mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-11-05 16:25:08 +01:00
123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
"""Dataset service orchestrated by a TFJob
|
|
"""
|
|
from typing import Optional
|
|
import uuid
|
|
|
|
from tml.ml_logging.torch_logging import logging
|
|
import tml.machines.environment as env
|
|
|
|
import packaging.version
|
|
import tensorflow as tf
|
|
|
|
try:
|
|
import tensorflow_io as tfio
|
|
except:
|
|
pass
|
|
from tensorflow.python.data.experimental.ops.data_service_ops import (
|
|
_from_dataset_id,
|
|
_register_dataset,
|
|
)
|
|
import torch.distributed as dist
|
|
|
|
|
|
def maybe_start_dataset_service():
|
|
if not env.has_readers():
|
|
return
|
|
|
|
if packaging.version.parse(tf.__version__) < packaging.version.parse("2.5"):
|
|
raise Exception(f"maybe_distribute_dataset requires TF >= 2.5; got {tf.__version__}")
|
|
|
|
if env.is_dispatcher():
|
|
logging.info(f"env.get_reader_port() = {env.get_reader_port()}")
|
|
logging.info(f"env.get_dds_journaling_dir() = {env.get_dds_journaling_dir()}")
|
|
work_dir = env.get_dds_journaling_dir()
|
|
server = tf.data.experimental.service.DispatchServer(
|
|
tf.data.experimental.service.DispatcherConfig(
|
|
port=env.get_reader_port(),
|
|
protocol="grpc",
|
|
work_dir=work_dir,
|
|
fault_tolerant_mode=bool(work_dir),
|
|
)
|
|
)
|
|
server.join()
|
|
|
|
elif env.is_reader():
|
|
logging.info(f"env.get_reader_port() = {env.get_reader_port()}")
|
|
logging.info(f"env.get_dds_dispatcher_address() = {env.get_dds_dispatcher_address()}")
|
|
logging.info(f"env.get_dds_worker_address() = {env.get_dds_worker_address()}")
|
|
server = tf.data.experimental.service.WorkerServer(
|
|
tf.data.experimental.service.WorkerConfig(
|
|
port=env.get_reader_port(),
|
|
dispatcher_address=env.get_dds_dispatcher_address(),
|
|
worker_address=env.get_dds_worker_address(),
|
|
protocol="grpc",
|
|
)
|
|
)
|
|
server.join()
|
|
|
|
|
|
def register_dataset(
|
|
dataset: tf.data.Dataset, dataset_service: str, compression: Optional[str] = "AUTO"
|
|
):
|
|
if dist.get_rank() == 0:
|
|
dataset_id = _register_dataset(
|
|
service=dataset_service,
|
|
dataset=dataset,
|
|
compression=compression,
|
|
)
|
|
job_name = uuid.uuid4().hex[:8]
|
|
id_and_job = [dataset_id.numpy(), job_name]
|
|
logging.info(f"rank{dist.get_rank()}: Created dds job with {dataset_id.numpy()}, {job_name}")
|
|
else:
|
|
id_and_job = [None, None]
|
|
|
|
dist.broadcast_object_list(id_and_job, src=0)
|
|
return tuple(id_and_job)
|
|
|
|
|
|
def distribute_from_dataset_id(
|
|
dataset_service: str,
|
|
dataset_id: int,
|
|
job_name: Optional[str],
|
|
compression: Optional[str] = "AUTO",
|
|
prefetch: Optional[int] = tf.data.experimental.AUTOTUNE,
|
|
) -> tf.data.Dataset:
|
|
logging.info(f"rank{dist.get_rank()}: Consuming dds job with {dataset_id}, {job_name}")
|
|
dataset = _from_dataset_id(
|
|
processing_mode="parallel_epochs",
|
|
service=dataset_service,
|
|
dataset_id=dataset_id,
|
|
job_name=job_name,
|
|
element_spec=None,
|
|
compression=compression,
|
|
)
|
|
if prefetch is not None:
|
|
dataset = dataset.prefetch(prefetch)
|
|
return dataset
|
|
|
|
|
|
def maybe_distribute_dataset(dataset: tf.data.Dataset) -> tf.data.Dataset:
|
|
"""Torch-compatible and distributed-training-aware dataset service distributor.
|
|
|
|
- rank 0 process will register the given dataset.
|
|
- rank 0 process will broadcast job name and dataset id.
|
|
- all rank processes will consume from the same job/dataset.
|
|
|
|
Without this, dataset workers will try to serve 1 job per rank process and OOM.
|
|
|
|
"""
|
|
if not env.has_readers():
|
|
return dataset
|
|
dataset_service = env.get_dds()
|
|
|
|
logging.info(f"using DDS = {dataset_service}")
|
|
dataset_id, job_name = register_dataset(dataset=dataset, dataset_service=dataset_service)
|
|
dataset = distribute_from_dataset_id(
|
|
dataset_service=dataset_service, dataset_id=dataset_id, job_name=job_name
|
|
)
|
|
return dataset
|
|
|
|
|
|
if __name__ == "__main__":
|
|
maybe_start_dataset_service()
|