damccorm commented on code in PR #25393: URL: https://github.com/apache/beam/pull/25393#discussion_r1101675328
########## sdks/python/apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py: ########## @@ -0,0 +1,205 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. +# + +""" +A pipeline that uses RunInference PTransform to perform image classification +and uses WatchFilePattern as side input to the RunInference PTransform. +WatchFilePattern is used to watch for a file updates matching the file_pattern +based on timestamps and emits latest model metadata, which is used in +RunInference API for the dynamic model updates without the need for stopping +the beam pipeline. + +This pipeline follows the pattern from +https://beam.apache.org/documentation/patterns/side-inputs/ + +This pipeline expects a PubSub topic as source, which emits an image +path(UTF-8 encoded) that is accessible by the pipeline. + +To run the example on DataflowRunner, + +python apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py # pylint: disable=line-too-long + --project=<your-project> + --re=<your-region> + --temp_location=<your-tmp-location> + --staging_location=<your-staging-location> + --runner=DataflowRunner + --streaming + --interval=10 + --num_workers=5 + --requirements_file=apache_beam/ml/inference/torch_tests_requirements.txt + --topic=<pubusb_topic> + --file_pattern=<glob_pattern> Review Comment: ```suggestion --file_pattern=<glob_pattern> ``` Could you add details here about how this file pattern is used (for updates) in a comment? ########## sdks/python/apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py: ########## @@ -0,0 +1,205 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. +# + +""" +A pipeline that uses RunInference PTransform to perform image classification +and uses WatchFilePattern as side input to the RunInference PTransform. +WatchFilePattern is used to watch for a file updates matching the file_pattern +based on timestamps and emits latest model metadata, which is used in +RunInference API for the dynamic model updates without the need for stopping +the beam pipeline. + +This pipeline follows the pattern from +https://beam.apache.org/documentation/patterns/side-inputs/ + +This pipeline expects a PubSub topic as source, which emits an image +path(UTF-8 encoded) that is accessible by the pipeline. + +To run the example on DataflowRunner, + +python apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py # pylint: disable=line-too-long + --project=<your-project> + --re=<your-region> + --temp_location=<your-tmp-location> + --staging_location=<your-staging-location> + --runner=DataflowRunner + --streaming + --interval=10 + --num_workers=5 + --requirements_file=apache_beam/ml/inference/torch_tests_requirements.txt + --topic=<pubusb_topic> + --file_pattern=<glob_pattern> +""" + +import argparse +import io +import logging +import os +from typing import Iterable +from typing import Iterator +from typing import Optional +from typing import Tuple + +import apache_beam as beam +import torch +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor +from apache_beam.ml.inference.utils import WatchFilePattern +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.runners.runner import PipelineResult +from PIL import Image +from torchvision import models +from torchvision import transforms + + +def read_image(image_file_name: str, + path_to_dir: Optional[str] = None) -> Tuple[str, Image.Image]: + if path_to_dir is not None: + image_file_name = os.path.join(path_to_dir, image_file_name) + with FileSystems().open(image_file_name, 'r') as file: + data = Image.open(io.BytesIO(file.read())).convert('RGB') + return image_file_name, data + + +def preprocess_image(data: Image.Image) -> torch.Tensor: + image_size = (224, 224) + # Pre-trained PyTorch models expect input images normalized with the + # below values (see: https://pytorch.org/vision/stable/models.html) + normalize = transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + transform = transforms.Compose([ + transforms.Resize(image_size), + transforms.ToTensor(), + normalize, + ]) + return transform(data) + + +def filter_empty_lines(text: str) -> Iterator[str]: + if len(text.strip()) > 0: + yield text + + +class PostProcessor(beam.DoFn): + """ + Return filename, prediction and the model id used to perform the + prediction + """ + def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]: + filename, prediction_result = element + prediction = torch.argmax(prediction_result.inference, dim=0) + yield filename, prediction, prediction_result.model_id + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--topic', + dest='topic', + help='PubSub topic emitting absolute path to the images.' + 'Path must be accessible by the pipeline.') + parser.add_argument( + '--model_path', Review Comment: Nit: Maybe `initial_model_path`? Could we also update the description to say something like: `Path to the initial model's state_dict. This will be used until the first model update occurs.` ########## sdks/python/apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py: ########## @@ -0,0 +1,205 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. +# + +""" +A pipeline that uses RunInference PTransform to perform image classification +and uses WatchFilePattern as side input to the RunInference PTransform. +WatchFilePattern is used to watch for a file updates matching the file_pattern +based on timestamps and emits latest model metadata, which is used in +RunInference API for the dynamic model updates without the need for stopping +the beam pipeline. + +This pipeline follows the pattern from +https://beam.apache.org/documentation/patterns/side-inputs/ + +This pipeline expects a PubSub topic as source, which emits an image +path(UTF-8 encoded) that is accessible by the pipeline. + +To run the example on DataflowRunner, + +python apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py # pylint: disable=line-too-long + --project=<your-project> + --re=<your-region> + --temp_location=<your-tmp-location> + --staging_location=<your-staging-location> + --runner=DataflowRunner + --streaming + --interval=10 + --num_workers=5 + --requirements_file=apache_beam/ml/inference/torch_tests_requirements.txt + --topic=<pubusb_topic> + --file_pattern=<glob_pattern> +""" + +import argparse +import io +import logging +import os +from typing import Iterable +from typing import Iterator +from typing import Optional +from typing import Tuple + +import apache_beam as beam +import torch +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor +from apache_beam.ml.inference.utils import WatchFilePattern +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.runners.runner import PipelineResult +from PIL import Image +from torchvision import models +from torchvision import transforms + + +def read_image(image_file_name: str, + path_to_dir: Optional[str] = None) -> Tuple[str, Image.Image]: + if path_to_dir is not None: + image_file_name = os.path.join(path_to_dir, image_file_name) + with FileSystems().open(image_file_name, 'r') as file: + data = Image.open(io.BytesIO(file.read())).convert('RGB') + return image_file_name, data + + +def preprocess_image(data: Image.Image) -> torch.Tensor: + image_size = (224, 224) + # Pre-trained PyTorch models expect input images normalized with the + # below values (see: https://pytorch.org/vision/stable/models.html) + normalize = transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + transform = transforms.Compose([ + transforms.Resize(image_size), + transforms.ToTensor(), + normalize, + ]) + return transform(data) + + +def filter_empty_lines(text: str) -> Iterator[str]: + if len(text.strip()) > 0: + yield text + + +class PostProcessor(beam.DoFn): + """ + Return filename, prediction and the model id used to perform the + prediction + """ + def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]: + filename, prediction_result = element + prediction = torch.argmax(prediction_result.inference, dim=0) + yield filename, prediction, prediction_result.model_id + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--topic', + dest='topic', + help='PubSub topic emitting absolute path to the images.' + 'Path must be accessible by the pipeline.') + parser.add_argument( + '--model_path', + dest='model_path', + default='gs://apache-beam-samples/run_inference/resnet152.pth', + help="Path to the model's state_dict.") + parser.add_argument( + '--file_pattern', help='Glob pattern to watch for an update.') + parser.add_argument( + '--interval', + default=10, + type=int, + help='Interval used to check for file updates.') + + return parser.parse_known_args(argv) + + +def run( + argv=None, + model_class=None, + model_params=None, + save_main_session=True, + device='CPU', + test_pipeline=None) -> PipelineResult: + """ + Args: + argv: Command line arguments defined for this example. + model_class: Reference to the class definition of the model. + model_params: Parameters passed to the constructor of the model_class. + These will be used to instantiate the model object in the + RunInference PTransform. + save_main_session: Used for internal testing. + device: Device to be used on the Runner. Choices are (CPU, GPU). + test_pipeline: Used for internal testing. + """ + known_args, pipeline_args = parse_known_args(argv) + pipeline_options = PipelineOptions(pipeline_args) + pipeline_options.view_as(SetupOptions).save_main_session = save_main_session + + if not model_class: + model_class = models.resnet152 + model_params = {'num_classes': 1000} + + class PytorchModelHandlerTensorWithBatchSize(PytorchModelHandlerTensor): + def batch_elements_kwargs(self): Review Comment: Can we use the batch config options now? ########## sdks/python/apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py: ########## @@ -0,0 +1,205 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. +# + +""" +A pipeline that uses RunInference PTransform to perform image classification +and uses WatchFilePattern as side input to the RunInference PTransform. +WatchFilePattern is used to watch for a file updates matching the file_pattern +based on timestamps and emits latest model metadata, which is used in +RunInference API for the dynamic model updates without the need for stopping +the beam pipeline. + +This pipeline follows the pattern from +https://beam.apache.org/documentation/patterns/side-inputs/ + +This pipeline expects a PubSub topic as source, which emits an image +path(UTF-8 encoded) that is accessible by the pipeline. + +To run the example on DataflowRunner, + +python apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py # pylint: disable=line-too-long + --project=<your-project> + --re=<your-region> + --temp_location=<your-tmp-location> + --staging_location=<your-staging-location> + --runner=DataflowRunner + --streaming + --interval=10 + --num_workers=5 + --requirements_file=apache_beam/ml/inference/torch_tests_requirements.txt + --topic=<pubusb_topic> Review Comment: ```suggestion --topic=<pubsub_topic> ``` ########## sdks/python/apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py: ########## @@ -0,0 +1,205 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. +# + +""" +A pipeline that uses RunInference PTransform to perform image classification +and uses WatchFilePattern as side input to the RunInference PTransform. +WatchFilePattern is used to watch for a file updates matching the file_pattern +based on timestamps and emits latest model metadata, which is used in +RunInference API for the dynamic model updates without the need for stopping +the beam pipeline. + +This pipeline follows the pattern from +https://beam.apache.org/documentation/patterns/side-inputs/ + +This pipeline expects a PubSub topic as source, which emits an image +path(UTF-8 encoded) that is accessible by the pipeline. + +To run the example on DataflowRunner, + +python apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py # pylint: disable=line-too-long + --project=<your-project> + --re=<your-region> + --temp_location=<your-tmp-location> + --staging_location=<your-staging-location> + --runner=DataflowRunner + --streaming + --interval=10 + --num_workers=5 + --requirements_file=apache_beam/ml/inference/torch_tests_requirements.txt + --topic=<pubusb_topic> + --file_pattern=<glob_pattern> +""" + +import argparse +import io +import logging +import os +from typing import Iterable +from typing import Iterator +from typing import Optional +from typing import Tuple + +import apache_beam as beam +import torch +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor +from apache_beam.ml.inference.utils import WatchFilePattern +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.runners.runner import PipelineResult +from PIL import Image +from torchvision import models +from torchvision import transforms + + +def read_image(image_file_name: str, + path_to_dir: Optional[str] = None) -> Tuple[str, Image.Image]: + if path_to_dir is not None: + image_file_name = os.path.join(path_to_dir, image_file_name) + with FileSystems().open(image_file_name, 'r') as file: + data = Image.open(io.BytesIO(file.read())).convert('RGB') + return image_file_name, data + + +def preprocess_image(data: Image.Image) -> torch.Tensor: + image_size = (224, 224) + # Pre-trained PyTorch models expect input images normalized with the + # below values (see: https://pytorch.org/vision/stable/models.html) + normalize = transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + transform = transforms.Compose([ + transforms.Resize(image_size), + transforms.ToTensor(), + normalize, + ]) + return transform(data) + + +def filter_empty_lines(text: str) -> Iterator[str]: + if len(text.strip()) > 0: + yield text + + +class PostProcessor(beam.DoFn): + """ + Return filename, prediction and the model id used to perform the + prediction + """ + def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]: + filename, prediction_result = element + prediction = torch.argmax(prediction_result.inference, dim=0) + yield filename, prediction, prediction_result.model_id + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--topic', + dest='topic', + help='PubSub topic emitting absolute path to the images.' + 'Path must be accessible by the pipeline.') + parser.add_argument( + '--model_path', + dest='model_path', + default='gs://apache-beam-samples/run_inference/resnet152.pth', + help="Path to the model's state_dict.") + parser.add_argument( + '--file_pattern', help='Glob pattern to watch for an update.') + parser.add_argument( + '--interval', + default=10, + type=int, + help='Interval used to check for file updates.') + + return parser.parse_known_args(argv) + + +def run( + argv=None, + model_class=None, + model_params=None, + save_main_session=True, + device='CPU', + test_pipeline=None) -> PipelineResult: + """ + Args: + argv: Command line arguments defined for this example. + model_class: Reference to the class definition of the model. + model_params: Parameters passed to the constructor of the model_class. + These will be used to instantiate the model object in the + RunInference PTransform. + save_main_session: Used for internal testing. + device: Device to be used on the Runner. Choices are (CPU, GPU). + test_pipeline: Used for internal testing. + """ + known_args, pipeline_args = parse_known_args(argv) + pipeline_options = PipelineOptions(pipeline_args) + pipeline_options.view_as(SetupOptions).save_main_session = save_main_session + + if not model_class: + model_class = models.resnet152 + model_params = {'num_classes': 1000} + + class PytorchModelHandlerTensorWithBatchSize(PytorchModelHandlerTensor): + def batch_elements_kwargs(self): + return {'min_batch_size': 10, 'max_batch_size': 100} + + # In this example we pass keyed inputs to RunInference transform. + # Therefore, we use KeyedModelHandler wrapper over PytorchModelHandler. + model_handler = KeyedModelHandler( + PytorchModelHandlerTensorWithBatchSize( + state_dict_path=known_args.model_path, + model_class=model_class, + model_params=model_params, + device=device)) + + pipeline = test_pipeline + if not test_pipeline: + pipeline = beam.Pipeline(options=pipeline_options) + + side_input = pipeline | WatchFilePattern( + interval=known_args.interval, file_pattern=known_args.file_pattern) Review Comment: This is awesome, just 2 lines and an additional arg to RunInference to do this! ########## sdks/python/apache_beam/ml/inference/utils.py: ########## @@ -46,3 +61,101 @@ def _convert_to_result( y in zip(batch, predictions_per_tensor) ] return [PredictionResult(x, y, model_id) for x, y in zip(batch, predictions)] + + +class _ConvertIterToSingleton(beam.DoFn): + """ + Internal only; No backwards compatibility. + + The MatchContinuously transform examines all files present in a given + directory and returns those that have timestamps older than the + pipeline's start time. This can produce an Iterable rather than a + Singleton. This class only returns the file path when it is first + encountered, and it is cached as part of the side input caching mechanism. + If the path is seen again, it will not return anything. + By doing this, we can ensure that the output of this transform can be wrapped + with beam.pvalue.AsSingleton(). + """ + COUNT_STATE = CombiningValueStateSpec('count', combine_fn=sum) + + def process(self, element, count_state=beam.DoFn.StateParam(COUNT_STATE)): + counter = count_state.read() + if counter == 0: + count_state.add(1) + yield element[1] + + +class _GetLatestFileByTimeStamp(beam.DoFn): + """ + Internal only; No backwards compatibility. + + This DoFn checks the timestamps of files against the time that the pipeline + began running. It returns the files that were modified after the pipeline + started. If no such files are found, it returns a default file as fallback. + """ + TIME_STATE = CombiningValueStateSpec( + 'count', combine_fn=partial(max, default=_START_TIME_STAMP)) Review Comment: ```suggestion 'max', combine_fn=partial(max, default=_START_TIME_STAMP)) ``` Nit ########## sdks/python/apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py: ########## @@ -0,0 +1,205 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. +# + +""" +A pipeline that uses RunInference PTransform to perform image classification +and uses WatchFilePattern as side input to the RunInference PTransform. +WatchFilePattern is used to watch for a file updates matching the file_pattern +based on timestamps and emits latest model metadata, which is used in +RunInference API for the dynamic model updates without the need for stopping +the beam pipeline. + +This pipeline follows the pattern from +https://beam.apache.org/documentation/patterns/side-inputs/ + +This pipeline expects a PubSub topic as source, which emits an image +path(UTF-8 encoded) that is accessible by the pipeline. Review Comment: Could you add more detail on how this is emitted? Basically, something like "if you want to use this, publish messages to the topic specified in your args, formatted as a gcs string..." ########## sdks/python/apache_beam/ml/inference/utils.py: ########## @@ -46,3 +61,101 @@ def _convert_to_result( y in zip(batch, predictions_per_tensor) ] return [PredictionResult(x, y, model_id) for x, y in zip(batch, predictions)] + + +class _ConvertIterToSingleton(beam.DoFn): + """ + Internal only; No backwards compatibility. + + The MatchContinuously transform examines all files present in a given + directory and returns those that have timestamps older than the + pipeline's start time. This can produce an Iterable rather than a + Singleton. This class only returns the file path when it is first + encountered, and it is cached as part of the side input caching mechanism. + If the path is seen again, it will not return anything. + By doing this, we can ensure that the output of this transform can be wrapped + with beam.pvalue.AsSingleton(). + """ + COUNT_STATE = CombiningValueStateSpec('count', combine_fn=sum) + + def process(self, element, count_state=beam.DoFn.StateParam(COUNT_STATE)): + counter = count_state.read() + if counter == 0: + count_state.add(1) + yield element[1] + + +class _GetLatestFileByTimeStamp(beam.DoFn): + """ + Internal only; No backwards compatibility. + + This DoFn checks the timestamps of files against the time that the pipeline + began running. It returns the files that were modified after the pipeline + started. If no such files are found, it returns a default file as fallback. + """ + TIME_STATE = CombiningValueStateSpec( + 'count', combine_fn=partial(max, default=_START_TIME_STAMP)) + + def process( + self, element, time_state=beam.DoFn.StateParam(TIME_STATE) + ) -> List[Tuple[str, ModelMetdata]]: + _, file_metadata = element + new_ts = file_metadata.last_updated_in_seconds + old_ts = time_state.read() + if new_ts > old_ts: + # time_state.clear() Review Comment: ```suggestion ``` Nit: ########## sdks/python/apache_beam/ml/inference/utils.py: ########## @@ -46,3 +61,101 @@ def _convert_to_result( y in zip(batch, predictions_per_tensor) ] return [PredictionResult(x, y, model_id) for x, y in zip(batch, predictions)] + + +class _ConvertIterToSingleton(beam.DoFn): + """ + Internal only; No backwards compatibility. + + The MatchContinuously transform examines all files present in a given + directory and returns those that have timestamps older than the + pipeline's start time. This can produce an Iterable rather than a + Singleton. This class only returns the file path when it is first + encountered, and it is cached as part of the side input caching mechanism. + If the path is seen again, it will not return anything. + By doing this, we can ensure that the output of this transform can be wrapped + with beam.pvalue.AsSingleton(). + """ + COUNT_STATE = CombiningValueStateSpec('count', combine_fn=sum) + + def process(self, element, count_state=beam.DoFn.StateParam(COUNT_STATE)): + counter = count_state.read() + if counter == 0: + count_state.add(1) + yield element[1] + + +class _GetLatestFileByTimeStamp(beam.DoFn): + """ + Internal only; No backwards compatibility. + + This DoFn checks the timestamps of files against the time that the pipeline + began running. It returns the files that were modified after the pipeline + started. If no such files are found, it returns a default file as fallback. + """ + TIME_STATE = CombiningValueStateSpec( + 'count', combine_fn=partial(max, default=_START_TIME_STAMP)) + + def process( + self, element, time_state=beam.DoFn.StateParam(TIME_STATE) + ) -> List[Tuple[str, ModelMetdata]]: + _, file_metadata = element + new_ts = file_metadata.last_updated_in_seconds + old_ts = time_state.read() + if new_ts > old_ts: + # time_state.clear() + time_state.add(new_ts) + model_path = file_metadata.path + else: + model_path = '' + + model_name = os.path.splitext(os.path.basename(model_path))[0] + return [ + (model_path, ModelMetdata(model_id=model_path, model_name=model_name)) + ] + + +class WatchFilePattern(beam.PTransform): + def __init__( + self, + file_pattern, + interval=360, + stop_timestamp=MAX_TIMESTAMP, + ): + """ + Watches a directory for updates to files matching a given file pattern. Review Comment: We probably need a note in here about not having an empty file_pattern since we're using `EmptyMatchTreatment.DISALLOW` ########## sdks/python/apache_beam/ml/inference/utils_test.py: ########## @@ -0,0 +1,95 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. +# +# pytype: skip-file + +import unittest + +import apache_beam as beam +from apache_beam.io.filesystem import FileMetadata +from apache_beam.ml.inference import utils +from apache_beam.testing.test_pipeline import TestPipeline +from apache_beam.testing.util import assert_that +from apache_beam.testing.util import equal_to + + +class WatchFilePatternTest(unittest.TestCase): + def test_latest_file_by_timestamp_default_value(self): + # match continuously returns the files in sorted timestamp order. + main_input_pcoll = [ + FileMetadata( + 'path1.py', + 10, + last_updated_in_seconds=utils._START_TIME_STAMP - 20), + FileMetadata( + 'path2.py', + 10, + last_updated_in_seconds=utils._START_TIME_STAMP - 10) + ] + with TestPipeline() as p: + files_pc = ( + p + | beam.Create(main_input_pcoll) + | beam.Map(lambda x: (x.path, x)) + | beam.ParDo(utils._GetLatestFileByTimeStamp()) + | beam.Map(lambda x: x[0])) + assert_that(files_pc, equal_to(['', ''])) + + def test_latest_file_with_timestamp_after_pipeline_construction_time(self): + main_input_pcoll = [ + FileMetadata( + 'path1.py', + 10, + last_updated_in_seconds=utils._START_TIME_STAMP + 10) + ] + with TestPipeline() as p: + files_pc = ( + p + | beam.Create(main_input_pcoll) + | beam.Map(lambda x: (x.path, x)) + | beam.ParDo(utils._GetLatestFileByTimeStamp()) + | beam.Map(lambda x: x[0])) + assert_that(files_pc, equal_to(['path1.py'])) + + def test_emitting_singleton_output(self): + # match continuously returns the files in sorted timestamp order. + main_input_pcoll = [ + FileMetadata( + 'path1.py', + 10, + last_updated_in_seconds=utils._START_TIME_STAMP - 20), + # returns default + FileMetadata( + 'path2.py', + 10, + last_updated_in_seconds=utils._START_TIME_STAMP - 10), + # returns default + FileMetadata( + 'path3.py', + 10, + last_updated_in_seconds=utils._START_TIME_STAMP + 10) Review Comment: Could you add one more `FileMetadata` updated at `utils._START_TIME_STAMP + 20` just to exercise the multiple updates functionality (I don't see any reason it wouldn't equal `['', 'path3.py', 'path4.py']` but would like the extra validation + guardrails if we ever refactor -- This is an automated message from the Apache Git Service. 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