damccorm commented on code in PR #25947: URL: https://github.com/apache/beam/pull/25947#discussion_r1152254375
########## website/www/site/content/en/documentation/ml/side-input-updates.md: ########## @@ -0,0 +1,144 @@ +--- +title: "Auto Update ML models using WatchFilePattern" +--- +<!-- +Licensed 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. +--> + +# Use WatchFilePattern as side input to auto-update ML models in RunInference Review Comment: ```suggestion # Use WatchFilePattern to auto-update ML models in RunInference ``` I think this is a clearer title for the non-beam user. The "how" (side inputs) is sufficiently explained below, so the title can just focus on the behavior the user is trying to achieve. ########## website/www/site/content/en/documentation/ml/side-input-updates.md: ########## @@ -0,0 +1,144 @@ +--- +title: "Auto Update ML models using WatchFilePattern" +--- +<!-- +Licensed 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. +--> + +# Use WatchFilePattern as side input to auto-update ML models in RunInference + +The pipeline in this example uses a [RunInference](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) `PTransform` with a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` that emits `ModelMetadata` to run inferences on images using TensorFlow models. Review Comment: ```suggestion The pipeline in this example uses a [RunInference](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) `PTransform` to run inference on images using TensorFlow models. It uses a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` that emits `ModelMetadata` to update the model. ``` Suggestion for clarity - the current form makes it sound like the `ModelMetadata` (or maybe the side input) is the thing running inference ########## website/www/site/content/en/documentation/ml/side-input-updates.md: ########## @@ -0,0 +1,144 @@ +--- +title: "Auto Update ML models using WatchFilePattern" +--- +<!-- +Licensed 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. +--> + +# Use WatchFilePattern as side input to auto-update ML models in RunInference + +The pipeline in this example uses a [RunInference](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) `PTransform` with a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` that emits `ModelMetadata` to run inferences on images using TensorFlow models. + +Using side inputs, you can update your model (which is passed in the `ModelHandler`) in real-time, even while the Beam pipeline is still running. This can be done either by leveraging one of Beam's provided patterns, such as the `WatchFilePattern`, Review Comment: ```suggestion Using side inputs, you can update your model (which is passed in a `ModelHandler` configuration object) in real-time, even while the Beam pipeline is still running. This can be done either by leveraging one of Beam's provided patterns, such as the `WatchFilePattern`, ``` This mirrors how we talk about `ModelHandlers` elsewhere in our docs ########## website/www/site/layouts/partials/section-menu/en/documentation.html: ########## @@ -226,6 +226,7 @@ <li><a href="/documentation/ml/tensorrt-runinference">Build a custom model handler with TensorRT</a></li> <li><a href="/documentation/ml/large-language-modeling">Use LLM inference</a></li> <li><a href="/documentation/ml/multi-language-inference/">Build a multi-language inference pipeline</a></li> + <li><a href="/documentation/ml/side-input-updates/">Use side inputs to automatically update models</a></li> Review Comment: Same thing, I think focusing on the desired behavior vs the mechanism will help newer users ########## website/www/site/content/en/documentation/ml/side-input-updates.md: ########## @@ -0,0 +1,144 @@ +--- +title: "Auto Update ML models using WatchFilePattern" +--- +<!-- +Licensed 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. +--> + +# Use WatchFilePattern as side input to auto-update ML models in RunInference + +The pipeline in this example uses a [RunInference](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) `PTransform` with a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` that emits `ModelMetadata` to run inferences on images using TensorFlow models. + +Using side inputs, you can update your model (which is passed in the `ModelHandler`) in real-time, even while the Beam pipeline is still running. This can be done either by leveraging one of Beam's provided patterns, such as the `WatchFilePattern`, +or by configuring a custom side input `PCollection` that defines the logic for the model update. + +For more information about side inputs, see the [Side inputs](https://beam.apache.org/documentation/programming-guide/#side-inputs) section in the Apache Beam Programming Guide. + +This example uses [`WatchFilePattern`](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.utils.html#apache_beam.ml.inference.utils.WatchFilePattern) as a side input. `WatchFilePattern` is used to watch for the file updates matching the `file_pattern` +based on timestamps. It emits the latest [`ModelMetadata`](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/), which is used in +the RunInference `PTransform` to automatically update the ML model without stopping the Beam pipeline. + +## Set up the source + +To read the image names, use a Pub/Sub topic as the source. + * The Pub/Sub topic emits a `UTF-8` encoded model path that is used to read and preprocess images to run the inference. + +## Models for image segmentation + +For the purpose of this example, use TensorFlow models saved in [HDF5](https://www.tensorflow.org/tutorials/keras/save_and_load#hdf5_format) format. + + +## Pre-process images for inference +The Pub/Sub topic emits an image path. We need to read and preprocess the image to use it for RunInference. The `read_image` function is used to read the image for inference. + +```python +import io +from PIL import Image +from apache_beam.io.filesystems import FileSystems +import numpy +import tensorflow as tf + +def read_image(image_file_name): + with FileSystems().open(image_file_name, 'r') as file: + data = Image.open(io.BytesIO(file.read())).convert('RGB') + img = data.resize((224, 224)) + img = numpy.array(img) / 255.0 + img_tensor = tf.cast(tf.convert_to_tensor(img[...]), dtype=tf.float32) + return img_tensor +``` + +Now, let's jump into the pipeline code. + +**Pipeline steps**: +1. Get the image names from the Pub/Sub topic. +2. Read and pre-process the images using the `read_image` function. +3. Pass the images to the RunInference `PTransform`. RunInference takes `model_handler` and `model_metadata_pcoll` as input parameters. + +For the [`model_handler`](https://github.com/apache/beam/blob/07f52a478174f8733c7efedb7189955142faa5fa/sdks/python/apache_beam/ml/inference/base.py#L308), we use [TFModelHandlerTensor](https://github.com/apache/beam/blob/186973b110d82838fb8e5ba27f0225a67c336591/sdks/python/apache_beam/ml/inference/tensorflow_inference.py#L184). +```python +from apache_beam.ml.inference.tensorflow_inference import TFModelHandlerTensor +# initialize TFModelHandlerTensor with a .h5 model saved in a directory accessible by the pipeline. +tf_model_handler = TFModelHandlerTensor(model_uri='gs://<your-bucket>/<model_path.h5>') +``` + +The `model_metadata_pcoll` is a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` to the RunInference `PTransform`. This side input is used to update the models in the `model_handler` without needing to stop the beam pipeline. Review Comment: @rszper this following section has a lot of code blocks which collectively make it kinda hard to read IMO (http://apache-beam-website-pull-requests.storage.googleapis.com/25947/documentation/ml/side-input-updates/index.html), but I don't know of a great way around it - any ideas on how we could make this section a little cleaner looking/easier to read? The content itself is correct ########## website/www/site/content/en/documentation/ml/side-input-updates.md: ########## @@ -0,0 +1,144 @@ +--- +title: "Auto Update ML models using WatchFilePattern" +--- +<!-- +Licensed 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. +--> + +# Use WatchFilePattern as side input to auto-update ML models in RunInference + +The pipeline in this example uses a [RunInference](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) `PTransform` with a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` that emits `ModelMetadata` to run inferences on images using TensorFlow models. + +Using side inputs, you can update your model (which is passed in the `ModelHandler`) in real-time, even while the Beam pipeline is still running. This can be done either by leveraging one of Beam's provided patterns, such as the `WatchFilePattern`, +or by configuring a custom side input `PCollection` that defines the logic for the model update. + +For more information about side inputs, see the [Side inputs](https://beam.apache.org/documentation/programming-guide/#side-inputs) section in the Apache Beam Programming Guide. + +This example uses [`WatchFilePattern`](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.utils.html#apache_beam.ml.inference.utils.WatchFilePattern) as a side input. `WatchFilePattern` is used to watch for the file updates matching the `file_pattern` +based on timestamps. It emits the latest [`ModelMetadata`](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/), which is used in +the RunInference `PTransform` to automatically update the ML model without stopping the Beam pipeline. + +## Set up the source + +To read the image names, use a Pub/Sub topic as the source. + * The Pub/Sub topic emits a `UTF-8` encoded model path that is used to read and preprocess images to run the inference. + +## Models for image segmentation + +For the purpose of this example, use TensorFlow models saved in [HDF5](https://www.tensorflow.org/tutorials/keras/save_and_load#hdf5_format) format. + + +## Pre-process images for inference +The Pub/Sub topic emits an image path. We need to read and preprocess the image to use it for RunInference. The `read_image` function is used to read the image for inference. + +```python +import io +from PIL import Image +from apache_beam.io.filesystems import FileSystems +import numpy +import tensorflow as tf + +def read_image(image_file_name): + with FileSystems().open(image_file_name, 'r') as file: + data = Image.open(io.BytesIO(file.read())).convert('RGB') + img = data.resize((224, 224)) + img = numpy.array(img) / 255.0 + img_tensor = tf.cast(tf.convert_to_tensor(img[...]), dtype=tf.float32) + return img_tensor +``` + +Now, let's jump into the pipeline code. + +**Pipeline steps**: +1. Get the image names from the Pub/Sub topic. +2. Read and pre-process the images using the `read_image` function. +3. Pass the images to the RunInference `PTransform`. RunInference takes `model_handler` and `model_metadata_pcoll` as input parameters. + +For the [`model_handler`](https://github.com/apache/beam/blob/07f52a478174f8733c7efedb7189955142faa5fa/sdks/python/apache_beam/ml/inference/base.py#L308), we use [TFModelHandlerTensor](https://github.com/apache/beam/blob/186973b110d82838fb8e5ba27f0225a67c336591/sdks/python/apache_beam/ml/inference/tensorflow_inference.py#L184). +```python +from apache_beam.ml.inference.tensorflow_inference import TFModelHandlerTensor +# initialize TFModelHandlerTensor with a .h5 model saved in a directory accessible by the pipeline. +tf_model_handler = TFModelHandlerTensor(model_uri='gs://<your-bucket>/<model_path.h5>') +``` + +The `model_metadata_pcoll` is a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` to the RunInference `PTransform`. This side input is used to update the models in the `model_handler` without needing to stop the beam pipeline. +We will use `WatchFilePattern` as side input to watch a glob pattern matching `.h5` files. + +`model_metadata_pcoll` expects a `PCollection[ModelMetadata]` compatible with [`AsSingleton`](https://beam.apache.org/releases/pydoc/2.4.0/apache_beam.pvalue.html#apache_beam.pvalue.AsSingleton) view. Because the pipeline uses `WatchFilePattern` as side input, it will take care of windowing and wrapping the output into `ModelMetadata`. + + +After the pipeline starts processing data and when you see some outputs emitted from the RunInference `PTransform`, upload a `.h5` `TensorFlow` model that matches the `file_pattern` to the Google Cloud Storage bucket. RunInference will update the `model_uri` of `TFModelHandlerTensor` using `WatchFilePattern` as a side input. + +**Note**: Side input update frequency is non-deterministic and can have longer intervals between updates. + +```python +import apache_beam as beam +from apache_beam.ml.inference.utils import WatchFilePattern +from apache_beam.ml.inference.base import RunInference +with beam.Pipeline() as pipeline: + + file_pattern = 'gs://<your-bucket>/*.h5' + pubsub_topic = '<topic_emitting_image_names>' + + side_input_pcoll = ( + pipeline + | "FilePatternUpdates" >> WatchFilePattern(file_pattern=file_pattern)) + + images_pcoll = ( + pipeline + | "ReadFromPubSub" >> beam.io.ReadFromPubSub(topic=pubsub_topic) + | "DecodeBytes" >> beam.Map(lambda x: x.decode('utf-8')) + | "PreProcessImage" >> beam.Map(read_image) + ) + + inference_pcoll = ( + images_pcoll + | "RunInference" >> RunInference( + model_handler=tf_model_handler, + model_metadata_pcoll=side_input_pcoll)) + +``` + + +## Post-process the `PredictionResult` object + +When the inference is complete, RunInference outputs a `PredictionResult` object that contains `example`, `inference`, and `model_id`. Here, the `model_id` is used to identify which model is used for running the inference. Review Comment: ```suggestion When the inference is complete, RunInference outputs a `PredictionResult` object that contains `example`, `inference`, and `model_id` fields. The `model_id` is used to identify which model is used for running the inference. ``` ########## website/www/site/content/en/documentation/ml/side-input-updates.md: ########## @@ -0,0 +1,144 @@ +--- +title: "Auto Update ML models using WatchFilePattern" +--- +<!-- +Licensed 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. +--> + +# Use WatchFilePattern as side input to auto-update ML models in RunInference + +The pipeline in this example uses a [RunInference](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) `PTransform` with a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` that emits `ModelMetadata` to run inferences on images using TensorFlow models. + +Using side inputs, you can update your model (which is passed in the `ModelHandler`) in real-time, even while the Beam pipeline is still running. This can be done either by leveraging one of Beam's provided patterns, such as the `WatchFilePattern`, +or by configuring a custom side input `PCollection` that defines the logic for the model update. + +For more information about side inputs, see the [Side inputs](https://beam.apache.org/documentation/programming-guide/#side-inputs) section in the Apache Beam Programming Guide. + +This example uses [`WatchFilePattern`](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.utils.html#apache_beam.ml.inference.utils.WatchFilePattern) as a side input. `WatchFilePattern` is used to watch for the file updates matching the `file_pattern` +based on timestamps. It emits the latest [`ModelMetadata`](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/), which is used in +the RunInference `PTransform` to automatically update the ML model without stopping the Beam pipeline. + +## Set up the source + +To read the image names, use a Pub/Sub topic as the source. + * The Pub/Sub topic emits a `UTF-8` encoded model path that is used to read and preprocess images to run the inference. + +## Models for image segmentation + +For the purpose of this example, use TensorFlow models saved in [HDF5](https://www.tensorflow.org/tutorials/keras/save_and_load#hdf5_format) format. + + +## Pre-process images for inference +The Pub/Sub topic emits an image path. We need to read and preprocess the image to use it for RunInference. The `read_image` function is used to read the image for inference. + +```python +import io +from PIL import Image +from apache_beam.io.filesystems import FileSystems +import numpy +import tensorflow as tf + +def read_image(image_file_name): + with FileSystems().open(image_file_name, 'r') as file: + data = Image.open(io.BytesIO(file.read())).convert('RGB') + img = data.resize((224, 224)) + img = numpy.array(img) / 255.0 + img_tensor = tf.cast(tf.convert_to_tensor(img[...]), dtype=tf.float32) + return img_tensor +``` + +Now, let's jump into the pipeline code. + +**Pipeline steps**: +1. Get the image names from the Pub/Sub topic. +2. Read and pre-process the images using the `read_image` function. +3. Pass the images to the RunInference `PTransform`. RunInference takes `model_handler` and `model_metadata_pcoll` as input parameters. + +For the [`model_handler`](https://github.com/apache/beam/blob/07f52a478174f8733c7efedb7189955142faa5fa/sdks/python/apache_beam/ml/inference/base.py#L308), we use [TFModelHandlerTensor](https://github.com/apache/beam/blob/186973b110d82838fb8e5ba27f0225a67c336591/sdks/python/apache_beam/ml/inference/tensorflow_inference.py#L184). +```python +from apache_beam.ml.inference.tensorflow_inference import TFModelHandlerTensor +# initialize TFModelHandlerTensor with a .h5 model saved in a directory accessible by the pipeline. +tf_model_handler = TFModelHandlerTensor(model_uri='gs://<your-bucket>/<model_path.h5>') +``` + +The `model_metadata_pcoll` is a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` to the RunInference `PTransform`. This side input is used to update the models in the `model_handler` without needing to stop the beam pipeline. +We will use `WatchFilePattern` as side input to watch a glob pattern matching `.h5` files. + +`model_metadata_pcoll` expects a `PCollection[ModelMetadata]` compatible with [`AsSingleton`](https://beam.apache.org/releases/pydoc/2.4.0/apache_beam.pvalue.html#apache_beam.pvalue.AsSingleton) view. Because the pipeline uses `WatchFilePattern` as side input, it will take care of windowing and wrapping the output into `ModelMetadata`. + + +After the pipeline starts processing data and when you see some outputs emitted from the RunInference `PTransform`, upload a `.h5` `TensorFlow` model that matches the `file_pattern` to the Google Cloud Storage bucket. RunInference will update the `model_uri` of `TFModelHandlerTensor` using `WatchFilePattern` as a side input. + +**Note**: Side input update frequency is non-deterministic and can have longer intervals between updates. + +```python +import apache_beam as beam +from apache_beam.ml.inference.utils import WatchFilePattern +from apache_beam.ml.inference.base import RunInference +with beam.Pipeline() as pipeline: + + file_pattern = 'gs://<your-bucket>/*.h5' + pubsub_topic = '<topic_emitting_image_names>' + + side_input_pcoll = ( + pipeline + | "FilePatternUpdates" >> WatchFilePattern(file_pattern=file_pattern)) + + images_pcoll = ( + pipeline + | "ReadFromPubSub" >> beam.io.ReadFromPubSub(topic=pubsub_topic) + | "DecodeBytes" >> beam.Map(lambda x: x.decode('utf-8')) + | "PreProcessImage" >> beam.Map(read_image) + ) + + inference_pcoll = ( + images_pcoll + | "RunInference" >> RunInference( + model_handler=tf_model_handler, + model_metadata_pcoll=side_input_pcoll)) + +``` + + +## Post-process the `PredictionResult` object + +When the inference is complete, RunInference outputs a `PredictionResult` object that contains `example`, `inference`, and `model_id`. Here, the `model_id` is used to identify which model is used for running the inference. + +```python +from apache_beam.ml.inference.base import PredictionResult + +class PostProcessor(beam.DoFn): + """ + Process the PredictionResult to get the predicted label and model id used for inference. + """ + def process(self, element: PredictionResult) -> typing.Iterable[str]: + predicted_class = numpy.argmax(element.inference[0], axis=-1) + labels_path = tf.keras.utils.get_file( + 'ImageNetLabels.txt', + 'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt' + ) + imagenet_labels = numpy.array(open(labels_path).read().splitlines()) + predicted_class_name = imagenet_labels[predicted_class] + return predicted_class_name.title(), element.model_id + +post_processor_pcoll = (inference_pcoll | "PostProcessor" >> PostProcessor()) +``` + +## Run the pipeline +```python +result = pipeline.run().wait_until_finish() +``` +**Note**: The `model_name` of the `ModelMetaData` object will be attached as prefix to the [metrics](https://beam.apache.org/documentation/ml/runinference-metrics/) calculated by the RunInference `PTransform`. + +## Final remarks +Use this example as a pattern when using side inputs with the RunInference `PTransform` to auto-update the models without stopping the pipeline. You can see a similar example for PyTorch on [GitHub](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py). Review Comment: ```suggestion You can use this example as a pattern when using side inputs with the RunInference `PTransform` to auto-update the models without stopping the pipeline. You can see a similar example for PyTorch on [GitHub](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/inference/pytorch_image_classification_with_side_inputs.py). ``` Nit: consistency of style with the next sentence. ########## website/www/site/layouts/partials/section-menu/en/documentation.html: ########## @@ -226,6 +226,7 @@ <li><a href="/documentation/ml/tensorrt-runinference">Build a custom model handler with TensorRT</a></li> <li><a href="/documentation/ml/large-language-modeling">Use LLM inference</a></li> <li><a href="/documentation/ml/multi-language-inference/">Build a multi-language inference pipeline</a></li> + <li><a href="/documentation/ml/side-input-updates/">Use side inputs to automatically update models</a></li> Review Comment: ```suggestion <li><a href="/documentation/ml/side-input-updates/">Update your model during pipeline execution</a></li> ``` ########## website/www/site/content/en/documentation/ml/side-input-updates.md: ########## @@ -0,0 +1,144 @@ +--- +title: "Auto Update ML models using WatchFilePattern" +--- +<!-- +Licensed 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. +--> + +# Use WatchFilePattern as side input to auto-update ML models in RunInference + +The pipeline in this example uses a [RunInference](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) `PTransform` with a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` that emits `ModelMetadata` to run inferences on images using TensorFlow models. + +Using side inputs, you can update your model (which is passed in the `ModelHandler`) in real-time, even while the Beam pipeline is still running. This can be done either by leveraging one of Beam's provided patterns, such as the `WatchFilePattern`, +or by configuring a custom side input `PCollection` that defines the logic for the model update. + +For more information about side inputs, see the [Side inputs](https://beam.apache.org/documentation/programming-guide/#side-inputs) section in the Apache Beam Programming Guide. + +This example uses [`WatchFilePattern`](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.utils.html#apache_beam.ml.inference.utils.WatchFilePattern) as a side input. `WatchFilePattern` is used to watch for the file updates matching the `file_pattern` +based on timestamps. It emits the latest [`ModelMetadata`](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/), which is used in +the RunInference `PTransform` to automatically update the ML model without stopping the Beam pipeline. + +## Set up the source + +To read the image names, use a Pub/Sub topic as the source. + * The Pub/Sub topic emits a `UTF-8` encoded model path that is used to read and preprocess images to run the inference. + +## Models for image segmentation + +For the purpose of this example, use TensorFlow models saved in [HDF5](https://www.tensorflow.org/tutorials/keras/save_and_load#hdf5_format) format. + + +## Pre-process images for inference +The Pub/Sub topic emits an image path. We need to read and preprocess the image to use it for RunInference. The `read_image` function is used to read the image for inference. + +```python +import io +from PIL import Image +from apache_beam.io.filesystems import FileSystems +import numpy +import tensorflow as tf + +def read_image(image_file_name): + with FileSystems().open(image_file_name, 'r') as file: + data = Image.open(io.BytesIO(file.read())).convert('RGB') + img = data.resize((224, 224)) + img = numpy.array(img) / 255.0 + img_tensor = tf.cast(tf.convert_to_tensor(img[...]), dtype=tf.float32) + return img_tensor +``` + +Now, let's jump into the pipeline code. + +**Pipeline steps**: +1. Get the image names from the Pub/Sub topic. +2. Read and pre-process the images using the `read_image` function. +3. Pass the images to the RunInference `PTransform`. RunInference takes `model_handler` and `model_metadata_pcoll` as input parameters. + +For the [`model_handler`](https://github.com/apache/beam/blob/07f52a478174f8733c7efedb7189955142faa5fa/sdks/python/apache_beam/ml/inference/base.py#L308), we use [TFModelHandlerTensor](https://github.com/apache/beam/blob/186973b110d82838fb8e5ba27f0225a67c336591/sdks/python/apache_beam/ml/inference/tensorflow_inference.py#L184). +```python +from apache_beam.ml.inference.tensorflow_inference import TFModelHandlerTensor +# initialize TFModelHandlerTensor with a .h5 model saved in a directory accessible by the pipeline. +tf_model_handler = TFModelHandlerTensor(model_uri='gs://<your-bucket>/<model_path.h5>') +``` + +The `model_metadata_pcoll` is a [side input](https://beam.apache.org/documentation/programming-guide/#side-inputs) `PCollection` to the RunInference `PTransform`. This side input is used to update the models in the `model_handler` without needing to stop the beam pipeline. +We will use `WatchFilePattern` as side input to watch a glob pattern matching `.h5` files. + +`model_metadata_pcoll` expects a `PCollection[ModelMetadata]` compatible with [`AsSingleton`](https://beam.apache.org/releases/pydoc/2.4.0/apache_beam.pvalue.html#apache_beam.pvalue.AsSingleton) view. Because the pipeline uses `WatchFilePattern` as side input, it will take care of windowing and wrapping the output into `ModelMetadata`. Review Comment: ```suggestion `model_metadata_pcoll` expects a `PCollection` of ModelMetadata compatible with [AsSingleton](https://beam.apache.org/releases/pydoc/2.4.0/apache_beam.pvalue.html#apache_beam.pvalue.AsSingleton) view. Because the pipeline uses `WatchFilePattern` as side input, it will take care of windowing and wrapping the output into `ModelMetadata`. ``` I think this reads/renders more cleanly (code block links don't render well, see this line in http://apache-beam-website-pull-requests.storage.googleapis.com/25947/documentation/ml/side-input-updates/index.html) -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
