mirror of
https://github.com/allenai/olmocr.git
synced 2025-12-12 15:51:26 +00:00
Downloads from s3 based on hash
This commit is contained in:
Jake Poznanski
parent
6598e2dc45
commit
910c2ebcfc
@ -398,8 +398,7 @@ async def worker(args, queue, semaphore, worker_id):
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async def sglang_server_task(args, semaphore):
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model_cache_dir = os.path.join(os.path.expanduser('~'), '.cache', 'pdelfin', 'model')
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# TODO cache locally
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#download_directory(args.model, model_cache_dir)
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download_directory(args.model, model_cache_dir)
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# Check the rope config and make sure it's got the proper key
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with open(os.path.join(model_cache_dir, "config.json"), "r") as cfin:
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@ -484,6 +483,7 @@ async def sglang_server_task(args, semaphore):
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async def sglang_server_host(args, semaphore):
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while True:
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await sglang_server_task(args, semaphore)
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logger.warning("SGLang server task ended")
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async def sglang_server_ready():
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@ -525,7 +525,7 @@ async def main():
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parser.add_argument('--workspace_profile', help='S3 configuration profile for accessing the workspace', default=None)
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parser.add_argument('--pdf_profile', help='S3 configuration profile for accessing the raw pdf documents', default=None)
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parser.add_argument('--group_size', type=int, default=20, help='Number of pdfs that will be part of each work item in the work queue.')
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parser.add_argument('--workers', type=int, default=3, help='Number of workers to run at a time')
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parser.add_argument('--workers', type=int, default=5, help='Number of workers to run at a time')
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parser.add_argument('--model', help='List of paths where you can find the model to convert this pdf. You can specify several different paths here, and the script will try to use the one which is fastest to access',
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default=["weka://oe-data-default/jakep/Qwen_Qwen2-VL-7B-Instruct-e4ecf8-01JAH8GMWHTJ376S2N7ETXRXH4/best_bf16/",
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@ -6,6 +6,7 @@ import tempfile
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import boto3
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import requests
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import concurrent.futures
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import hashlib # Added for MD5 hash computation
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from urllib.parse import urlparse
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from pathlib import Path
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@ -14,7 +15,7 @@ from google.cloud import storage
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from botocore.config import Config
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from botocore.exceptions import NoCredentialsError
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from boto3.s3.transfer import TransferConfig
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from typing import Optional
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from typing import Optional, List
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from urllib.parse import urlparse
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import zstandard as zstd
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from io import BytesIO, TextIOWrapper
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@ -133,21 +134,19 @@ def is_running_on_gcp():
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except requests.RequestException:
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return False
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def download_directory(model_choices: list[str], local_dir: str):
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def download_directory(model_choices: List[str], local_dir: str):
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"""
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Download the model to a specified local directory.
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The function will attempt to download from the first available source in the provided list.
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Supports Weka (weka://), Google Cloud Storage (gs://), and Amazon S3 (s3://) links.
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Args:
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model_choices (list[str]): List of model paths (weka://, gs://, or s3://).
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model_choices (List[str]): List of model paths (weka://, gs://, or s3://).
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local_dir (str): Local directory path where the model will be downloaded.
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Raises:
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ValueError: If no valid model path is found in the provided choices.
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"""
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# Ensure the local directory exists
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local_path = Path(os.path.expanduser(local_dir))
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local_path.mkdir(parents=True, exist_ok=True)
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logger.info(f"Local directory set to: {local_path}")
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@ -157,148 +156,180 @@ def download_directory(model_choices: list[str], local_dir: str):
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other_choices = [path for path in model_choices if not path.startswith("weka://")]
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prioritized_choices = weka_choices + other_choices
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# Iterate through the provided choices and attempt to download from the first available source
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for model_path in prioritized_choices:
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logger.info(f"Attempting to download from: {model_path}")
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try:
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if model_path.startswith("weka://"):
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download_dir_from_weka(model_path, str(local_path))
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download_dir_from_storage(
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model_path, str(local_path), storage_type='weka')
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logger.info(f"Successfully downloaded model from Weka: {model_path}")
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return
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elif model_path.startswith("gs://"):
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download_dir_from_gcs(model_path, str(local_path))
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download_dir_from_storage(
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model_path, str(local_path), storage_type='gcs')
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logger.info(f"Successfully downloaded model from Google Cloud Storage: {model_path}")
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return
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elif model_path.startswith("s3://"):
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download_dir_from_s3(model_path, str(local_path))
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download_dir_from_storage(
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model_path, str(local_path), storage_type='s3')
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logger.info(f"Successfully downloaded model from S3: {model_path}")
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return
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else:
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logger.warning(f"Unsupported model path scheme: {model_path}")
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except Exception as e:
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logger.error(f"Failed to download from {model_path}: {e}")
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continue # Try the next available source
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continue
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raise ValueError("Failed to download the model from all provided sources.")
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def download_dir_from_gcs(gcs_path: str, local_dir: str):
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"""Download model files from Google Cloud Storage to a local directory."""
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client = storage.Client()
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bucket_name, prefix = parse_s3_path(gcs_path.replace("gs://", "s3://"))
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bucket = client.bucket(bucket_name)
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def download_dir_from_storage(storage_path: str, local_dir: str, storage_type: str):
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"""
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Generalized function to download model files from different storage services
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to a local directory, syncing using MD5 hashes where possible.
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blobs = list(bucket.list_blobs(prefix=prefix))
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total_files = len(blobs)
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logger.info(f"Found {total_files} files in GCS bucket '{bucket_name}' with prefix '{prefix}'.")
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with concurrent.futures.ThreadPoolExecutor() as executor:
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futures = []
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for blob in blobs:
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relative_path = os.path.relpath(blob.name, prefix)
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local_file_path = os.path.join(local_dir, relative_path)
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os.makedirs(os.path.dirname(local_file_path), exist_ok=True)
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futures.append(executor.submit(blob.download_to_filename, local_file_path))
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# Use tqdm to display progress
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for _ in tqdm(concurrent.futures.as_completed(futures), total=total_files, desc="Downloading from GCS"):
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pass
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logger.info(f"Downloaded model from Google Cloud Storage to {local_dir}")
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def download_dir_from_s3(s3_path: str, local_dir: str):
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"""Download model files from S3 to a local directory."""
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boto3_config = Config(
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max_pool_connections=500 # Adjust this number based on your requirements
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)
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s3_client = boto3.client('s3', config=boto3_config)
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bucket, prefix = parse_s3_path(s3_path)
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paginator = s3_client.get_paginator("list_objects_v2")
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pages = paginator.paginate(Bucket=bucket, Prefix=prefix)
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Args:
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storage_path (str): The path to the storage location (weka://, gs://, or s3://).
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local_dir (str): The local directory where files will be downloaded.
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storage_type (str): Type of storage ('weka', 'gcs', or 's3').
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Raises:
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ValueError: If the storage type is unsupported or credentials are missing.
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"""
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bucket_name, prefix = parse_s3_path(storage_path)
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total_files = 0
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objects = []
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for page in pages:
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if 'Contents' in page:
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objects.extend(page['Contents'])
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total_files = len(objects)
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logger.info(f"Found {total_files} files in S3 bucket '{bucket}' with prefix '{prefix}'.")
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if storage_type == 'gcs':
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client = storage.Client()
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bucket = client.bucket(bucket_name)
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blobs = list(bucket.list_blobs(prefix=prefix))
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total_files = len(blobs)
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logger.info(f"Found {total_files} files in GCS bucket '{bucket_name}' with prefix '{prefix}'.")
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def should_download(blob, local_file_path):
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return compare_hashes_gcs(blob, local_file_path)
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def download_blob(blob, local_file_path):
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blob.download_to_filename(local_file_path)
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items = blobs
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elif storage_type in ('s3', 'weka'):
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if storage_type == 'weka':
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weka_access_key = os.getenv("WEKA_ACCESS_KEY_ID")
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weka_secret_key = os.getenv("WEKA_SECRET_ACCESS_KEY")
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if not weka_access_key or not weka_secret_key:
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raise ValueError("WEKA_ACCESS_KEY_ID and WEKA_SECRET_ACCESS_KEY must be set for Weka access.")
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endpoint_url = "https://weka-aus.beaker.org:9000"
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boto3_config = Config(
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max_pool_connections=500,
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signature_version='s3v4',
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retries={'max_attempts': 10, 'mode': 'standard'}
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)
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s3_client = boto3.client(
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's3',
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endpoint_url=endpoint_url,
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aws_access_key_id=weka_access_key,
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aws_secret_access_key=weka_secret_key,
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config=boto3_config
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)
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else:
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s3_client = boto3.client('s3', config=Config(max_pool_connections=500))
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paginator = s3_client.get_paginator("list_objects_v2")
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pages = paginator.paginate(Bucket=bucket_name, Prefix=prefix)
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for page in pages:
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if 'Contents' in page:
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objects.extend(page['Contents'])
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total_files = len(objects)
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logger.info(f"Found {total_files} files in {'Weka' if storage_type == 'weka' else 'S3'} bucket '{bucket_name}' with prefix '{prefix}'.")
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transfer_config = TransferConfig(
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multipart_threshold=8 * 1024 * 1024,
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multipart_chunksize=8 * 1024 * 1024,
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max_concurrency=100,
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use_threads=True
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)
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def should_download(obj, local_file_path):
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return compare_hashes_s3(obj, local_file_path)
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def download_blob(obj, local_file_path):
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s3_client.download_file(bucket_name, obj['Key'], local_file_path, Config=transfer_config)
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items = objects
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else:
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raise ValueError(f"Unsupported storage type: {storage_type}")
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with concurrent.futures.ThreadPoolExecutor() as executor:
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futures = []
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for obj in objects:
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key = obj["Key"]
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relative_path = os.path.relpath(key, prefix)
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for item in items:
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if storage_type == 'gcs':
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relative_path = os.path.relpath(item.name, prefix)
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else:
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relative_path = os.path.relpath(item['Key'], prefix)
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local_file_path = os.path.join(local_dir, relative_path)
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os.makedirs(os.path.dirname(local_file_path), exist_ok=True)
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futures.append(executor.submit(s3_client.download_file, bucket, key, local_file_path))
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if should_download(item, local_file_path):
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futures.append(executor.submit(download_blob, item, local_file_path))
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else:
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total_files -= 1 # Decrement total_files as we're skipping this file
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# Use tqdm to display progress
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for _ in tqdm(concurrent.futures.as_completed(futures), total=total_files, desc="Downloading from S3"):
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pass
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if total_files > 0:
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for _ in tqdm(concurrent.futures.as_completed(futures), total=total_files, desc=f"Downloading from {storage_type.upper()}"):
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pass
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else:
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logger.info("All files are up-to-date. No downloads needed.")
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logger.info(f"Downloaded model from S3 to {local_dir}")
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logger.info(f"Downloaded model from {storage_type.upper()} to {local_dir}")
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def download_dir_from_weka(weka_path: str, local_dir: str):
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"""Download model files from Weka to a local directory."""
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# Retrieve Weka credentials from environment variables
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weka_access_key = os.getenv("WEKA_ACCESS_KEY_ID")
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weka_secret_key = os.getenv("WEKA_SECRET_ACCESS_KEY")
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if not weka_access_key or not weka_secret_key:
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raise ValueError("WEKA_ACCESS_KEY_ID and WEKA_SECRET_ACCESS_KEY environment variables must be set for Weka access.")
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# Configure the boto3 client for Weka
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weka_endpoint = "https://weka-aus.beaker.org:9000"
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boto3_config = Config(
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max_pool_connections=500, # Adjust this number based on your requirements
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signature_version='s3v4',
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retries={'max_attempts': 10, 'mode': 'standard'}
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)
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# Configure transfer settings for multipart download
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transfer_config = TransferConfig(
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multipart_threshold=8 * 1024 * 1024, # 8MB threshold for multipart downloads
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multipart_chunksize=8 * 1024 * 1024, # 8MB per part
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max_concurrency=100, # Number of threads for each file download
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use_threads=True # Enable threading
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)
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s3_client = boto3.client(
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's3',
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endpoint_url=weka_endpoint,
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aws_access_key_id=weka_access_key,
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aws_secret_access_key=weka_secret_key,
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config=boto3_config
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)
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def compare_hashes_gcs(blob, local_file_path: str) -> bool:
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"""Compare MD5 hashes for GCS blobs."""
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if os.path.exists(local_file_path):
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remote_md5_base64 = blob.md5_hash
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hash_md5 = hashlib.md5()
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with open(local_file_path, "rb") as f:
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for chunk in iter(lambda: f.read(8192), b""):
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hash_md5.update(chunk)
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local_md5 = hash_md5.digest()
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remote_md5 = base64.b64decode(remote_md5_base64)
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if remote_md5 == local_md5:
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logger.info(f"File '{local_file_path}' already up-to-date. Skipping download.")
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return False
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else:
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logger.info(f"File '{local_file_path}' differs from GCS. Downloading.")
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return True
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else:
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logger.info(f"File '{local_file_path}' does not exist locally. Downloading.")
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return True
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bucket, prefix = parse_s3_path(weka_path)
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paginator = s3_client.get_paginator("list_objects_v2")
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try:
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pages = paginator.paginate(Bucket=bucket, Prefix=prefix)
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except s3_client.exceptions.NoSuchBucket:
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raise ValueError(f"The bucket '{bucket}' does not exist in Weka.")
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objects = []
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for page in pages:
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if 'Contents' in page:
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objects.extend(page['Contents'])
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total_files = len(objects)
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logger.info(f"Found {total_files} files in Weka bucket '{bucket}' with prefix '{prefix}'.")
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with concurrent.futures.ThreadPoolExecutor() as executor:
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futures = []
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for obj in objects:
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key = obj["Key"]
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relative_path = os.path.relpath(key, prefix)
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local_file_path = os.path.join(local_dir, relative_path)
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os.makedirs(os.path.dirname(local_file_path), exist_ok=True)
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futures.append(executor.submit(s3_client.download_file, bucket, key, local_file_path, Config=transfer_config))
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# Use tqdm to display progress
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for _ in tqdm(concurrent.futures.as_completed(futures), total=total_files, desc="Downloading from Weka"):
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pass
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logger.info(f"Downloaded model from Weka to {local_dir}")
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def compare_hashes_s3(obj, local_file_path: str) -> bool:
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"""Compare MD5 hashes or sizes for S3 objects (including Weka)."""
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if os.path.exists(local_file_path):
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etag = obj['ETag'].strip('"')
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if '-' in etag:
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remote_size = obj['Size']
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local_size = os.path.getsize(local_file_path)
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if remote_size == local_size:
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logger.info(f"File '{local_file_path}' size matches remote multipart file. Skipping download.")
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return False
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else:
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logger.info(f"File '{local_file_path}' size differs from remote multipart file. Downloading.")
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return True
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else:
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hash_md5 = hashlib.md5()
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with open(local_file_path, "rb") as f:
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for chunk in iter(lambda: f.read(8192), b""):
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hash_md5.update(chunk)
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local_md5 = hash_md5.hexdigest()
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if etag == local_md5:
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logger.info(f"File '{local_file_path}' already up-to-date. Skipping download.")
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return False
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else:
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logger.info(f"File '{local_file_path}' differs from remote. Downloading.")
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return True
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else:
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logger.info(f"File '{local_file_path}' does not exist locally. Downloading.")
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return True
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