-
Notifications
You must be signed in to change notification settings - Fork 16
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Beam helper pipelines covering different conversion use-cases
PiperOrigin-RevId: 579985171
- Loading branch information
1 parent
2544e3b
commit 1079137
Showing
1 changed file
with
162 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,162 @@ | ||
"""Various opinionated Beam pipelines for testing different functionality.""" | ||
|
||
import apache_beam as beam | ||
from apache_beam.coders import coders | ||
from . import arrayrecordio | ||
from . import dofns | ||
from . import example | ||
from . import options | ||
|
||
|
||
## Grab CLI arguments. | ||
## Override by passing args/pipeline_options to the function manually. | ||
def_args, def_pipeline_options = options.get_arguments() | ||
|
||
|
||
def example_to_tfrecord( | ||
num_shards=1, | ||
args=def_args, | ||
pipeline_options=def_pipeline_options): | ||
"""Beam pipeline for creating example TFRecord data. | ||
Args: | ||
num_shards: Number of files | ||
args: Custom arguments | ||
pipeline_options: Beam arguments in dict format | ||
Returns: | ||
Beam Pipeline object | ||
""" | ||
|
||
p1 = beam.Pipeline(options=pipeline_options) | ||
initial = (p1 | ||
| 'Create' >> beam.Create(example.generate_movie_examples()) | ||
| 'Write' >> beam.io.WriteToTFRecord( | ||
args['output'], | ||
coder=coders.ToBytesCoder(), | ||
num_shards=num_shards, | ||
file_name_suffix='.tfrecord')) | ||
|
||
return p1, initial | ||
|
||
|
||
def example_to_arrayrecord( | ||
num_shards=1, | ||
args=def_args, | ||
pipeline_options=def_pipeline_options): | ||
"""Beam pipeline for creating example ArrayRecord data. | ||
Args: | ||
num_shards: Number of files | ||
args: Custom arguments | ||
pipeline_options: Beam arguments in dict format | ||
Returns: | ||
Beam Pipeline object | ||
""" | ||
|
||
p1 = beam.Pipeline(options=pipeline_options) | ||
initial = (p1 | ||
| 'Create' >> beam.Create(example.generate_movie_examples()) | ||
| 'Write' >> arrayrecordio.WriteToArrayRecord( | ||
args['output'], | ||
coder=coders.ToBytesCoder(), | ||
num_shards=num_shards, | ||
file_name_suffix='.arrayrecord')) | ||
|
||
return p1, initial | ||
|
||
|
||
def convert_tf_to_arrayrecord_disk( | ||
num_shards=1, | ||
args=def_args, | ||
pipeline_options=def_pipeline_options): | ||
"""Convert TFRecords to ArrayRecords using sink/sharding functionality. | ||
THIS ONLY WORKS FOR DISK ARRAYRECORD WRITES | ||
Args: | ||
num_shards: Number of files | ||
args: Custom arguments | ||
pipeline_options: Beam arguments in dict format | ||
Returns: | ||
Beam Pipeline object | ||
""" | ||
|
||
p1 = beam.Pipeline(options=pipeline_options) | ||
initial = (p1 | ||
| 'Read TFRecord' >> beam.io.ReadFromTFRecord(args['input']) | ||
| 'Write ArrayRecord' >> arrayrecordio.WriteToArrayRecord( | ||
args['output'], | ||
coder=coders.ToBytesCoder(), | ||
num_shards=num_shards, | ||
file_name_suffix='.arrayrecord')) | ||
|
||
return p1, initial | ||
|
||
|
||
def convert_tf_to_arrayrecord_disk_match_shards( | ||
args=def_args, | ||
pipeline_options=def_pipeline_options): | ||
"""Convert TFRecords to matching number of ArrayRecords. | ||
THIS ONLY WORKS FOR DISK ARRAYRECORD WRITES | ||
Args: | ||
args: Custom arguments | ||
pipeline_options: Beam arguments in dict format | ||
Returns: | ||
Beam Pipeline object | ||
""" | ||
|
||
p1 = beam.Pipeline(options=pipeline_options) | ||
initial = (p1 | ||
| 'Start' >> beam.Create([args['input']]) | ||
| 'Read' >> beam.io.ReadAllFromTFRecord(with_filename=True)) | ||
|
||
file_count = (initial | ||
| 'Group' >> beam.GroupByKey() | ||
| 'Count Shards' >> beam.combiners.Count.Globally()) | ||
|
||
write_files = (initial | ||
| 'Drop Filename' >> beam.Map(lambda x: x[1]) | ||
| 'Write ArrayRecord' >> arrayrecordio.WriteToArrayRecord( | ||
args['output'], | ||
coder=coders.ToBytesCoder(), | ||
num_shards=beam.pvalue.AsSingleton(file_count), | ||
file_name_suffix='.arrayrecord')) | ||
|
||
return p1, write_files | ||
|
||
|
||
def convert_tf_to_arrayrecord_gcs( | ||
overwrite_extension=False, | ||
file_path_suffix='.arrayrecord', | ||
args=def_args, | ||
pipeline_options=def_pipeline_options): | ||
"""Convert TFRecords to ArrayRecords in GCS 1:1. | ||
Args: | ||
overwrite_extension: Boolean making DoFn attempt to overwrite extension | ||
file_path_suffix: Intended suffix for overwrite or append | ||
args: Custom arguments | ||
pipeline_options: Beam arguments in dict format | ||
Returns: | ||
Beam Pipeline object | ||
""" | ||
|
||
p1 = beam.Pipeline(options=pipeline_options) | ||
initial = (p1 | ||
| 'Start' >> beam.Create([args['input']]) | ||
| 'Read' >> beam.io.ReadAllFromTFRecord(with_filename=True) | ||
| 'Group' >> beam.GroupByKey() | ||
| 'Write to ArrayRecord in GCS' >> beam.ParDo( | ||
dofns.ConvertToArrayRecordGCS(), | ||
args['output'], | ||
file_path_suffix=file_path_suffix, | ||
overwrite_extension=overwrite_extension)) | ||
|
||
return p1, initial |