yeandy commented on code in PR #21758: URL: https://github.com/apache/beam/pull/21758#discussion_r894645154
########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -0,0 +1,114 @@ +<!-- + 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. +--> + +# Example RunInference API Pipelines + +This module contains example pipelines that use the Beam RunInference +API. <!---TODO: Add link to full documentation on Beam website when it's published.--> + +## Pre-requisites + +You must have `apache-beam>=2.40.0` installed in order to run these pipelines, +because the `apache_beam.examples.inference` module was added in that release. +Using the RunInference API also `torch` to be installed. + +To install for a local pipeline, run: +``` +pip install apache-beam torch==1.11.0 +``` + +To install for a Dataflow pipeline, refer to these +[instructions](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#pypi-dependencies). +You'll need to add `torch` to a `requirements.txt` file, and then run your +pipeline with the following command-line option: +``` +--requirements_file requirements.txt +``` + +<!--- +TODO: Add link to full documentation on Beam website when it's published. + +i.e. "See the +[documentation](https://beam.apache.org/documentation/dsls/dataframes/overview/#pre-requisites) +for details." +--> + +## Image Classification with ImageNet dataset + +[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains +an implementation for a RunInference pipeline thatpeforms image classification +on [ImageNet dataset](https://www.image-net.org/) using the MobileNetV2 +architecture. + +The pipeline reads the images, performs basic preprocessing, passes them to the +PyTorch implementation of RunInference, and then writes the predictions +to a text file in GCS. + +### Data +Data related to RunInference has been staged in +`gs://apache-beam-ml/` for use with these example pipelines: + +<!--- +Add once benchmark test is released +- `gs://apache-beam-ml/testing/inputs/it_mobilenetv2_imagenet_validation_inputs.txt`: + text file containing the GCS paths of the images of all 5000 imagenet validation data + - gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000001.JPEG + - ... + - gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00050000.JPEG +--> +- `gs://apache-beam-ml/testing/inputs/imagenet_validation_inputs.txt/`: + text file containing the GCS paths of the images of a subset of 15 imagenet + validation data + - gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000001.JPEG + - ... + - gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000015.JPEG + +- `gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_*.JPEG`: + JPEG images for the entire validation dataset. + +- `gs://apache-beam-ml/models/imagenet_classification_mobilenet_v2.pt`: Path to + the location of the saved state_dict of the pretrained mobilenet_v2 model + from the `torchvision.models` subdirectory. + +### Running `pytorch_image_classification.py` + +To run the image classification pipeline locally, use the following command: +```sh +python -m apache_beam.examples.inference.pytorch_image_classification \ + --input gs://apache-beam-ml/testing/inputs/it_mobilenetv2_imagenet_validation_inputs.txt \ + --output predictions.csv \ + --model_state_dict_path gs://apache-beam-ml/models/imagenet_classification_mobilenet_v2.pt +``` + +This will write the output to the `predictions.csv` with contents like: +``` +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005002.JPEG,333 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005003.JPEG,711 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005004.JPEG,286 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005005.JPEG,433 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005006.JPEG,290 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005007.JPEG,890 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005008.JPEG,592 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005009.JPEG,406 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005010.JPEG,996 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005011.JPEG,327 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005012.JPEG,573 +``` +where the second item in each line is the integer representing the predicted class of the +image. Review Comment: I have that for a different example https://github.com/apache/beam/pull/21766 ########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -0,0 +1,114 @@ +<!-- + 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. +--> + +# Example RunInference API Pipelines + +This module contains example pipelines that use the Beam RunInference +API. <!---TODO: Add link to full documentation on Beam website when it's published.--> + +## Pre-requisites + +You must have `apache-beam>=2.40.0` installed in order to run these pipelines, +because the `apache_beam.examples.inference` module was added in that release. +Using the RunInference API also `torch` to be installed. + +To install for a local pipeline, run: +``` +pip install apache-beam torch==1.11.0 +``` + +To install for a Dataflow pipeline, refer to these +[instructions](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#pypi-dependencies). +You'll need to add `torch` to a `requirements.txt` file, and then run your +pipeline with the following command-line option: +``` +--requirements_file requirements.txt +``` + +<!--- +TODO: Add link to full documentation on Beam website when it's published. + +i.e. "See the +[documentation](https://beam.apache.org/documentation/dsls/dataframes/overview/#pre-requisites) +for details." +--> + +## Image Classification with ImageNet dataset + +[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains +an implementation for a RunInference pipeline thatpeforms image classification +on [ImageNet dataset](https://www.image-net.org/) using the MobileNetV2 +architecture. + +The pipeline reads the images, performs basic preprocessing, passes them to the +PyTorch implementation of RunInference, and then writes the predictions +to a text file in GCS. + +### Data +Data related to RunInference has been staged in +`gs://apache-beam-ml/` for use with these example pipelines: + +<!--- +Add once benchmark test is released +- `gs://apache-beam-ml/testing/inputs/it_mobilenetv2_imagenet_validation_inputs.txt`: + text file containing the GCS paths of the images of all 5000 imagenet validation data + - gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000001.JPEG + - ... + - gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00050000.JPEG +--> +- `gs://apache-beam-ml/testing/inputs/imagenet_validation_inputs.txt/`: + text file containing the GCS paths of the images of a subset of 15 imagenet + validation data + - gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000001.JPEG + - ... + - gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000015.JPEG + +- `gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_*.JPEG`: + JPEG images for the entire validation dataset. + +- `gs://apache-beam-ml/models/imagenet_classification_mobilenet_v2.pt`: Path to + the location of the saved state_dict of the pretrained mobilenet_v2 model + from the `torchvision.models` subdirectory. + +### Running `pytorch_image_classification.py` + +To run the image classification pipeline locally, use the following command: +```sh +python -m apache_beam.examples.inference.pytorch_image_classification \ + --input gs://apache-beam-ml/testing/inputs/it_mobilenetv2_imagenet_validation_inputs.txt \ + --output predictions.csv \ + --model_state_dict_path gs://apache-beam-ml/models/imagenet_classification_mobilenet_v2.pt +``` + +This will write the output to the `predictions.csv` with contents like: +``` +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005002.JPEG,333 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005003.JPEG,711 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005004.JPEG,286 +gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005005.JPEG,433 Review Comment: Fixed. -- This is an automated message from the Apache Git Service. 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