damccorm commented on code in PR #38917: URL: https://github.com/apache/beam/pull/38917#discussion_r3499769482
########## sdks/python/apache_beam/testing/benchmarks/inference/mltransform_image_embedding_benchmark.py: ########## @@ -0,0 +1,227 @@ +# +# 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. +# + +import logging +from typing import Optional + +from google.cloud import monitoring_v3 +from google.protobuf.duration_pb2 import Duration + +from apache_beam.examples.ml_transform import mltransform_image_embedding +from apache_beam.options.pipeline_options import DebugOptions +from apache_beam.options.pipeline_options import GoogleCloudOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.options.pipeline_options import WorkerOptions +from apache_beam.testing.load_tests import dataflow_cost_consts as costs +from apache_beam.testing.load_tests.dataflow_cost_benchmark import DataflowCostBenchmark +from apache_beam.testing.load_tests.load_test import LoadTestOptions + + +class MLTransformImageEmbeddingOptions( + LoadTestOptions, + StandardOptions, + GoogleCloudOptions, + WorkerOptions, + DebugOptions, + SetupOptions, +): + @classmethod + def _add_argparse_args(cls, parser): + parser.add_argument('--mode', default='batch') + parser.add_argument('--input', default='') + parser.add_argument('--input_file', default='') + parser.add_argument('--output_table', default='') + parser.add_argument('--artifact_location', default='') + parser.add_argument( + '--pretrained_model_name', + default=mltransform_image_embedding.DEFAULT_IMAGE_MODEL_NAME) + parser.add_argument('--device', default='CPU') + parser.add_argument('--min_batch_size', type=int, default=8) + parser.add_argument('--max_batch_size', type=int, default=64) + parser.add_argument( + '--embedding_accelerator', + default=mltransform_image_embedding.DEFAULT_ACCELERATOR) + parser.add_argument( + '--embedding_min_ram', + default=mltransform_image_embedding.DEFAULT_EMBEDDING_MIN_RAM) + + +class MLTransformImageEmbeddingBenchmarkTest(DataflowCostBenchmark): + options_class = MLTransformImageEmbeddingOptions + + def __init__(self): + self.metrics_namespace = 'BeamML_MLTransform' + super().__init__( + metrics_namespace=self.metrics_namespace, + pcollection='FormatOutput.out0') + self.opts = self.pipeline.get_pipeline_options().view_as( + MLTransformImageEmbeddingOptions) + if self.opts.device == 'GPU': + self.gpu = costs.Accelerator.T4 + + def _get_worker_time_interval( Review Comment: Should this exist on the base class instead? ########## sdks/python/apache_beam/testing/benchmarks/inference/mltransform_image_embedding_benchmark.py: ########## @@ -0,0 +1,227 @@ +# +# 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. +# + +import logging +from typing import Optional + +from google.cloud import monitoring_v3 +from google.protobuf.duration_pb2 import Duration + +from apache_beam.examples.ml_transform import mltransform_image_embedding +from apache_beam.options.pipeline_options import DebugOptions +from apache_beam.options.pipeline_options import GoogleCloudOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.options.pipeline_options import WorkerOptions +from apache_beam.testing.load_tests import dataflow_cost_consts as costs +from apache_beam.testing.load_tests.dataflow_cost_benchmark import DataflowCostBenchmark +from apache_beam.testing.load_tests.load_test import LoadTestOptions + + +class MLTransformImageEmbeddingOptions( + LoadTestOptions, + StandardOptions, + GoogleCloudOptions, + WorkerOptions, + DebugOptions, + SetupOptions, +): + @classmethod + def _add_argparse_args(cls, parser): + parser.add_argument('--mode', default='batch') + parser.add_argument('--input', default='') + parser.add_argument('--input_file', default='') + parser.add_argument('--output_table', default='') + parser.add_argument('--artifact_location', default='') + parser.add_argument( + '--pretrained_model_name', + default=mltransform_image_embedding.DEFAULT_IMAGE_MODEL_NAME) + parser.add_argument('--device', default='CPU') + parser.add_argument('--min_batch_size', type=int, default=8) + parser.add_argument('--max_batch_size', type=int, default=64) + parser.add_argument( + '--embedding_accelerator', + default=mltransform_image_embedding.DEFAULT_ACCELERATOR) + parser.add_argument( + '--embedding_min_ram', + default=mltransform_image_embedding.DEFAULT_EMBEDDING_MIN_RAM) + + +class MLTransformImageEmbeddingBenchmarkTest(DataflowCostBenchmark): + options_class = MLTransformImageEmbeddingOptions + + def __init__(self): + self.metrics_namespace = 'BeamML_MLTransform' + super().__init__( + metrics_namespace=self.metrics_namespace, + pcollection='FormatOutput.out0') + self.opts = self.pipeline.get_pipeline_options().view_as( + MLTransformImageEmbeddingOptions) + if self.opts.device == 'GPU': + self.gpu = costs.Accelerator.T4 + + def _get_worker_time_interval( Review Comment: Same question applies elsewhere. If it needs to be on a different path because it doesn't work for non-GPU jobs, we can make that configurable during __init__ ########## sdks/python/apache_beam/testing/benchmarks/inference/mltransform_image_embedding_benchmark.py: ########## @@ -0,0 +1,227 @@ +# +# 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. +# + +import logging +from typing import Optional + +from google.cloud import monitoring_v3 +from google.protobuf.duration_pb2 import Duration + +from apache_beam.examples.ml_transform import mltransform_image_embedding +from apache_beam.options.pipeline_options import DebugOptions +from apache_beam.options.pipeline_options import GoogleCloudOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.options.pipeline_options import WorkerOptions +from apache_beam.testing.load_tests import dataflow_cost_consts as costs +from apache_beam.testing.load_tests.dataflow_cost_benchmark import DataflowCostBenchmark +from apache_beam.testing.load_tests.load_test import LoadTestOptions + + +class MLTransformImageEmbeddingOptions( + LoadTestOptions, + StandardOptions, + GoogleCloudOptions, + WorkerOptions, + DebugOptions, + SetupOptions, +): + @classmethod + def _add_argparse_args(cls, parser): + parser.add_argument('--mode', default='batch') + parser.add_argument('--input', default='') + parser.add_argument('--input_file', default='') + parser.add_argument('--output_table', default='') + parser.add_argument('--artifact_location', default='') + parser.add_argument( + '--pretrained_model_name', + default=mltransform_image_embedding.DEFAULT_IMAGE_MODEL_NAME) + parser.add_argument('--device', default='CPU') + parser.add_argument('--min_batch_size', type=int, default=8) + parser.add_argument('--max_batch_size', type=int, default=64) + parser.add_argument( + '--embedding_accelerator', + default=mltransform_image_embedding.DEFAULT_ACCELERATOR) + parser.add_argument( + '--embedding_min_ram', + default=mltransform_image_embedding.DEFAULT_EMBEDDING_MIN_RAM) + + +class MLTransformImageEmbeddingBenchmarkTest(DataflowCostBenchmark): + options_class = MLTransformImageEmbeddingOptions + + def __init__(self): + self.metrics_namespace = 'BeamML_MLTransform' + super().__init__( + metrics_namespace=self.metrics_namespace, + pcollection='FormatOutput.out0') + self.opts = self.pipeline.get_pipeline_options().view_as( + MLTransformImageEmbeddingOptions) + if self.opts.device == 'GPU': + self.gpu = costs.Accelerator.T4 + + def _get_worker_time_interval( Review Comment: Seems like we're repeating this, which means we probably at least want to refactor it out -- This is an automated message from the Apache Git Service. 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