AnandInguva commented on code in PR #25947: URL: https://github.com/apache/beam/pull/25947#discussion_r1153688583
########## 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 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` 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. + +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`, +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` 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`. 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). Because the pipeline uses `WatchFilePattern` as side input, it will take care of windowing and wrapping the output into `ModelMetadata`. ``` -- This is an automated message from the Apache Git Service. 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