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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/06/08 21:51:22 UTC
[GitHub] [beam] ryanthompson591 commented on a diff in pull request #21758: Add README for image classification example
ryanthompson591 commented on code in PR #21758:
URL: https://github.com/apache/beam/pull/21758#discussion_r892890839
##########
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.
Review Comment:
This sentence needs to be fixed.
##########
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
Review Comment:
Maybe move this up to a torch dep section.
##########
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:
Review Comment:
Not all users will be using pytorch. Maybe change this sentence to:
To use pytorch, first install pytorch:
##########
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:
3 or 4 lines should be adequate.
##########
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:
it would be cool if one of the ptransforms in the example joined to integer prediction to the actual name of the image.
for example:
gs://apache-beam-ml/datasets/.....5102.jpeg, horse
gs://apache-beam-ml/datasets/.....5102.jpeg, cheese
etc.
But that is outside of the scope of this PR.
##########
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
Review Comment:
Is this a todo? Maybe add todo here if so.
##########
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:
Review Comment:
I don't know gs://apache-beam-ml will really work as a link or is right.
maybe "staged in apache-beam-testing" will work
Feel free to keep it this way if I'm just wrong or misunderstanding.
Maybe this will link right if the users cloud account is set to apache-beam-testing.
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