File size: 3,212 Bytes
fd357f4 | 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 | #!/usr/bin/env python3
"""
Individual model file upload script
Uses the successful single-file upload approach
"""
import os
import logging
from huggingface_hub import upload_file
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def upload_individual_models():
"""Upload individual model files using proven method"""
token = os.getenv('HF_TOKEN')
if not token:
raise ValueError("HF_TOKEN environment variable not set")
# Find all model files (excluding extremely large ones)
model_files = []
experiments_path = "/data/experiments"
if os.path.exists(experiments_path):
for root, _, files in os.walk(experiments_path):
for file in files:
if file.endswith(('.safetensors', '.pt', '.bin')):
file_path = os.path.join(root, file)
try:
file_size = os.path.getsize(file_path)
# Skip files larger than 10GB
if file_size > 10 * 1024**3:
logger.warning(f"Skipping extremely large file: {file_path} ({file_size/1024**3:.1f}GB)")
continue
model_files.append(file_path)
except OSError:
logger.warning(f"Could not get size for {file_path}")
logger.info(f"Found {len(model_files)} model files to upload")
# Upload files individually
success_count = 0
failed_count = 0
for file_path in model_files:
try:
# Create repository path
rel_path = file_path.replace('/data/experiments/', '')
logger.info(f"Uploading: {file_path} -> LevelUp2x/dto-models/{rel_path}")
# Upload individual file (this method worked for the test file)
upload_file(
path_or_fileobj=file_path,
path_in_repo=rel_path,
repo_id='LevelUp2x/dto-models',
token=token,
commit_message=f"DTO Archive: Uploading {os.path.basename(file_path)}"
)
logger.info(f"✅ Successfully uploaded {file_path}")
success_count += 1
except Exception as e:
logger.error(f"❌ Failed to upload {file_path}: {e}")
failed_count += 1
logger.info(f"Upload Summary: {success_count} successful, {failed_count} failed")
if success_count > 0:
logger.info("✅ Individual file upload completed successfully")
return True
else:
logger.error("❌ Individual file upload failed completely")
return False
if __name__ == "__main__":
# Load environment variables
env_file = "/data/adaptai/platform/dataops/dto/.env"
if os.path.exists(env_file):
with open(env_file) as f:
for line in f:
if line.strip() and not line.startswith('#'):
key, value = line.strip().split('=', 1)
os.environ[key] = value
upload_individual_models() |