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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Rebalance a set of CSV/TFRecord shards to a target number of files.
"""
import argparse
import datetime
import os
import apache_beam as beam
import tensorflow as tf, tf_keras
parser = argparse.ArgumentParser()
parser.add_argument(
"--input_path",
default=None,
required=True,
help="Input path.")
parser.add_argument(
"--output_path",
default=None,
required=True,
help="Output path.")
parser.add_argument(
"--num_output_files",
type=int,
default=256,
help="Number of output file shards.")
parser.add_argument(
"--filetype",
default="tfrecord",
help="File type, needs to be one of {tfrecord, csv}.")
parser.add_argument(
"--project",
default=None,
help="ID (not name) of your project. Ignored by DirectRunner")
parser.add_argument(
"--runner",
help="Runner for Apache Beam, needs to be one of "
"{DirectRunner, DataflowRunner}.",
default="DirectRunner")
parser.add_argument(
"--region",
default=None,
help="region")
args = parser.parse_args()
def rebalance_data_shards():
"""Rebalances data shards."""
def csv_pipeline(pipeline: beam.Pipeline):
"""Rebalances CSV dataset.
Args:
pipeline: Beam pipeline object.
"""
_ = (
pipeline
| beam.io.ReadFromText(args.input_path)
| beam.io.WriteToText(args.output_path,
num_shards=args.num_output_files))
def tfrecord_pipeline(pipeline: beam.Pipeline):
"""Rebalances TFRecords dataset.
Args:
pipeline: Beam pipeline object.
"""
example_coder = beam.coders.ProtoCoder(tf.train.Example)
_ = (
pipeline
| beam.io.ReadFromTFRecord(args.input_path, coder=example_coder)
| beam.io.WriteToTFRecord(args.output_path, file_name_suffix="tfrecord",
coder=example_coder,
num_shards=args.num_output_files))
job_name = (
f"shard-rebalancer-{datetime.datetime.now().strftime('%y%m%d-%H%M%S')}")
# set up Beam pipeline.
options = {
"staging_location": os.path.join(args.output_path, "tmp", "staging"),
"temp_location": os.path.join(args.output_path, "tmp"),
"job_name": job_name,
"project": args.project,
"save_main_session": True,
"region": args.region,
}
opts = beam.pipeline.PipelineOptions(flags=[], **options)
with beam.Pipeline(args.runner, options=opts) as pipeline:
if args.filetype == "tfrecord":
tfrecord_pipeline(pipeline)
elif args.filetype == "csv":
csv_pipeline(pipeline)
if __name__ == "__main__":
rebalance_data_shards()
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