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__all__ = ['ArtifactsFetcher']
import os
import tempfile
from google.cloud.storage import Client
from google.cloud.storage import transfer_manager
import tensorflow_transform as tft
from apache_beam.ml.transforms import base
def download_artifacts_from_gcs(bucket_name, prefix, local_path):
"""Downloads artifacts from GCS to the local file system.
Args:
bucket_name: The name of the GCS bucket to download from.
prefix: Prefix of GCS objects to download.
local_path: The local path to download the folder to.
"""
client = Client()
bucket = client.get_bucket(bucket_name)
blobs = [blob.name for blob in bucket.list_blobs(prefix=prefix)]
_ = transfer_manager.download_many_to_path(
bucket, blobs, destination_directory=local_path)
[docs]
class ArtifactsFetcher:
"""
Utility class used to fetch artifacts from the artifact_location passed
to the TFTProcessHandlers in MLTransform.
This is intended to be used for testing purposes only.
"""
def __init__(self, artifact_location: str):
tempdir = tempfile.mkdtemp()
if artifact_location.startswith('gs://'):
parts = artifact_location[5:].split('/')
bucket_name = parts[0]
prefix = '/'.join(parts[1:])
download_artifacts_from_gcs(bucket_name, prefix, tempdir)
assert os.listdir(tempdir), f"No files found in {artifact_location}"
artifact_location = os.path.join(tempdir, prefix)
files = os.listdir(artifact_location)
files.remove(base._ATTRIBUTE_FILE_NAME)
# TODO: https://github.com/apache/beam/issues/29356
# Integrate ArtifactFetcher into MLTransform.
if len(files) > 1:
raise NotImplementedError(
"MLTransform may have been utilized alongside transforms written "
"in TensorFlow Transform, in conjunction with those from different "
"frameworks. Currently, retrieving artifacts from this "
"multi-framework setup is not supported.")
self._artifact_location = os.path.join(artifact_location, files[0])
self.transform_output = tft.TFTransformOutput(self._artifact_location)
[docs]
def get_vocab_list(self, vocab_filename: str) -> list[bytes]:
"""
Returns list of vocabulary terms created during MLTransform.
"""
try:
vocab_list = self.transform_output.vocabulary_by_name(vocab_filename)
except ValueError as e:
raise ValueError(
'Vocabulary file {} not found in artifact location'.format(
vocab_filename)) from e
return [x.decode('utf-8') for x in vocab_list]
[docs]
def get_vocab_filepath(self, vocab_filename: str) -> str:
"""
Return the path to the vocabulary file created during MLTransform.
"""
return self.transform_output.vocabulary_file_by_name(vocab_filename)
[docs]
def get_vocab_size(self, vocab_filename: str) -> int:
return self.transform_output.vocabulary_size_by_name(vocab_filename)