#!/usr/bin/env python3 """ Setup script for Hugging Face Dataset repository for Trackio experiments """ import os import json from datetime import datetime from pathlib import Path from datasets import Dataset from huggingface_hub import HfApi, create_repo import subprocess def get_username_from_token(token: str) -> str: """Get username from HF token with fallback to CLI""" try: # Try API first api = HfApi(token=token) user_info = api.whoami() # Handle different possible response formats if isinstance(user_info, dict): # Try different possible keys for username username = ( user_info.get('name') or user_info.get('username') or user_info.get('user') or None ) elif isinstance(user_info, str): # If whoami returns just the username as string username = user_info else: username = None if username: print(f"✅ Got username from API: {username}") return username else: print("⚠️ Could not get username from API, trying CLI...") return get_username_from_cli(token) except Exception as e: print(f"⚠️ API whoami failed: {e}") print("⚠️ Trying CLI fallback...") return get_username_from_cli(token) def get_username_from_cli(token: str) -> str: """Fallback method to get username using CLI""" try: # Set HF token for CLI os.environ['HF_TOKEN'] = token # Get username using CLI result = subprocess.run( ["huggingface-cli", "whoami"], capture_output=True, text=True, timeout=30 ) if result.returncode == 0: username = result.stdout.strip() if username: print(f"✅ Got username from CLI: {username}") return username else: print("⚠️ CLI returned empty username") return None else: print(f"⚠️ CLI whoami failed: {result.stderr}") return None except Exception as e: print(f"⚠️ CLI fallback failed: {e}") return None def setup_trackio_dataset(): """Set up the Trackio experiments dataset on Hugging Face Hub""" # Configuration - get from environment variables with fallbacks hf_token = os.environ.get('HF_TOKEN') if not hf_token: print("❌ HF_TOKEN not found. Please set the HF_TOKEN environment variable.") print("You can get your token from: https://huggingface.co/settings/tokens") return False username = get_username_from_token(hf_token) if not username: print("❌ Could not determine username from token. Please check your token.") return False print(f"✅ Authenticated as: {username}") # Use username in dataset repository if not specified dataset_repo = os.environ.get('TRACKIO_DATASET_REPO', f'{username}/trackio-experiments') print(f"🚀 Setting up Trackio dataset: {dataset_repo}") print(f"🔧 Using dataset repository: {dataset_repo}") # Initial experiment data initial_experiments = [ { 'experiment_id': 'exp_20250720_130853', 'name': 'petite-elle-l-aime-3', 'description': 'SmolLM3 fine-tuning experiment', 'created_at': '2025-07-20T11:20:01.780908', 'status': 'running', 'metrics': json.dumps([ { 'timestamp': '2025-07-20T11:20:01.780908', 'step': 25, 'metrics': { 'loss': 1.1659, 'grad_norm': 10.3125, 'learning_rate': 7e-08, 'num_tokens': 1642080.0, 'mean_token_accuracy': 0.75923578992486, 'epoch': 0.004851130919895701 } }, { 'timestamp': '2025-07-20T11:26:39.042155', 'step': 50, 'metrics': { 'loss': 1.165, 'grad_norm': 10.75, 'learning_rate': 1.4291666666666667e-07, 'num_tokens': 3324682.0, 'mean_token_accuracy': 0.7577659255266189, 'epoch': 0.009702261839791402 } }, { 'timestamp': '2025-07-20T11:33:16.203045', 'step': 75, 'metrics': { 'loss': 1.1639, 'grad_norm': 10.6875, 'learning_rate': 2.1583333333333334e-07, 'num_tokens': 4987941.0, 'mean_token_accuracy': 0.7581205774843692, 'epoch': 0.014553392759687101 } }, { 'timestamp': '2025-07-20T11:39:53.453917', 'step': 100, 'metrics': { 'loss': 1.1528, 'grad_norm': 10.75, 'learning_rate': 2.8875e-07, 'num_tokens': 6630190.0, 'mean_token_accuracy': 0.7614579878747463, 'epoch': 0.019404523679582803 } } ]), 'parameters': json.dumps({ 'model_name': 'HuggingFaceTB/SmolLM3-3B', 'max_seq_length': 12288, 'use_flash_attention': True, 'use_gradient_checkpointing': False, 'batch_size': 8, 'gradient_accumulation_steps': 16, 'learning_rate': 3.5e-06, 'weight_decay': 0.01, 'warmup_steps': 1200, 'max_iters': 18000, 'eval_interval': 1000, 'log_interval': 25, 'save_interval': 2000, 'optimizer': 'adamw_torch', 'beta1': 0.9, 'beta2': 0.999, 'eps': 1e-08, 'scheduler': 'cosine', 'min_lr': 3.5e-07, 'fp16': False, 'bf16': True, 'ddp_backend': 'nccl', 'ddp_find_unused_parameters': False, 'save_steps': 2000, 'eval_steps': 1000, 'logging_steps': 25, 'save_total_limit': 5, 'eval_strategy': 'steps', 'metric_for_best_model': 'eval_loss', 'greater_is_better': False, 'load_best_model_at_end': True, 'data_dir': None, 'train_file': None, 'validation_file': None, 'test_file': None, 'use_chat_template': True, 'chat_template_kwargs': {'add_generation_prompt': True, 'no_think_system_message': True}, 'enable_tracking': True, 'trackio_url': 'https://tonic-test-trackio-test.hf.space', 'trackio_token': None, 'log_artifacts': True, 'log_metrics': True, 'log_config': True, 'experiment_name': 'petite-elle-l-aime-3', 'dataset_name': 'legmlai/openhermes-fr', 'dataset_split': 'train', 'input_field': 'prompt', 'target_field': 'accepted_completion', 'filter_bad_entries': True, 'bad_entry_field': 'bad_entry', 'packing': False, 'max_prompt_length': 12288, 'max_completion_length': 8192, 'truncation': True, 'dataloader_num_workers': 10, 'dataloader_pin_memory': True, 'dataloader_prefetch_factor': 3, 'max_grad_norm': 1.0, 'group_by_length': True }), 'artifacts': json.dumps([]), 'logs': json.dumps([]), 'last_updated': datetime.now().isoformat() }, { 'experiment_id': 'exp_20250720_134319', 'name': 'petite-elle-l-aime-3-1', 'description': 'SmolLM3 fine-tuning experiment', 'created_at': '2025-07-20T11:54:31.993219', 'status': 'running', 'metrics': json.dumps([ { 'timestamp': '2025-07-20T11:54:31.993219', 'step': 25, 'metrics': { 'loss': 1.166, 'grad_norm': 10.375, 'learning_rate': 7e-08, 'num_tokens': 1642080.0, 'mean_token_accuracy': 0.7590958896279335, 'epoch': 0.004851130919895701 } }, { 'timestamp': '2025-07-20T11:54:33.589487', 'step': 25, 'metrics': { 'gpu_0_memory_allocated': 17.202261447906494, 'gpu_0_memory_reserved': 75.474609375, 'gpu_0_utilization': 0, 'cpu_percent': 2.7, 'memory_percent': 10.1 } } ]), 'parameters': json.dumps({ 'model_name': 'HuggingFaceTB/SmolLM3-3B', 'max_seq_length': 12288, 'use_flash_attention': True, 'use_gradient_checkpointing': False, 'batch_size': 8, 'gradient_accumulation_steps': 16, 'learning_rate': 3.5e-06, 'weight_decay': 0.01, 'warmup_steps': 1200, 'max_iters': 18000, 'eval_interval': 1000, 'log_interval': 25, 'save_interval': 2000, 'optimizer': 'adamw_torch', 'beta1': 0.9, 'beta2': 0.999, 'eps': 1e-08, 'scheduler': 'cosine', 'min_lr': 3.5e-07, 'fp16': False, 'bf16': True, 'ddp_backend': 'nccl', 'ddp_find_unused_parameters': False, 'save_steps': 2000, 'eval_steps': 1000, 'logging_steps': 25, 'save_total_limit': 5, 'eval_strategy': 'steps', 'metric_for_best_model': 'eval_loss', 'greater_is_better': False, 'load_best_model_at_end': True, 'data_dir': None, 'train_file': None, 'validation_file': None, 'test_file': None, 'use_chat_template': True, 'chat_template_kwargs': {'add_generation_prompt': True, 'no_think_system_message': True}, 'enable_tracking': True, 'trackio_url': 'https://tonic-test-trackio-test.hf.space', 'trackio_token': None, 'log_artifacts': True, 'log_metrics': True, 'log_config': True, 'experiment_name': 'petite-elle-l-aime-3-1', 'dataset_name': 'legmlai/openhermes-fr', 'dataset_split': 'train', 'input_field': 'prompt', 'target_field': 'accepted_completion', 'filter_bad_entries': True, 'bad_entry_field': 'bad_entry', 'packing': False, 'max_prompt_length': 12288, 'max_completion_length': 8192, 'truncation': True, 'dataloader_num_workers': 10, 'dataloader_pin_memory': True, 'dataloader_prefetch_factor': 3, 'max_grad_norm': 1.0, 'group_by_length': True }), 'artifacts': json.dumps([]), 'logs': json.dumps([]), 'last_updated': datetime.now().isoformat() } ] try: # Initialize HF API api = HfApi(token=hf_token) # First, try to create the dataset repository print(f"Creating dataset repository: {dataset_repo}") try: create_repo( repo_id=dataset_repo, token=hf_token, repo_type="dataset", exist_ok=True, private=True # Make it private for security ) print(f"✅ Dataset repository created: {dataset_repo}") except Exception as e: print(f"⚠️ Repository creation failed (may already exist): {e}") # Create dataset dataset = Dataset.from_list(initial_experiments) # Get the project root directory (2 levels up from this script) project_root = Path(__file__).parent.parent.parent templates_dir = project_root / "templates" / "datasets" readme_path = templates_dir / "readme.md" # Read README content if it exists readme_content = None if readme_path.exists(): with open(readme_path, 'r', encoding='utf-8') as f: readme_content = f.read() print(f"✅ Found README template: {readme_path}") # Push to HF Hub print("Pushing dataset to HF Hub...") dataset.push_to_hub( dataset_repo, token=hf_token, private=False # Make it private for security ) # Create README separately if available if readme_content: try: print("Uploading README.md...") api.upload_file( path_or_fileobj=readme_content.encode('utf-8'), path_in_repo="README.md", repo_id=dataset_repo, repo_type="dataset", token=hf_token ) print("📝 Uploaded README.md successfully") except Exception as e: print(f"⚠️ Could not upload README: {e}") print(f"✅ Successfully created dataset: {dataset_repo}") print(f"📊 Added {len(initial_experiments)} experiments") if readme_content: print("📝 Included README from templates") print("🔓 Dataset is public (accessible to everyone)") print(f"👤 Created by: {username}") print("\n🎯 Next steps:") print("1. Set HF_TOKEN in your Hugging Face Space environment") print("2. Deploy the updated app.py to your Space") print("3. The app will now load experiments from the dataset") return True except Exception as e: print(f"❌ Failed to create dataset: {e}") print("\nTroubleshooting:") print("1. Check that your HF token has write permissions") print("2. Verify the dataset repository name is available") print("3. Try creating the dataset manually on HF first") return False if __name__ == "__main__": setup_trackio_dataset()