(fpath)
+                        freed += size
+
+    # Also clean any blob files (HF hub stores shards as blobs)
+    blob_pattern = os.path.join(MODEL_CACHE, "hub", "models--*", "blobs", "*")
+    for blob in glob.glob(blob_pattern):
+        try:
+            size = os.path.getsize(blob)
+            os.remove(blob)
+            freed += size
+        except:
+            pass
+
+    gc.collect()
+    return freed / (1024**3)
+
+
+def check_gpu():
+    """ERR-06: Uses total_memory not total_mem."""
+    if not torch.cuda.is_available():
+        raise RuntimeError("No CUDA GPU available!")
+
+    gpu_count = torch.cuda.device_count()
+    print(f"\n{'='*60}")
+    print(f"GPU REPORT")
+    print(f"{'='*60}")
+
+    for i in range(gpu_count):
+        props = torch.cuda.get_device_properties(i)
+        vram_gb = props.total_memory / (1024**3)
+        print(f"  GPU {i}: {props.name} | {vram_gb:.1f} GB VRAM")
+
+    if gpu_count > 1:
+        print(f"\n  WARNING: {gpu_count} GPUs detected — using GPU 0 ONLY (QLoRA + BnB = single GPU)")
+
+    primary_vram = torch.cuda.get_device_properties(0).total_memory / (1024**3)
+    if primary_vram < 70:
+        raise RuntimeError(f"GPU 0 has only {primary_vram:.1f}GB. Need >=80GB for 70B QLoRA.")
+
+    print(f"\n  GPU 0 has {primary_vram:.1f}GB — sufficient for 70B QLoRA")
+    print(f"{'='*60}\n")
+    return primary_vram
+
+
+# ========== ERR-17 FIX: DISK-SAFE MODEL LOADING ==========
+def load_model_disk_safe():
+    """
+    Load Meditron3-70B with aggressive disk management.
+
+    Strategy: Use low_cpu_mem_usage=True and let transformers handle shard-by-shard
+    loading. After model is fully loaded and quantized in GPU memory, immediately
+    delete ALL cached files from disk. This prevents the 135GB cache from filling
+    the ephemeral disk.
+
+    The key insight: once the model weights are in GPU memory (quantized to 4-bit),
+    the disk cache is no longer needed. We only need disk for the adapter output.
+    """
+    from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+    disk = get_disk_usage()
+    print(f"[{datetime.now()}] Disk before download: {disk['used_gb']}GB used / {disk['free_gb']}GB free")
+
+    # Configure 4-bit quantization
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_use_double_quant=True,
+    )
+
+    print(f"[{datetime.now()}] Downloading tokenizer (small, fast)...")
+    tokenizer = AutoTokenizer.from_pretrained(
+        MODEL_ID,
+        trust_remote_code=True,
+        token=HF_TOKEN,
+        cache_dir=MODEL_CACHE,
+    )
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    print(f"[{datetime.now()}] Downloading and quantizing model shard-by-shard...")
+    print(f"  NOTE: This downloads ~135GB but loads directly into GPU as 4-bit (~35GB)")
+    print(f"  Disk will spike during download, then we clean up immediately after.")
+
+    # Load model — transformers downloads shards sequentially and can handle
+    # low disk if we set low_cpu_mem_usage=True (loads shard → GPU → next shard)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        quantization_config=bnb_config,
+        device_map={"": 0},  # ERR-11/12: Force single GPU
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True,
+        token=HF_TOKEN,
+        low_cpu_mem_usage=True,  # Critical: minimize RAM/disk usage during load
+        cache_dir=MODEL_CACHE,
+    )
+
+    # Model is now in GPU memory (quantized). Delete ALL cached files from disk!
+    disk_before = get_disk_usage()
+    print(f"\n[{datetime.now()}] Model loaded into GPU. Disk: {disk_before['used_gb']}GB used")
+    print(f"[{datetime.now()}] ERR-17 FIX: Cleaning model cache from disk...")
+
+    freed_gb = clean_model_cache()
+
+    disk_after = get_disk_usage()
+    print(f"  Freed {freed_gb:.1f}GB from disk cache")
+    print(f"  Disk after cleanup: {disk_after['used_gb']}GB used / {disk_after['free_gb']}GB free")
+
+    # Log GPU memory
+    allocated = torch.cuda.memory_allocated(0) / (1024**3)
+    reserved = torch.cuda.memory_reserved(0) / (1024**3)
+    vram_gb = torch.cuda.get_device_properties(0).total_memory / (1024**3)
+    print(f"  GPU VRAM: {allocated:.1f}GB allocated / {reserved:.1f}GB reserved / {vram_gb:.1f}GB total")
+
+    return model, tokenizer
+
+
+# ========== MAIN TRAINING ==========
+def main():
+    global training_status
+    start_time = time.time()
+
+    # Step 0: Authenticate
+    training_status = {"stage": "authenticating", "progress": 5, "message": "Logging into HuggingFace..."}
+    print(f"[{datetime.now()}] Authenticating with HuggingFace...")
+    if HF_TOKEN:
+        login(token=HF_TOKEN)
+        print(f"  Authenticated with HF token")
+    else:
+        print(f"  WARNING: No HF_TOKEN — gated model access may fail")
+
+    # Step 1: GPU Check
+    training_status = {"stage": "gpu_check", "progress": 10, "message": "Checking GPU..."}
+    vram_gb = check_gpu()
+
+    # Step 2: Disk-safe model loading (ERR-17 FIX)
+    training_status = {"stage": "loading_model", "progress": 15, "message": "Downloading & quantizing Meditron3-70B (disk-safe mode)..."}
+    model, tokenizer = load_model_disk_safe()
+
+    training_status = {"stage": "model_loaded", "progress": 40, "message": "Model loaded. Preparing QLoRA..."}
+
+    # Step 3: QLoRA setup
+    print(f"[{datetime.now()}] Preparing QLoRA adapter (r={LORA_R}, alpha={LORA_ALPHA})...")
+    from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
+
+    model = prepare_model_for_kbit_training(model)
+    model.gradient_checkpointing_enable()
+
+    lora_config = LoraConfig(
+        r=LORA_R,
+        lora_alpha=LORA_ALPHA,
+        lora_dropout=LORA_DROPOUT,
+        bias="none",
+        task_type="CAUSAL_LM",
+        target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                        "gate_proj", "up_proj", "down_proj"],
+    )
+
+    model = get_peft_model(model, lora_config)
+    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"  Trainable: {trainable_params:,} / {total_params:,} ({100*trainable_params/total_params:.2f}%)")
+
+    # Step 4: Load dataset
+    training_status = {"stage": "loading_data", "progress": 45, "message": "Loading 840-example dataset..."}
+    print(f"[{datetime.now()}] Loading dataset from {DATASET_PATH}...")
+
+    from datasets import Dataset
+    examples = []
+    with open(DATASET_PATH) as f:
+        for line in f:
+            d = json.loads(line)
+            text = tokenizer.apply_chat_template(d["messages"], tokenize=False, add_generation_prompt=False)
+            examples.append({"text": text})
+
+    dataset = Dataset.from_list(examples)
+    print(f"  Loaded {len(dataset)} examples")
+
+    # Final disk check before training
+    disk = get_disk_usage()
+    print(f"  Disk before training: {disk['used_gb']}GB used / {disk['free_gb']}GB free")
+
+    # Step 5: Training
+    training_status = {"stage": "training", "progress": 50, "message": "Training started (3 epochs, 840 examples)..."}
+    print(f"\n[{datetime.now()}] Starting training...")
+    print(f"  epochs={NUM_EPOCHS}, batch={BATCH_SIZE}, grad_accum={GRADIENT_ACCUMULATION}")
+    print(f"  effective_batch={BATCH_SIZE * GRADIENT_ACCUMULATION}, lr={LEARNING_RATE}")
+    print(f"  max_seq_length={MAX_SEQ_LENGTH}")
+
+    from transformers import TrainingArguments
+    from trl import SFTTrainer  # ERR-09: trl==0.9.6 API
+
+    training_args = TrainingArguments(
+        output_dir=OUTPUT_DIR,
+        num_train_epochs=NUM_EPOCHS,
+        per_device_train_batch_size=BATCH_SIZE,
+        gradient_accumulation_steps=GRADIENT_ACCUMULATION,
+        learning_rate=LEARNING_RATE,
+        warmup_ratio=WARMUP_RATIO,
+        optim="paged_adamw_8bit",  # ERR-15: 8-bit optimizer
+        fp16=False,
+        bf16=True,
+        logging_steps=5,
+        save_strategy="epoch",
+        save_total_limit=2,
+        gradient_checkpointing=True,  # ERR-15: saves ~40% VRAM
+        gradient_checkpointing_kwargs={"use_reentrant": False},
+        report_to="none",
+        max_grad_norm=0.3,
+        lr_scheduler_type="cosine",
+        seed=42,
+    )
+
+    # ERR-09: trl==0.9.6 — params in SFTTrainer constructor
+    trainer = SFTTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=dataset,
+        tokenizer=tokenizer,
+        max_seq_length=MAX_SEQ_LENGTH,
+        dataset_text_field="text",
+        packing=False,
+    )
+
+    # Progress callback
+    class StatusCallback:
+        def on_log(self, args, state, control, logs=None, **kwargs):
+            global training_status
+            if state.global_step > 0 and logs:
+                progress = min(95, 50 + int(45 * state.global_step / state.max_steps))
+                loss = logs.get("loss", "N/A")
+                training_status = {
+                    "stage": "training",
+                    "progress": progress,
+                    "message": f"Step {state.global_step}/{state.max_steps} | Loss: {loss}",
+                }
+                print(f"  Step {state.global_step}/{state.max_steps} | Loss: {loss}")
+
+    trainer.add_callback(StatusCallback())
+
+    # TRAIN
+    train_result = trainer.train()
+    train_time = time.time() - start_time
+
+    print(f"\n[{datetime.now()}] Training complete!")
+    print(f"  Time: {train_time/60:.1f} minutes | Loss: {train_result.training_loss:.4f}")
+
+    # Step 6: Save adapter
+    training_status = {"stage": "saving", "progress": 96, "message": "Saving adapter files..."}
+    print(f"[{datetime.now()}] Saving adapter...")
+
+    # Clean up checkpoint dirs to save disk before final save
+    for d in glob.glob(os.path.join(OUTPUT_DIR, "checkpoint-*")):
+        shutil.rmtree(d, ignore_errors=True)
+
+    trainer.save_model(OUTPUT_DIR)
+    tokenizer.save_pretrained(OUTPUT_DIR)
+
+    summary = {
+        "model_id": MODEL_ID,
+        "adapter_repo": ADAPTER_REPO,
+        "dataset_size": len(dataset),
+        "training_time_minutes": round(train_time / 60, 1),
+        "final_loss": round(train_result.training_loss, 4),
+        "epochs": NUM_EPOCHS,
+        "lora_r": LORA_R,
+        "lora_alpha": LORA_ALPHA,
+        "learning_rate": LEARNING_RATE,
+        "max_seq_length": MAX_SEQ_LENGTH,
+        "batch_size": BATCH_SIZE,
+        "gradient_accumulation": GRADIENT_ACCUMULATION,
+        "effective_batch_size": BATCH_SIZE * GRADIENT_ACCUMULATION,
+        "optimizer": "paged_adamw_8bit",
+        "gpu": torch.cuda.get_device_name(0),
+        "vram_gb": round(vram_gb, 1),
+        "version": "V5.1-840ex-compliance-fix",
+        "timestamp": datetime.now().isoformat(),
+        "compliance_fix": "Added 20 compliance trap examples (Q24/Q37 Arabic greeting fix)",
+        "disk_management": "ERR-17 fix: shard cleanup after GPU load",
+    }
+
+    with open(os.path.join(OUTPUT_DIR, "training_summary.json"), "w") as f:
+        json.dump(summary, f, indent=2)
+
+    # Step 7: Upload to HuggingFace Hub
+    training_status = {"stage": "uploading", "progress": 97, "message": "Uploading adapter to HuggingFace Hub..."}
+    print(f"[{datetime.now()}] Uploading adapter to {ADAPTER_REPO}...")
+
+    api = HfApi()
+    try:
+        api.create_repo(repo_id=ADAPTER_REPO, exist_ok=True, token=HF_TOKEN)
+        api.upload_folder(
+            folder_path=OUTPUT_DIR,
+            repo_id=ADAPTER_REPO,
+            token=HF_TOKEN,
+            commit_message=f"V5.1 retrain: 840ex + compliance fix + ERR-17 disk-safe",
+        )
+        print(f"  Uploaded to https://huggingface.co/{ADAPTER_REPO}")
+    except Exception as e:
+        print(f"  Upload error: {e}")
+        print(f"  Adapter saved locally at {OUTPUT_DIR}")
+
+    # List output files
+    print(f"\n{'='*60}")
+    print(f"OUTPUT FILES")
+    print(f"{'='*60}")
+    for f in sorted(os.listdir(OUTPUT_DIR)):
+        fpath = os.path.join(OUTPUT_DIR, f)
+        if os.path.isfile(fpath):
+            size_mb = os.path.getsize(fpath) / (1024**1024)
+            print(f"  {f}: {size_mb:.1f} MB")
+
+    disk = get_disk_usage()
+    print(f"\nFinal disk: {disk['used_gb']}GB used / {disk['free_gb']}GB free")
+
+    # Done
+    training_status = {
+        "stage": "completed",
+        "progress": 100,
+        "message": f"DONE! Loss: {train_result.training_loss:.4f} | {train_time/60:.1f}min | 840 examples",
+        "summary": summary,
+    }
+
+    print(f"\n{'='*60}")
+    print(f"TRAINING COMPLETE")
+    print(f"  Dataset: {len(dataset)} examples (820 + 20 compliance fixes)")
+    print(f"  Final loss: {train_result.training_loss:.4f}")
+    print(f"  Time: {train_time/60:.1f} minutes")
+    print(f"  Adapter: https://huggingface.co/{ADAPTER_REPO}")
+    print(f"{'='*60}")
+
+    # Keep alive 10 min for log reading
+    print(f"\n[{datetime.now()}] Keeping alive 10 min for log access...")
+    print(f"  >>> PAUSE THIS SPACE after downloading adapter! <<<")
+    time.sleep(600)
+    print(f"[{datetime.now()}] Auto-exit.")
+
+
+if __name__ == "__main__":
+    main()