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123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
"""Dataset service orchestrated by a TFJob
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"""
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from typing import Optional
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import uuid
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from tml.ml_logging.torch_logging import logging
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import tml.machines.environment as env
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import packaging.version
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import tensorflow as tf
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try:
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import tensorflow_io as tfio
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except:
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pass
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from tensorflow.python.data.experimental.ops.data_service_ops import (
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_from_dataset_id,
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_register_dataset,
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)
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import torch.distributed as dist
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def maybe_start_dataset_service():
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if not env.has_readers():
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return
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if packaging.version.parse(tf.__version__) < packaging.version.parse("2.5"):
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raise Exception(f"maybe_distribute_dataset requires TF >= 2.5; got {tf.__version__}")
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if env.is_dispatcher():
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logging.info(f"env.get_reader_port() = {env.get_reader_port()}")
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logging.info(f"env.get_dds_journaling_dir() = {env.get_dds_journaling_dir()}")
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work_dir = env.get_dds_journaling_dir()
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server = tf.data.experimental.service.DispatchServer(
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tf.data.experimental.service.DispatcherConfig(
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port=env.get_reader_port(),
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protocol="grpc",
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work_dir=work_dir,
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fault_tolerant_mode=bool(work_dir),
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)
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)
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server.join()
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elif env.is_reader():
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logging.info(f"env.get_reader_port() = {env.get_reader_port()}")
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logging.info(f"env.get_dds_dispatcher_address() = {env.get_dds_dispatcher_address()}")
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logging.info(f"env.get_dds_worker_address() = {env.get_dds_worker_address()}")
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server = tf.data.experimental.service.WorkerServer(
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tf.data.experimental.service.WorkerConfig(
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port=env.get_reader_port(),
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dispatcher_address=env.get_dds_dispatcher_address(),
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worker_address=env.get_dds_worker_address(),
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protocol="grpc",
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)
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)
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server.join()
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def register_dataset(
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dataset: tf.data.Dataset, dataset_service: str, compression: Optional[str] = "AUTO"
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):
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if dist.get_rank() == 0:
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dataset_id = _register_dataset(
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service=dataset_service,
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dataset=dataset,
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compression=compression,
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)
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job_name = uuid.uuid4().hex[:8]
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id_and_job = [dataset_id.numpy(), job_name]
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logging.info(f"rank{dist.get_rank()}: Created dds job with {dataset_id.numpy()}, {job_name}")
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else:
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id_and_job = [None, None]
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dist.broadcast_object_list(id_and_job, src=0)
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return tuple(id_and_job)
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def distribute_from_dataset_id(
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dataset_service: str,
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dataset_id: int,
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job_name: Optional[str],
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compression: Optional[str] = "AUTO",
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prefetch: Optional[int] = tf.data.experimental.AUTOTUNE,
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) -> tf.data.Dataset:
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logging.info(f"rank{dist.get_rank()}: Consuming dds job with {dataset_id}, {job_name}")
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dataset = _from_dataset_id(
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processing_mode="parallel_epochs",
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service=dataset_service,
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dataset_id=dataset_id,
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job_name=job_name,
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element_spec=None,
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compression=compression,
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)
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if prefetch is not None:
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dataset = dataset.prefetch(prefetch)
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return dataset
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def maybe_distribute_dataset(dataset: tf.data.Dataset) -> tf.data.Dataset:
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"""Torch-compatible and distributed-training-aware dataset service distributor.
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- rank 0 process will register the given dataset.
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- rank 0 process will broadcast job name and dataset id.
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- all rank processes will consume from the same job/dataset.
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Without this, dataset workers will try to serve 1 job per rank process and OOM.
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"""
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if not env.has_readers():
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return dataset
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dataset_service = env.get_dds()
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logging.info(f"using DDS = {dataset_service}")
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dataset_id, job_name = register_dataset(dataset=dataset, dataset_service=dataset_service)
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dataset = distribute_from_dataset_id(
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dataset_service=dataset_service, dataset_id=dataset_id, job_name=job_name
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)
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return dataset
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if __name__ == "__main__":
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maybe_start_dataset_service()
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