TheNeuralBit commented on code in PR #21758: URL: https://github.com/apache/beam/pull/21758#discussion_r894667985
########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -0,0 +1,111 @@ +<!-- + 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. +``` +pip install apache-beam==2.40.0 +``` + +### Pytorch dependencies +The RunInference API has support for the Pytorch framework. To use Pytorch locally, first install `torch`. +``` +pip install torch==1.11.0 +``` + +For installation of the `torch` dependency for Dataflow pipelines, refer to these +[instructions](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#pypi-dependencies). + +<!--- +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." +--> + +### Datasets and Models for RunInference +Data related to RunInference has been staged in +`gs://apache-beam-ml/` for use with these example pipelines. You can see this by using the [gsutil tool](https://cloud.google.com/storage/docs/gsutil#gettingstarted). Review Comment: (optional) maybe link to the cloud console here: https://pantheon.corp.google.com/storage/browser/apache-beam-ml ########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -0,0 +1,111 @@ +<!-- + 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. +``` +pip install apache-beam==2.40.0 +``` + +### Pytorch dependencies +The RunInference API has support for the Pytorch framework. To use Pytorch locally, first install `torch`. +``` +pip install torch==1.11.0 +``` + +For installation of the `torch` dependency for Dataflow pipelines, refer to these +[instructions](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#pypi-dependencies). + +<!--- +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." +--> + +### Datasets and Models for RunInference +Data related to RunInference has been staged in +`gs://apache-beam-ml/` for use with these example pipelines. You can see this by using the [gsutil tool](https://cloud.google.com/storage/docs/gsutil#gettingstarted). +``` +gsutil ls gs://apache-beam-ml +``` + +--- +## 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. + +### Dataset and model for Image Classification + +<!--- +TODO: Add once benchmark test is released +- `gs://apache-beam-ml/testing/inputs/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/it_imagenet_validation_inputs.txt/`: Review Comment: ```suggestion - `gs://apache-beam-ml/testing/inputs/it_imagenet_validation_inputs.txt`: ``` ########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -0,0 +1,111 @@ +<!-- + 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. +``` +pip install apache-beam==2.40.0 +``` + +### Pytorch dependencies +The RunInference API has support for the Pytorch framework. To use Pytorch locally, first install `torch`. +``` +pip install torch==1.11.0 +``` + +For installation of the `torch` dependency for Dataflow pipelines, refer to these +[instructions](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#pypi-dependencies). + +<!--- +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." +--> + +### Datasets and Models for RunInference +Data related to RunInference has been staged in +`gs://apache-beam-ml/` for use with these example pipelines. You can see this by using the [gsutil tool](https://cloud.google.com/storage/docs/gsutil#gettingstarted). +``` +gsutil ls gs://apache-beam-ml +``` + +--- +## 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. + +### Dataset and model for Image Classification + +<!--- +TODO: Add once benchmark test is released +- `gs://apache-beam-ml/testing/inputs/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/it_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 Review Comment: (optional) It might be nice to clarify that these sub-bullets are the file contents with something like: ```sh $ gsutil cat gs://apache-beam-ml/testing/inputs/it_imagenet_validation_inputs.txt 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 ``` -- This is an automated message from the Apache Git Service. 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