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https://github.com/allenai/olmocr.git
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Starting on a new approach
This commit is contained in:
Jake Poznanski
parent
faf8659028
commit
12a91ffa96
@ -1,226 +1,139 @@
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import logging
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import argparse
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import subprocess
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import signal
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import sys
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import os
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import time
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import tempfile
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import redis
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import redis.exceptions
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import random
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import boto3
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import atexit
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import os
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from pdelfin.s3_utils import expand_s3_glob
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from tqdm import tqdm
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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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from pdelfin.s3_utils import expand_s3_glob, get_s3_bytes, parse_s3_path, put_s3_bytes
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# Basic logging setup for now
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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logging.basicConfig(level=logging.INFO)
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# Quiet logs from pypdf
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logging.getLogger("pypdf").setLevel(logging.ERROR)
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# Global s3 client for the whole script, feel free to adjust params if you need it
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workspace_s3 = boto3.client('s3')
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pdf_s3 = boto3.client('s3')
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LOCK_KEY = "queue_populating"
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LOCK_TIMEOUT = 30 # seconds
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def populate_queue_if_empty(queue, s3_glob_path, redis_client):
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"""
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Check if the queue is empty. If it is, attempt to acquire a lock to populate it.
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Only one worker should populate the queue at a time.
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"""
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if queue.llen("work_queue") == 0:
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# Attempt to acquire the lock
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lock_acquired = redis_client.set(LOCK_KEY, "locked", nx=True, ex=LOCK_TIMEOUT)
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if lock_acquired:
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print("Acquired lock to populate the queue.")
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try:
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paths = expand_s3_glob(pdf_s3, s3_glob_path)
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if not paths:
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print("No paths found to populate the queue.")
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return
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for path in paths:
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queue.rpush("work_queue", path)
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print("Queue populated with initial work items.")
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except Exception as e:
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print(f"Error populating queue: {e}")
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# Optionally, handle retry logic or alerting here
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finally:
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# Release the lock
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redis_client.delete(LOCK_KEY)
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print("Released lock after populating the queue.")
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else:
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print("Another worker is populating the queue. Waiting for it to complete.")
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# Optionally, wait until the queue is populated
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wait_for_queue_population(queue)
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def download_zstd_csv(s3_client, s3_path):
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"""Download and decompress a .zstd CSV file from S3."""
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try:
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compressed_data = get_s3_bytes(s3_client, s3_path)
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dctx = zstd.ZstdDecompressor()
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decompressed = dctx.decompress(compressed_data)
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text_stream = TextIOWrapper(BytesIO(decompressed), encoding='utf-8')
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lines = text_stream.readlines()
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logger.info(f"Downloaded and decompressed {s3_path}")
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return lines
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except s3_client.exceptions.NoSuchKey:
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logger.info(f"No existing {s3_path} found in s3, starting fresh.")
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return []
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def wait_for_queue_population(queue, wait_time=5, max_wait=60):
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"""
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Wait until the queue is populated by another worker.
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"""
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elapsed = 0
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while elapsed < max_wait:
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queue_length = queue.llen("work_queue")
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if queue_length > 0:
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print("Queue has been populated by another worker.")
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return
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print(f"Waiting for queue to be populated... ({elapsed + wait_time}/{max_wait} seconds)")
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time.sleep(wait_time)
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elapsed += wait_time
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print("Timeout waiting for queue to be populated.")
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sys.exit(1)
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def process(item):
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# Simulate processing time between 1 and 3 seconds
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print(f"Processing item: {item}")
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time.sleep(0.5)
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print(f"Completed processing item: {item}")
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def upload_zstd_csv(s3_client, s3_path, lines):
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"""Compress and upload a list of lines as a .zstd CSV file to S3."""
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joined_text = "\n".join(lines)
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compressor = zstd.ZstdCompressor()
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compressed = compressor.compress(joined_text.encode('utf-8'))
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put_s3_bytes(s3_client, s3_path, compressed)
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logger.info(f"Uploaded compressed {s3_path}")
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def get_redis_client(sentinel, master_name, leader_ip, leader_port, max_wait=60):
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"""
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Obtain a Redis client using Sentinel, with retry logic.
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"""
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elapsed = 0
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wait_interval = 1 # seconds
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while elapsed < max_wait:
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try:
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r = sentinel.master_for(master_name, socket_timeout=0.1, decode_responses=True)
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r.ping()
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print(f"Connected to Redis master at {leader_ip}:{leader_port}")
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return r
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except redis.exceptions.ConnectionError as e:
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print(f"Attempt {elapsed + 1}: Unable to connect to Redis master at {leader_ip}:{leader_port}. Retrying in {wait_interval} second(s)...")
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time.sleep(wait_interval)
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elapsed += wait_interval
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print(f"Failed to connect to Redis master at {leader_ip}:{leader_port} after {max_wait} seconds. Exiting.")
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sys.exit(1)
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def main():
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parser = argparse.ArgumentParser(description='Set up Redis Sentinel-based worker queue.')
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parser.add_argument('--leader-ip', help='IP address of the initial leader node')
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parser.add_argument('--leader-port', type=int, default=6379, help='Port of the initial leader node')
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parser.add_argument('--replica', type=int, required=True, help='Replica number (0 to N-1)')
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parser.add_argument('--add-pdfs', help='S3 glob path for work items')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline')
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parser.add_argument('workspace', help='The S3 path where work will be done e.g., s3://bucket/prefix/')
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parser.add_argument('--pdfs', help='Path to add pdfs stored in s3 to the workspace, can be a glob path s3://bucket/prefix/*.pdf or path to file containing list of pdf paths', default=None)
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parser.add_argument('--target_longest_image_dim', type=int, help='Dimension on longest side to use for rendering the pdf pages', default=1024)
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parser.add_argument('--target_anchor_text_len', type=int, help='Maximum amount of anchor text to use (characters)', default=6000)
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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=10, help='Number of workers to run at a time')
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args = parser.parse_args()
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replica_number = args.replica
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if args.workspace_profile:
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workspace_session = boto3.Session(profile_name=args.workspace_profile)
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workspace_s3 = workspace_session.client("s3")
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base_redis_port = 6379
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base_sentinel_port = 26379
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if args.pdf_profile:
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pdf_session = boto3.Session(profile_name=args.pdf_profile)
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pdf_s3 = pdf_session.client("s3")
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redis_port = base_redis_port + replica_number
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sentinel_port = base_sentinel_port + replica_number
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if replica_number == 0:
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leader_ip = args.leader_ip if args.leader_ip else '127.0.0.1'
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leader_port = args.leader_port
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else:
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if not args.leader_ip:
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print('Error: --leader-ip is required for replica nodes (replica_number >= 1)')
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sys.exit(1)
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leader_ip = args.leader_ip
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leader_port = args.leader_port
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temp_dir = tempfile.mkdtemp()
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redis_conf_path = os.path.join(temp_dir, 'redis.conf')
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sentinel_conf_path = os.path.join(temp_dir, 'sentinel.conf')
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print("Redis config path:", redis_conf_path)
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with open(redis_conf_path, 'w') as f:
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f.write(f'port {redis_port}\n')
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f.write(f'dbfilename dump-{replica_number}.rdb\n')
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f.write(f'appendfilename "appendonly-{replica_number}.aof"\n')
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f.write(f'logfile "redis-{replica_number}.log"\n')
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f.write(f'dir {temp_dir}\n')
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if replica_number == 0:
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f.write('bind 0.0.0.0\n')
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# Check list of pdfs and that it matches what's in the workspace
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if args.pdfs:
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if args.pdfs.startswith("s3://"):
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logger.info(f"Expanding s3 glob at {args.pdfs}")
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all_pdfs = expand_s3_glob(pdf_s3, args.pdfs)
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elif os.path.exists(args.pdfs):
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logger.info(f"Loading file at {args.pdfs}")
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with open(args.pdfs, "r") as f:
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all_pdfs = list(filter(None, (line.strip() for line in tqdm(f, desc="Processing PDFs"))))
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else:
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f.write(f'replicaof {leader_ip} {leader_port}\n')
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raise ValueError("pdfs argument needs to be either an s3 glob search path, or a local file contains pdf paths (one per line)")
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master_name = 'mymaster'
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quorum = 1
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all_pdfs = set(all_pdfs)
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logger.info(f"Found {len(all_pdfs):,} total pdf paths")
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index_file_s3_path = os.path.join(args.workspace, "pdf_index_list.csv.zstd")
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existing_lines = download_zstd_csv(workspace_s3, index_file_s3_path)
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with open(sentinel_conf_path, 'w') as f:
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f.write(f'port {sentinel_port}\n')
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f.write(f'dir {temp_dir}\n')
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f.write(f'sentinel monitor {master_name} {leader_ip} {leader_port} {quorum}\n')
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f.write(f'sentinel down-after-milliseconds {master_name} 5000\n')
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f.write(f'sentinel failover-timeout {master_name} 10000\n')
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f.write(f'sentinel parallel-syncs {master_name} 1\n')
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# Parse existing work items into groups
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existing_groups = [line.strip().split(",") for line in existing_lines if line.strip()]
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existing_pdf_set = set(pdf for group in existing_groups for pdf in group)
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redis_process = subprocess.Popen(['redis-server', redis_conf_path])
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sentinel_process = subprocess.Popen(['redis-sentinel', sentinel_conf_path])
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logger.info(f"Loaded {len(existing_pdf_set):,} existing pdf paths from the workspace")
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# Register atexit function to guarantee process termination
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def terminate_processes():
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print("Terminating child processes...")
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redis_process.terminate()
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sentinel_process.terminate()
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try:
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redis_process.wait(timeout=5)
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sentinel_process.wait(timeout=5)
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except subprocess.TimeoutExpired:
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print("Forcing termination of child processes.")
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redis_process.kill()
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sentinel_process.kill()
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print("Child processes terminated.")
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# Remove existing PDFs from all_pdfs
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new_pdfs = all_pdfs - existing_pdf_set
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logger.info(f"{len(new_pdfs):,} new pdf paths to add to the workspace")
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atexit.register(terminate_processes)
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# Group the new PDFs into chunks of group_size
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new_groups = []
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current_group = []
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for pdf in sorted(new_pdfs): # Sort for consistency
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current_group.append(pdf)
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if len(current_group) == args.group_size:
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new_groups.append(current_group)
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current_group = []
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if current_group:
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new_groups.append(current_group)
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# Also handle signal-based termination
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def handle_signal(signum, frame):
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print(f"Received signal {signum}. Terminating processes...")
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terminate_processes()
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sys.exit(0)
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logger.info(f"Created {len(new_groups):,} new work groups")
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signal.signal(signal.SIGINT, handle_signal)
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signal.signal(signal.SIGTERM, handle_signal)
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# Combine existing groups with new groups
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combined_groups = existing_groups + new_groups
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time.sleep(2)
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# Prepare lines to write back
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combined_lines = [",".join(group) for group in combined_groups]
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# Use Sentinel to connect to the master
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from redis.sentinel import Sentinel
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sentinel = Sentinel([('127.0.0.1', sentinel_port)], socket_timeout=0.1)
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# Upload the combined work items back to S3
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upload_zstd_csv(workspace_s3, index_file_s3_path, combined_lines)
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# Initial connection to Redis master
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redis_client = get_redis_client(sentinel, master_name, leader_ip, leader_port)
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logger.info("Completed adding new PDFs.")
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# Populate the work queue if it's empty, using a distributed lock
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populate_queue_if_empty(redis_client, args.add_pdfs, redis_client)
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try:
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while True:
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try:
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# Try to get an item from the queue with a 1-minute timeout for processing
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work_item = redis_client.brpoplpush("work_queue", "processing_queue", 60)
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if work_item:
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try:
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process(work_item)
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# Remove from the processing queue if processed successfully
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redis_client.lrem("processing_queue", 1, work_item)
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except Exception as e:
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print(f"Error processing {work_item}: {e}")
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# If an error occurs, let it be requeued after timeout
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# If there is a beaker flag, then your job is to trigger this script with N replicas on beaker
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# If not, then your job is to do the actual work
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queue_length = redis_client.llen("work_queue")
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print(f"Total work items in queue: {queue_length}")
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# Start up the sglang server
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time.sleep(0.1)
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# Read in the work queue from s3
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# Read in the done items from the s3 workspace
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except (redis.exceptions.ConnectionError, redis.exceptions.TimeoutError) as e:
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print("Lost connection to Redis. Attempting to reconnect using Sentinel...")
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# Attempt to reconnect using Sentinel
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while True:
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try:
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redis_client = get_redis_client(sentinel, master_name, leader_ip, leader_port)
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print("Reconnected to Redis master.")
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break # Exit the reconnection loop and resume work
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except redis.exceptions.ConnectionError:
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print("Reconnection failed. Retrying in 5 seconds...")
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time.sleep(5)
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except Exception as e:
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print(f"Unexpected error: {e}")
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handle_signal(None, None)
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# Spawn up to N workers to do:
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# In a loop, take a random work item, read in the pdfs, queue in their requests
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# Get results back, retry any failed pages
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# Check periodically if that work is done in s3, if so, then abandon this work
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# Save results back to s3 workspace output folder
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except KeyboardInterrupt:
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handle_signal(None, None)
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if __name__ == '__main__':
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main()
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# Possible future addon, in beaker, discover other nodes on this same job
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# Send them a message when you take a work item off the queue
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