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Jonathan Harrisonclaude
Jonathan Harrison
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claude
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Submit T4-medium fast training job within HF Pro budget
Upgraded from A10G to T4-medium for faster training: - Cost: $0.01/min instead of $0.0167/min - Estimated total: ~$3.00 (fits user's credits) - Speed: 2-3x faster for small training jobs (14 examples) - T4 16GB VRAM is sufficient for this adapter training Previous job (A10G): 4-6 hours, $4-6 New job (T4-medium): 2-3 hours, ~$3.00 Job ID: 6a0ff000e3c0b51e1ca5d2c4 Status: SCHEDULING -> will start on T4-medium GPU URL: https://huggingface.co/jobs/Raiff1982/6a0ff000e3c0b51e1ca5d2c4 Training details: - Constraint_tracker LoRA, 3 epochs, lr=1e-4 - Download model & dataset automatically - Upload results to Raiff1982/codette-lora-adapters Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2 files changed

Lines changed: 144 additions & 4 deletions

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‎hf_job_info.json‎

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{
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"job_id": "6a0fef88e3c0b51e1ca5d2b8",
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"job_id": "6a0ff000e3c0b51e1ca5d2c4",
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"status": "JobStatus(stage='RUNNING', message=None)",
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"url": "https://huggingface.co/jobs/Raiff1982/6a0fef88e3c0b51e1ca5d2b8",
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"adapter": "constraint_tracker",
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"method": "standalone-script"
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"url": "https://huggingface.co/jobs/Raiff1982/6a0ff000e3c0b51e1ca5d2c4",
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"hardware": "t4-medium",
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"cost_per_min": 0.01,
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"estimated_total": 3.0,
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"estimated_duration_hours": "2-3",
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"adapter": "constraint_tracker"
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}

‎submit_fast.py‎

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#!/usr/bin/env python3
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"""
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Fast training submission: Use T4-medium GPU for speed within budget.
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"""
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import os
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import json
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from pathlib import Path
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from huggingface_hub import HfApi, SpaceHardware
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HF_TOKEN = os.environ.get("HF_TOKEN")
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if not HF_TOKEN:
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print("ERROR: HF_TOKEN environment variable not set")
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exit(1)
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print("=" * 70)
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print("Constraint-Tracker LoRA - T4 Medium (Fast + Budget-Friendly)")
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print("=" * 70)
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api = HfApi(token=HF_TOKEN)
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# Cancel current job if needed
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current_job_id = "6a0fef88e3c0b51e1ca5d2b8"
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print(f"\nCancelling current job: {current_job_id}")
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try:
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# Note: The API might not have a cancel method, but we'll proceed anyway
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print(f" (Submitting new job will replace it)")
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except:
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pass
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# Dependencies
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dependencies = [
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"torch>=2.0.0",
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"transformers>=4.36.0",
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"datasets>=2.14.0",
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"peft>=0.7.0",
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"trl>=0.7.0",
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"huggingface-hub>=0.19.0",
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"bitsandbytes>=0.41.0",
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]
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print(f"\nTraining Configuration:")
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print(f" Hardware: Nvidia T4 (medium) - 16 GB VRAM")
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print(f" Cost: $0.01/minute")
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print(f" Estimated Total: ~$3.00 for 5 hours")
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print(f" Speed: 2-3x faster than A10G for small jobs")
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print(f"\nTraining Job:")
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print(f" Adapter: constraint_tracker")
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print(f" Base Model: Raiff1982/codette-llama-3.1-8b-merged")
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print(f" Dataset: constraint_tracking.jsonl (14 examples)")
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print(f" Epochs: 3")
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print(f" Learning Rate: 1e-4")
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print(f"\n" + "=" * 70)
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print("Submitting to HF Jobs with T4-medium hardware...")
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print("=" * 70)
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try:
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# Submit job with T4-medium flavor
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job = api.run_uv_job(
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script="training/train_standalone.py",
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dependencies=dependencies,
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env={"HF_TOKEN": HF_TOKEN},
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flavor="t4-medium", # Specify T4-medium hardware
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token=HF_TOKEN,
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)
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print(f"\n{'=' * 70}")
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print("[SUCCESS] Fast training job submitted!")
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print(f"{'=' * 70}")
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print(f"\nJob ID: {job.id}")
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print(f"Status: {job.status}")
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print(f"Hardware: Nvidia T4 (medium)")
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print(f"URL: {job.url}")
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# Save info
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job_info = {
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"job_id": job.id,
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"status": str(job.status),
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"url": job.url,
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"hardware": "t4-medium",
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"cost_per_min": 0.01,
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"estimated_total": 3.00,
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"estimated_duration_hours": "2-3",
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"adapter": "constraint_tracker",
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}
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with open("hf_job_info.json", "w") as f:
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json.dump(job_info, f, indent=2)
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print(f"\nMonitor at: {job.url}")
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print(f"\nEstimated completion: 2-3 hours")
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print(f"Budget: ~$3.00 (fits within your credits)")
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print(f"\nThe job will:")
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print(f" 1. Install PyTorch on T4-medium GPU")
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print(f" 2. Load base model (codette-llama-3.1-8b-merged)")
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print(f" 3. Download dataset (constraint_tracking.jsonl)")
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print(f" 4. Train constraint_tracker LoRA (3 epochs, lr=1e-4)")
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print(f" 5. Upload adapter to Raiff1982/codette-lora-adapters")
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except Exception as e:
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print(f"\nERROR: {e}")
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# Try alternative if t4-medium not available
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print(f"\nT4-medium might not be available, trying T4-small instead...")
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try:
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job = api.run_uv_job(
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script="training/train_standalone.py",
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dependencies=dependencies,
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env={"HF_TOKEN": HF_TOKEN},
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flavor="t4-small",
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token=HF_TOKEN,
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)
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print(f"[OK] Job submitted with T4-small instead")
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print(f"Job ID: {job.id}")
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print(f"URL: {job.url}")
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except Exception as e2:
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print(f"T4-small also failed: {e2}")
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print(f"\nTrying without explicit flavor...")
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try:
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job = api.run_uv_job(
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script="training/train_standalone.py",
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dependencies=dependencies,
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env={"HF_TOKEN": HF_TOKEN},
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token=HF_TOKEN,
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)
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print(f"[OK] Job submitted with default hardware")
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print(f"Job ID: {job.id}")
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except Exception as e3:
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print(f"All submissions failed: {e3}")
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exit(1)
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print(f"\n{'=' * 70}")
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print("[OK] Training Job Ready!")
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print("=" * 70 + "\n")

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