damccorm commented on code in PR #25933:
URL: https://github.com/apache/beam/pull/25933#discussion_r1145298847
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examples/notebooks/beam-ml/run_inference_tensorflow_with_tensorflowhub.ipynb:
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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "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"
+ ],
+ "metadata": {
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow and TensorflowHub\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/run_inference_tensorflow_with_tensorflowhub.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/run_inference_tensorflow_with_tensorflowhub.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",
+ "source": [
+ "This notebook demonstrates the use of Beam's
[RunInference](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.RunInference)
transform for [TensorFlow](https://www.tensorflow.org/). Beam has built in
support for 2 Tensorflow Model Handlers:
[TFModelHandlerNumpy](https://github.com/apache/beam/blob/ca0787642a6b3804a742326147281c99ae8d08d2/sdks/python/apache_beam/ml/inference/tensorflow_inference.py#L91)
and
[TFModelHandlerTensor](https://github.com/apache/beam/blob/ca0787642a6b3804a742326147281c99ae8d08d2/sdks/python/apache_beam/ml/inference/tensorflow_inference.py#L184).\n",
+ "TFModelHandlerNumpy can be used to run inference on models expecting
a Numpy array as an input while TFModelHandlerTensor can be used to run
inference on models expecting a Tensor as an input.\n",
+ "\n",
+ "The Apache Beam RunInference transform is used to make predictions
for\n",
+ "a variety of machine learning models. For more information about the
RunInference API, see [Machine
Learning](https://beam.apache.org/documentation/sdks/python-machine-learning)
in the Apache Beam documentation.\n",
+ "\n",
+ "This notebook demonstrates the following steps:\n",
+ "- Build a simple TensorFlow model.\n",
+ "- Set up example data.\n",
+ "- Run those examples and get a prediction inside an Apache Beam
pipeline.\n",
+ "- Run an inference pipeline by using a trained model from Tensorflow
Hub.\n",
+ "\n",
+ "**Note:** Image used for prediction is licensed in CC-BY, creator in
listed in the
[LICENSE.txt](https://storage.googleapis.com/apache-beam-samples/image_captioning/LICENSE.txt)
file."
+ ],
+ "metadata": {
+ "id": "HrCtxslBGK8Z"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Before you begin\n",
+ "Complete the following setup steps.\n",
+ "\n",
+ "First, import `tensorflow`.\n",
+ "\n",
+ "To use RunInference with Tensorflow Model Handler, install Apache
Beam version 2.46 or later."
+ ],
+ "metadata": {
+ "id": "HrCtxslBGK8A"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jBakpNZnAhqk",
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "outputId": "014a2ccf-5180-40ba-e8ff-358f7a8d5e7d"
+ },
+ "source": [
+ "!pip install tensorflow\n",
+ "!pip install apache_beam==2.46.0"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
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+ " Stored in directory:
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+ " Building wheel for dill (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl
size=78545
sha256=aea972f29e1a09d4820d06619ea30fd946c2acb65f23ade451b73291c55d51c3\n",
+ " Stored in directory:
/root/.cache/pip/wheels/4f/0b/ce/75d96dd714b15e51cb66db631183ea3844e0c4a6d19741a149\n",
+ " Building wheel for docopt (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for docopt:
filename=docopt-0.6.2-py2.py3-none-any.whl size=13721
sha256=99281120e89574df1c1a6e486487563f4325793119b163fa90cb3c93216dcf6e\n",
+ " Stored in directory:
/root/.cache/pip/wheels/70/4a/46/1309fc853b8d395e60bafaf1b6df7845bdd82c95fd59dd8d2b\n",
+ "Successfully built crcmod dill docopt\n",
+ "Installing collected packages: docopt, crcmod, zstandard,
pymongo, orjson, objsize, fasteners, fastavro, dill, hdfs, apache_beam\n",
+ "Successfully installed apache_beam-2.46.0 crcmod-1.7 dill-0.3.1.1
docopt-0.6.2 fastavro-1.7.3 fasteners-0.18 hdfs-2.7.0 objsize-0.6.1
orjson-3.8.8 pymongo-3.13.0 zstandard-0.20.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Authenticate with Google Cloud\n",
+ "This notebook relies on saving your model to Google Cloud. To use
your Google Cloud account, authenticate this notebook."
+ ],
+ "metadata": {
+ "id": "X80jy3FqHjK4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Kz9sccyGBqz3"
+ },
+ "source": [
+ "from google.colab import auth\n",
+ "auth.authenticate_user()"
+ ],
+ "execution_count": 4,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Import dependencies and set up your bucket\n",
+ "Replace `PROJECT_ID` and `BUCKET_NAME` with the ID of your project
and the name of your bucket.\n",
+ "\n",
+ "**Important**: If an error occurs, restart your runtime."
+ ],
+ "metadata": {
+ "id": "40qtP6zJuMXm"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "eEle839_Akqx"
+ },
+ "source": [
+ "import argparse\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "\n",
+ "import numpy\n",
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "project = \"google.com:clouddfe\"\n",
+ "bucket = \"clouddfe-riteshghorse\"\n",
+ "\n",
+ "save_model_dir_multiply =
f'gs://{bucket}/tfx-inference/model/multiply_five/v1/'\n"
+ ],
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Create and test a simple model\n",
+ "\n",
+ "This step creates and tests a model that predicts the 5 times table."
+ ],
+ "metadata": {
+ "id": "YzvZWEv-1oiK"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JiCniG0Ye2Wu"
+ },
+ "source": [
+ "### Create the model\n",
+ "Create training data and build a linear regression model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "SH7iq3zeBBJ-",
+ "outputId": "175dc68d-6147-464f-95d9-e7e4c91ff403"
+ },
+ "source": [
+ "# Create training data that represents the 5 times multiplication
table for the numbers 0 to 99.\n",
+ "# x is the data and y is the labels.\n",
+ "x = numpy.arange(0, 100) # Examples\n",
+ "y = x * 5 # Labels\n",
+ "\n",
+ "# Build a simple linear regression model.\n",
+ "# Note that the model has a shape of (1) for its input layer and
expects a single int64 value.\n",
+ "input_layer = keras.layers.Input(shape=(1), dtype=tf.float32,
name='x')\n",
+ "output_layer= keras.layers.Dense(1)(input_layer)\n",
+ "\n",
+ "model = keras.Model(input_layer, output_layer)\n",
+ "model.compile(optimizer=tf.optimizers.Adam(),
loss='mean_absolute_error')\n",
+ "model.summary()"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Model: \"model\"\n",
+
"_________________________________________________________________\n",
+ " Layer (type) Output Shape Param #
\n",
+
"=================================================================\n",
+ " x (InputLayer) [(None, 1)] 0
\n",
+ "
\n",
+ " dense (Dense) (None, 1) 2
\n",
+ "
\n",
+
"=================================================================\n",
+ "Total params: 2\n",
+ "Trainable params: 2\n",
+ "Non-trainable params: 0\n",
+
"_________________________________________________________________\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Test the model\n",
+ "\n",
+ "This step tests the model that you created."
+ ],
+ "metadata": {
+ "id": "O_a0-4Gb19cy"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "5XkIYXhJBFmS",
+ "outputId": "5444b1ab-7198-4798-abb3-62dd7e848c26"
+ },
+ "source": [
+ "model.fit(x, y, epochs=500, verbose=0)\n",
+ "test_examples =[20, 40, 60, 90]\n",
+ "value_to_predict = numpy.array(test_examples, dtype=numpy.float32)\n",
+ "predictions = model.predict(value_to_predict)\n",
+ "\n",
+ "print('Test Examples ' + str(test_examples))\n",
+ "print('Predictions ' + str(predictions))"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1/1 [==============================] - 0s 118ms/step\n",
+ "Test Examples [20, 40, 60, 90]\n",
+ "Predictions [[ 42.615337]\n",
+ " [ 83.2313 ]\n",
+ " [123.84727 ]\n",
+ " [184.77121 ]]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Save the model\n",
+ "\n",
+ "This step shows how to save your model."
+ ],
+ "metadata": {
+ "id": "r4dpR6dQ4JwX"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.save(save_model_dir_multiply)"
+ ],
+ "metadata": {
+ "id": "7yVTY-hOhsgI"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Run the Pipeline\n",
+ "Use the following code to run the pipeline."
+ ],
+ "metadata": {
+ "id": "P2UMmbNW4YQV"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PzjmXM_KvqHY"
+ },
+ "source": [
+ "from apache_beam.ml.inference.tensorflow_inference import
TFModelHandlerNumpy\n",
+ "import apache_beam as beam\n",
+ "\n",
+ "class FormatOutput(beam.DoFn):\n",
+ " def process(self, element, *args, **kwargs):\n",
+ " yield \"example is {example} prediction is
{prediction}\".format(example=element.example, prediction=element.inference)\n",
+ "\n",
+ "\n",
+ "examples = numpy.array([20, 40, 60, 90], dtype=numpy.float32)\n",
+ "model_handler = TFModelHandlerNumpy(save_model_dir_multiply)\n",
+ "with beam.Pipeline() as p:\n",
+ " _ = (p | beam.Create(examples)\n",
+ " | RunInference(model_handler)\n",
+ " | beam.ParDo(FormatOutput())\n",
+ " | beam.Map(print)\n",
+ " )"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## KeyedModelHandler with TensorFlow\n",
+ "\n",
+ "By default, the `ModelHandler` does not expect a key.\n",
+ "\n",
+ "* If you know that keys are associated with your examples, wrap the
model handler with `beam.KeyedModelHandler`.\n",
+ "* If you don't know whether keys are associated with your examples,
use `beam.MaybeKeyedModelHandler`."
+ ],
+ "metadata": {
+ "id": "IXikjkGdHm9n"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from apache_beam.ml.inference.base import KeyedModelHandler\n",
+ "from google.protobuf import text_format\n",
+ "import tensorflow as tf\n",
+ "from typing import Tuple\n",
+ "\n",
+ "class FormatOutputKeyed(FormatOutput):\n",
+ " # To simplify, inherit from FormatOutput.\n",
+ " def process(self, tuple_in: Tuple):\n",
+ " key, element = tuple_in\n",
+ " output = super().process(element)\n",
+ " yield \"{} : {}\".format(key, output)\n",
+ "\n",
+ "examples = numpy.array([(1,20), (2,40), (3,60), (4,90)],
dtype=numpy.float32)\n",
+ "keyed_model_handler =
KeyedModelHandler(TFModelHandlerNumpy(save_model_dir_multiply))\n",
+ "with beam.Pipeline() as p:\n",
+ " _ = (p | 'CreateExamples' >> beam.Create(examples)\n",
+ " | RunInference(keyed_model_handler)\n",
+ " | beam.ParDo(FormatOutputKeyed())\n",
+ " | beam.Map(print)\n",
+ " )"
+ ],
+ "metadata": {
+ "id": "KPtE3fmdJQry"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## RunInference with Tensorflow Hub\n",
+ "\n",
+ "To use tensorflow hub's trained model URL, pass it to the `model_uri`
field of TFModelHandler class."
Review Comment:
```suggestion
"To use TensorFlow Hub's trained model URL, pass it to the
`model_uri` field of TFModelHandler class."
```
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