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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/06/10 15:58:00 UTC
[GitHub] [beam] TheNeuralBit commented on a diff in pull request #21758: Add README for image classification example
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
```
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