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title: "Auto Update ML models using WatchFilePattern"
---
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# 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). 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` fields. 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
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).