{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "OsFaZscKSPvo"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
        "\n",
        "# Licensed to the Apache Software Foundation (ASF) under one\n",
        "# or more contributor license agreements. See the NOTICE file\n",
        "# distributed with this work for additional information\n",
        "# regarding copyright ownership. The ASF licenses this file\n",
        "# to you under the Apache License, Version 2.0 (the\n",
        "# \"License\"); you may not use this file except in compliance\n",
        "# with the License. You may obtain a copy of the License at\n",
        "#\n",
        "#   http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing,\n",
        "# software distributed under the License is distributed on an\n",
        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
        "# KIND, either express or implied. See the License for the\n",
        "# specific language governing permissions and limitations\n",
        "# under the License"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUSiAR62SgO8"
      },
      "source": [
        "# Update ML models in running pipelines\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/automatic_model_refresh.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/automatic_model_refresh.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBtqF5UpKJNZ"
      },
      "source": [
        "This notebook demonstrates how to perform automatic model updates without stopping your Apache Beam pipeline.\n",
        "You can use side inputs to update your model in real time, even while the Apache Beam pipeline is running. The side input is passed in a `ModelHandler` configuration object. You can update the model either by leveraging one of Apache Beam's provided patterns, such as the `WatchFilePattern`, or by configuring a custom side input `PCollection` that defines the logic for the model update.\n",
        "\n",
        "The pipeline in this notebook uses a RunInference `PTransform` with TensorFlow machine learning (ML) models to run inference on images. To update the model, it uses a side input `PCollection` that emits `ModelMetadata`.\n",
        "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.\n",
        "\n",
        "This example uses `WatchFilePattern` as a side input. `WatchFilePattern` is used to watch for file updates that match the `file_pattern` based on timestamps. It emits the latest `ModelMetadata`, which is used in the RunInference `PTransform` to automatically update the ML model without stopping the Apache Beam pipeline.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPuXFowiTpWx"
      },
      "source": [
        "## Before you begin\n",
        "Install the dependencies required to run this notebook.\n",
        "\n",
        "To use RunInference with side inputs for automatic model updates, use Apache Beam version 2.46.0 or later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1RyTYsFEIOlA"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "!pip install apache_beam[interactive,gcp]>=2.46.0  tensorflow==2.15.0 tensorflow_hub==0.16.1 keras==2.15.0 Pillow==11.0.0 --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rs4cwwNrIV9H"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Imports required for the notebook.\n",
        "import logging\n",
        "import time\n",
        "import os\n",
        "from typing import Iterable\n",
        "from typing import Tuple\n",
        "\n",
        "import apache_beam as beam\n",
        "from apache_beam.ml.inference.base import PredictionResult\n",
        "from apache_beam.ml.inference.base import RunInference\n",
        "from apache_beam.ml.inference.tensorflow_inference import TFModelHandlerTensor\n",
        "from apache_beam.ml.inference.utils import WatchFilePattern\n",
        "from apache_beam.options.pipeline_options import GoogleCloudOptions\n",
        "from apache_beam.options.pipeline_options import PipelineOptions\n",
        "from apache_beam.options.pipeline_options import SetupOptions\n",
        "from apache_beam.options.pipeline_options import StandardOptions\n",
        "from apache_beam.options.pipeline_options import WorkerOptions\n",
        "from apache_beam.transforms.periodicsequence import PeriodicImpulse\n",
        "import numpy\n",
        "from PIL import Image\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jAKpPcmmGm03"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Authenticate to your Google Cloud account.\n",
        "def auth_to_colab():\n",
        "  from google.colab import auth\n",
        "  auth.authenticate_user()\n",
        "\n",
        "auth_to_colab()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORYNKhH3WQyP"
      },
      "source": [
        "## Configure the runner\n",
        "\n",
        "This pipeline uses the Dataflow Runner. To run the pipeline, you need to complete the following tasks:\n",
        "\n",
        "* Ensure that you have all the required permissions to run the pipeline on Dataflow.\n",
        "* Configure the pipeline options for the pipeline to run on Dataflow. Make sure the pipeline is using streaming mode.\n",
        "\n",
        "In the following code, replace `BUCKET_NAME` with the the name of your Cloud Storage bucket."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wWjbnq6X-4uE"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "options = PipelineOptions()\n",
        "options.view_as(StandardOptions).streaming = True\n",
        "\n",
        "# Replace with your bucket name.\n",
        "BUCKET_NAME = '<BUCKET_NAME>' # @param {type:'string'} \n",
        "os.environ['BUCKET_NAME'] = BUCKET_NAME\n",
        "\n",
        "# Provide required pipeline options for the Dataflow Runner.\n",
        "options.view_as(StandardOptions).runner = \"DataflowRunner\"\n",
        "\n",
        "# Set the project to the default project in your current Google Cloud environment.\n",
        "PROJECT_NAME = '<PROJECT_NAME>' # @param {type:'string'}\n",
        "options.view_as(GoogleCloudOptions).project = PROJECT_NAME\n",
        "\n",
        "# Set the Google Cloud region that you want to run Dataflow in.\n",
        "options.view_as(GoogleCloudOptions).region = 'us-central1'\n",
        "\n",
        "# IMPORTANT: Replace BUCKET_NAME with the the name of your Cloud Storage bucket.\n",
        "dataflow_gcs_location = \"gs://%s/dataflow\" % BUCKET_NAME\n",
        "\n",
        "# The Dataflow staging location. This location is used to stage the Dataflow pipeline and the SDK binary.\n",
        "options.view_as(GoogleCloudOptions).staging_location = '%s/staging' % dataflow_gcs_location\n",
        "\n",
        "\n",
        "# The Dataflow staging location. This location is used to stage the Dataflow pipeline and the SDK binary.\n",
        "options.view_as(GoogleCloudOptions).staging_location = '%s/staging' % dataflow_gcs_location\n",
        "\n",
        "# The Dataflow temp location. This location is used to store temporary files or intermediate results before outputting to the sink.\n",
        "options.view_as(GoogleCloudOptions).temp_location = '%s/temp' % dataflow_gcs_location\n",
        "\n",
        "options.view_as(SetupOptions).save_main_session = True\n",
        "\n",
        "# Launching Dataflow with only one worker might result in processing delays due to\n",
        "# initial input processing. This could further postpone the side input model updates.\n",
        "# To expedite the model update process, it's recommended to set num_workers>1.\n",
        "# https://github.com/apache/beam/issues/28776\n",
        "options.view_as(WorkerOptions).num_workers = 5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HTJV8pO2Wcw4"
      },
      "source": [
        "Install the `tensorflow` and `tensorflow_hub` dependencies on Dataflow. Use the `requirements_file` pipeline option to pass these dependencies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lEy4PkluWbdm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# In a requirements file, define the dependencies required for the pipeline.\n",
        "!printf 'tensorflow==2.15.0\\ntensorflow_hub==0.16.1\\nkeras==2.15.0\\nPillow==11.0.0' > ./requirements.txt\n",
        "# Install the pipeline dependencies on Dataflow.\n",
        "options.view_as(SetupOptions).requirements_file = './requirements.txt'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_AUNH_GJk_NE"
      },
      "source": [
        "## Use the TensorFlow model handler\n",
        " This example uses `TFModelHandlerTensor` as the model handler and the `resnet_101` model trained on [ImageNet](https://www.image-net.org/).\n",
        "\n",
        "\n",
        "For the Dataflow runner, you need to store the model in a remote location that the Apache Beam pipeline can access. For this example, download the `ResNet101` model, and upload it to the Google Cloud Storage bucket.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ibkWiwVNvyrn"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "model = tf.keras.applications.resnet.ResNet101()\n",
        "model.save('resnet101_weights_tf_dim_ordering_tf_kernels.keras')\n",
        "# After saving the model locally, upload the model to GCS bucket and provide that gcs bucket `URI` as `model_uri` to the `TFModelHandler`\n",
        "!gsutil cp resnet101_weights_tf_dim_ordering_tf_kernels.keras gs://${BUCKET_NAME}/dataflow/resnet101_weights_tf_dim_ordering_tf_kernels.keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kkSnsxwUk-Sp"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "model_handler = TFModelHandlerTensor(\n",
        "    model_uri=dataflow_gcs_location + \"/resnet101_weights_tf_dim_ordering_tf_kernels.keras\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZH0r0sL-if5"
      },
      "source": [
        "## Preprocess images\n",
        "\n",
        "Use `preprocess_image` to run the inference, read the image, and convert the image to a TensorFlow tensor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dU5imgTt-8Ne"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "def preprocess_image(image_name, image_dir):\n",
        "  img = tf.keras.utils.get_file(image_name, image_dir + image_name)\n",
        "  img = Image.open(img).resize((224, 224))\n",
        "  img = numpy.array(img) / 255.0\n",
        "  img_tensor = tf.cast(tf.convert_to_tensor(img[...]), dtype=tf.float32)\n",
        "  return img_tensor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6V5tJxO6-gyt"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "class PostProcessor(beam.DoFn):\n",
        "  \"\"\"Process the PredictionResult to get the predicted label.\n",
        "  Returns predicted label.\n",
        "  \"\"\"\n",
        "  def process(self, element: PredictionResult) -> Iterable[Tuple[str, str]]:\n",
        "    predicted_class = numpy.argmax(element.inference, axis=-1)\n",
        "    labels_path = tf.keras.utils.get_file(\n",
        "        'ImageNetLabels.txt',\n",
        "        'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt'  # pylint: disable=line-too-long\n",
        "    )\n",
        "    imagenet_labels = numpy.array(open(labels_path).read().splitlines())\n",
        "    predicted_class_name = imagenet_labels[predicted_class]\n",
        "    yield predicted_class_name.title(), element.model_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GpdKk72O_NXT"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Define the pipeline object.\n",
        "pipeline = beam.Pipeline(options=options)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elZ53uxc_9Hv"
      },
      "source": [
        "Next, review the pipeline steps and examine the code.\n",
        "\n",
        "### Pipeline steps\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "305tkV2sAD-S"
      },
      "source": [
        "1. Create a `PeriodicImpulse` transform, which emits output every `n` seconds. The `PeriodicImpulse` transform generates an infinite sequence of elements with a given runtime interval.\n",
        "\n",
        "   In this example, `PeriodicImpulse` mimics the Pub/Sub source. Because the inputs in a streaming pipeline arrive in intervals, use `PeriodicImpulse` to output elements at `m` intervals.\n",
        "To learn more about `PeriodicImpulse`, see the [`PeriodicImpulse` code](https://github.com/apache/beam/blob/9c52e0594d6f0e59cd17ee005acfb41da508e0d5/sdks/python/apache_beam/transforms/periodicsequence.py#L150)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vUFStz66_Tbb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "start_timestamp = time.time() # start timestamp of the periodic impulse\n",
        "end_timestamp = start_timestamp + 60 * 20 # end timestamp of the periodic impulse (will run for 20 minutes).\n",
        "main_input_fire_interval = 60 # interval in seconds at which the main input PCollection is emitted.\n",
        "side_input_fire_interval = 60 # interval in seconds at which the side input PCollection is emitted.\n",
        "\n",
        "periodic_impulse = (\n",
        "      pipeline\n",
        "      | \"MainInputPcoll\" >> PeriodicImpulse(\n",
        "          start_timestamp=start_timestamp,\n",
        "          stop_timestamp=end_timestamp,\n",
        "          fire_interval=main_input_fire_interval))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-sal2rFAxP2"
      },
      "source": [
        "2. To read and preprocess the images, use the `preprocess_image` function. This example uses `Cat-with-beanie.jpg` for all inferences.\n",
        "\n",
        "  **Note**: The image used for prediction is licensed in CC-BY. The creator is listed in the [LICENSE.txt](https://storage.googleapis.com/apache-beam-samples/image_captioning/LICENSE.txt) file."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gW4cE8bhXS-d"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dGg11TpV_aV6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "image_data = (periodic_impulse | beam.Map(lambda x: \"Cat-with-beanie.jpg\")\n",
        "      | \"ReadImage\" >> beam.Map(lambda image_name: preprocess_image(\n",
        "          image_name=image_name, image_dir='https://storage.googleapis.com/apache-beam-samples/image_captioning/')))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eB0-ewd-BCKE"
      },
      "source": [
        "3. Pass the images to the RunInference `PTransform`. RunInference takes `model_handler` and `model_metadata_pcoll` as input parameters.\n",
        "  * `model_metadata_pcoll` is a side input `PCollection` to the RunInference `PTransform`. This side input updates the `model_uri` in the `model_handler` while the Apache Beam pipeline runs.\n",
        "  * Use `WatchFilePattern` as side input to watch a `file_pattern` matching `.keras` files. In this case, the `file_pattern` is `'gs://BUCKET_NAME/dataflow/*keras'`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_AjvvexJ_hUq"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        " # The side input used to watch for the .keras file and update the model_uri of the TFModelHandlerTensor.\n",
        "file_pattern = dataflow_gcs_location + '/*.keras'\n",
        "side_input_pcoll = (\n",
        "      pipeline\n",
        "      | \"WatchFilePattern\" >> WatchFilePattern(file_pattern=file_pattern,\n",
        "                                                interval=side_input_fire_interval,\n",
        "                                                stop_timestamp=end_timestamp))\n",
        "inferences = (\n",
        "      image_data\n",
        "      | \"ApplyWindowing\" >> beam.WindowInto(beam.window.FixedWindows(10))\n",
        "      | \"RunInference\" >> RunInference(model_handler=model_handler,\n",
        "                                      model_metadata_pcoll=side_input_pcoll))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTA4wRWNDVis"
      },
      "source": [
        "4. Post-process the `PredictionResult` object.\n",
        "When the inference is complete, RunInference outputs a `PredictionResult` object that contains the fields `example`, `inference`, and `model_id`. The `model_id` field identifies the model used to run the inference. The `PostProcessor` returns the predicted label and the model ID used to run the inference on the predicted label."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9TB76fo-_vZJ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "post_processor = (\n",
        "    inferences\n",
        "    | \"PostProcessResults\" >> beam.ParDo(PostProcessor())\n",
        "    | \"LogResults\" >> beam.Map(logging.info))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYp-mBHHjOjA"
      },
      "source": [
        "### Watch for the model update\n",
        "\n",
        "After the pipeline starts processing data, when you see output emitted from the RunInference `PTransform`, upload a `resnet152` model saved in the `.keras` format to a Google Cloud Storage bucket location that matches the `file_pattern` you defined earlier.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FpUfNBSWH9Xy"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "model = tf.keras.applications.resnet.ResNet152()\n",
        "model.save('resnet152_weights_tf_dim_ordering_tf_kernels.keras')\n",
        "!gsutil cp resnet152_weights_tf_dim_ordering_tf_kernels.keras gs://${BUCKET_NAME}/resnet152_weights_tf_dim_ordering_tf_kernels.keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ty03jDnKdKR"
      },
      "source": [
        "## Run the pipeline\n",
        "\n",
        "Use the following code to run the pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wd0VJLeLEWBU"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Run the pipeline.\n",
        "result = pipeline.run().wait_until_finish()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}