File size: 2,714 Bytes
6963ef7 c4dd148 6963ef7 2638a07 6963ef7 2638a07 6963ef7 2638a07 6963ef7 2638a07 6963ef7 2638a07 6963ef7 2638a07 6963ef7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | #!/usr/bin/env python3
# /// script
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers>=4.36.0",
# "accelerate>=0.24.0",
# "trackio",
# ]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio
import os
print("🚀 Starting TRL + Trackio Demo")
print("=" * 50)
# Initialize Trackio with Space sync for remote viewing
# Trackio will auto-create the Space if it doesn't exist
print("\n📊 Initializing Trackio...")
trackio.init(
project="trl-demo",
space_id="evalstate/trl-trackio-dashboard", # Auto-creates if needed!
config={
"model": "Qwen/Qwen2.5-0.5B",
"dataset": "trl-lib/Capybara",
"max_steps": 50, # Longer for better visualization
"learning_rate": 2e-5,
}
)
print("✅ Trackio initialized! Dashboard: https://huggingface.co/spaces/evalstate/trl-trackio-dashboard")
# Load a small dataset (200 examples for better visualization)
print("\n📊 Loading dataset...")
dataset = load_dataset("trl-lib/Capybara", split="train[:200]")
print(f"✅ Dataset loaded: {len(dataset)} examples")
# Get username for hub push
username = os.environ.get("HF_USERNAME", "evalstate") # fallback to evalstate
# Training configuration with Trackio enabled
print("\n⚙️ Configuring training...")
config = SFTConfig(
# Output and Hub settings
output_dir="trl-demo",
push_to_hub=True,
hub_model_id=f"{username}/trl-trackio-demo",
# Training settings (longer for better metrics)
max_steps=50, # More steps for visualization
per_device_train_batch_size=2,
# Logging (log frequently for real-time monitoring)
logging_steps=5,
# Trackio monitoring - this is the key!
report_to="trackio",
# Learning rate
learning_rate=2e-5,
)
# LoRA configuration (reduces memory usage)
print("🔧 Setting up LoRA...")
peft_config = LoraConfig(
r=8,
lora_alpha=16,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
# Initialize trainer
print("\n🎯 Initializing trainer...")
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B",
train_dataset=dataset,
args=config,
peft_config=peft_config,
)
# Train!
print("\n🏃 Training started...")
print("📈 Trackio will track: loss, learning rate, GPU usage, memory, throughput")
print("-" * 50)
trainer.train()
# Save to Hub
print("\n💾 Pushing to Hub...")
trainer.push_to_hub()
# Finish Trackio logging
print("\n📊 Finalizing Trackio...")
trackio.finish()
print("\n✅ Demo complete!")
print(f"📦 Model saved to: https://huggingface.co/{username}/trl-trackio-demo")
print("📊 Check Trackio for training metrics and visualizations!")
|