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Posted to github@beam.apache.org by "riteshghorse (via GitHub)" <gi...@apache.org> on 2023/04/04 18:36:19 UTC
[GitHub] [beam] riteshghorse opened a new pull request, #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
riteshghorse opened a new pull request, #26105:
URL: https://github.com/apache/beam/pull/26105
------------------------
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[GitHub] [beam] damccorm commented on pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on PR #26105:
URL: https://github.com/apache/beam/pull/26105#issuecomment-1498306892
@riteshghorse FYI this may end up getting reverted because of the current code freeze - https://lists.apache.org/thread/rpmq9r9tbgrry8yco70mzyt4psyk5m20 - if that happens we can just make the same change again (I'll be happy to approve)
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[GitHub] [beam] github-actions[bot] commented on pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #26105:
URL: https://github.com/apache/beam/pull/26105#issuecomment-1496428561
Stopping reviewer notifications for this pull request: review requested by someone other than the bot, ceding control
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[GitHub] [beam] riteshghorse commented on pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "riteshghorse (via GitHub)" <gi...@apache.org>.
riteshghorse commented on PR #26105:
URL: https://github.com/apache/beam/pull/26105#issuecomment-1496427122
R: @rezarokni @rszper
CC: @damccorm
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[GitHub] [beam] riteshghorse commented on pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "riteshghorse (via GitHub)" <gi...@apache.org>.
riteshghorse commented on PR #26105:
URL: https://github.com/apache/beam/pull/26105#issuecomment-1498327648
Oops sorry! I missed that email, revert PR at https://github.com/apache/beam/pull/26143
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[GitHub] [beam] riteshghorse commented on pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "riteshghorse (via GitHub)" <gi...@apache.org>.
riteshghorse commented on PR #26105:
URL: https://github.com/apache/beam/pull/26105#issuecomment-1497490610
Run RAT PreCommit
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[GitHub] [beam] riteshghorse commented on pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "riteshghorse (via GitHub)" <gi...@apache.org>.
riteshghorse commented on PR #26105:
URL: https://github.com/apache/beam/pull/26105#issuecomment-1497908160
Run RAT PreCommit
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[GitHub] [beam] rszper commented on pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "rszper (via GitHub)" <gi...@apache.org>.
rszper commented on PR #26105:
URL: https://github.com/apache/beam/pull/26105#issuecomment-1498259558
LGTM
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[GitHub] [beam] riteshghorse merged pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "riteshghorse (via GitHub)" <gi...@apache.org>.
riteshghorse merged PR #26105:
URL: https://github.com/apache/beam/pull/26105
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[GitHub] [beam] rszper commented on a diff in pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "rszper (via GitHub)" <gi...@apache.org>.
rszper commented on code in PR #26105:
URL: https://github.com/apache/beam/pull/26105#discussion_r1157633109
##########
examples/notebooks/beam-ml/run_inference_tensorflow.ipynb:
##########
@@ -67,21 +67,18 @@
"source": [
"This notebook demonstrates the use of the [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/).\n",
"\n",
- "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",
+ "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 `tf.Tensor` as an input.\n",
"\n",
- "Beam's Runinference transform also accepts a ModelHandler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) using `CreateModelHandler`.\n",
+ "If your model needs input of type `tf.Example` then please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb).\n",
Review Comment:
```suggestion
"If your model needs input of type `tf.Example` see the [Apache Beam RunInference with `tfx-bsl` notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb).\n",
```
##########
examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb:
##########
@@ -0,0 +1,647 @@
+{
+ "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": {
+ "cellView": "form",
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow\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_tfx.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_tfx.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 the [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/) using [tfx-bsl](https://github.com/tensorflow/tfx-bsl).\n",
+ "\n",
+ "This approach should only be used when the trained model requires a `tf.Example` input. If you have `numpy` or `tf.Tensor` inputs, please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb) instead which demonstrates the use built-in model handlers for TensorFlow in Beam SDK starting 2.46.0.\n",
Review Comment:
```suggestion
"Use this approach when the trained model requires a `tf.Example` input. If you have `numpy` or `tf.Tensor` inputs, see the [Apache Beam RunInference with TensorFlow notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb) instead which demonstrates the use built-in model handlers for TensorFlow in Beam SDK starting 2.46.0.\n",
```
##########
examples/notebooks/beam-ml/run_inference_tensorflow.ipynb:
##########
@@ -90,19 +87,7 @@
{
"cell_type": "markdown",
"source": [
- "## Before you begin\n",
- "Complete the following setup steps.\n",
- "\n",
- "First, import `tfx-bsl`."
- ],
- "metadata": {
- "id": "HrCtxslBGK8A"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "To use RunInference with built-in Tensorflow Model Handler, install Apache Beam version 2.46 or later. Creation of a ModelHandler is supported in `tfx-bsl` versions 1.10 and later."
+ "To use RunInference with built-in Tensorflow Model Handler, install Apache Beam version 2.46 or later."
Review Comment:
```suggestion
"To use RunInference with built-in Tensorflow model handler, install Apache Beam version 2.46 or later."
```
##########
examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb:
##########
@@ -0,0 +1,647 @@
+{
+ "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": {
+ "cellView": "form",
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow\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_tfx.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_tfx.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 the [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/) using [tfx-bsl](https://github.com/tensorflow/tfx-bsl).\n",
+ "\n",
+ "This approach should only be used when the trained model requires a `tf.Example` input. If you have `numpy` or `tf.Tensor` inputs, please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb) instead which demonstrates the use built-in model handlers for TensorFlow in Beam SDK starting 2.46.0.\n",
+ "\n",
+ "Beam's Runinference transform accepts a ModelHandler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) using `CreateModelHandler`.\n",
Review Comment:
```suggestion
"The Apache Beam RunInference transform accepts a model handler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) by using `CreateModelHandler`.\n",
```
##########
examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb:
##########
@@ -0,0 +1,647 @@
+{
+ "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": {
+ "cellView": "form",
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow\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_tfx.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_tfx.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 the [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/) using [tfx-bsl](https://github.com/tensorflow/tfx-bsl).\n",
+ "\n",
+ "This approach should only be used when the trained model requires a `tf.Example` input. If you have `numpy` or `tf.Tensor` inputs, please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb) instead which demonstrates the use built-in model handlers for TensorFlow in Beam SDK starting 2.46.0.\n",
+ "\n",
+ "Beam's Runinference transform accepts a ModelHandler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) using `CreateModelHandler`.\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",
+ "- Import [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl).\n",
+ "- Build a simple TensorFlow model.\n",
+ "- Set up example data\n",
Review Comment:
```suggestion
"- Set up example data.\n",
```
##########
examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb:
##########
@@ -0,0 +1,647 @@
+{
+ "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": {
+ "cellView": "form",
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow\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_tfx.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_tfx.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 the [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/) using [tfx-bsl](https://github.com/tensorflow/tfx-bsl).\n",
+ "\n",
+ "This approach should only be used when the trained model requires a `tf.Example` input. If you have `numpy` or `tf.Tensor` inputs, please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb) instead which demonstrates the use built-in model handlers for TensorFlow in Beam SDK starting 2.46.0.\n",
+ "\n",
+ "Beam's Runinference transform accepts a ModelHandler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) using `CreateModelHandler`.\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",
+ "- Import [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl).\n",
+ "- Build a simple TensorFlow model.\n",
+ "- Set up example data\n",
+ "- Run those examples with `tfx-bsl` model handler and get a prediction inside an Apache Beam pipeline."
+ ],
+ "metadata": {
+ "id": "HrCtxslBGK8Z"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Before you begin\n",
+ "Complete the following setup steps.\n",
+ "\n",
+ "First, import `tfx-bsl`."
+ ],
+ "metadata": {
+ "id": "HrCtxslBGK8A"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Creation of a ModelHandler is supported in `tfx-bsl` versions 1.10 and later."
+ ],
+ "metadata": {
+ "id": "gVCtGOKTHMm4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jBakpNZnAhqk"
+ },
+ "source": [
+ "!pip install tfx_bsl==1.10.0 --quiet\n",
+ "!pip install protobuf --quiet\n",
+ "!pip install apache_beam --quiet"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "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": 2,
+ "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",
+ "from tensorflow_serving.apis import prediction_log_pb2\n",
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "import tfx_bsl\n",
+ "from tfx_bsl.public.beam.run_inference import CreateModelHandler\n",
+ "from tfx_bsl.public import tfxio\n",
+ "from tfx_bsl.public.proto import model_spec_pb2\n",
+ "from tensorflow_metadata.proto.v0 import schema_pb2\n",
+ "\n",
+ "import numpy\n",
+ "\n",
+ "from typing import Dict, Text, Any, Tuple, List\n",
+ "\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "project = \"PROJECT_ID\"\n",
+ "bucket = \"BUCKET_NAME\"\n",
+ "\n",
+ "save_model_dir_multiply = f'gs://{bucket}/tfx-inference/model/multiply_five/v1/'\n"
+ ],
+ "execution_count": 12,
+ "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": "rIwD_qEpX7Gu"
+ },
+ "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": "c5adb7ec-285b-401e-f9be-1e9b83c6d0ba"
+ },
+ "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": 4,
+ "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": "e3bb5079-5cb8-4fe4-eb8d-d3d13d5f9f0c"
+ },
+ "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": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1/1 [==============================] - 0s 94ms/step\n",
+ "Test Examples [20, 40, 60, 90]\n",
+ "Predictions [[ 9.201942]\n",
+ " [16.40566 ]\n",
+ " [23.609379]\n",
+ " [34.41496 ]]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## RunInference with Tensorflow using tfx-bsl\n",
+ "In versions 1.10.0 and later of `tfx-bsl`, you can\n",
+ "create a TensorFlow `ModelHandler` for use with Apache Beam. 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",
+ "### Populate the data in a TensorFlow proto\n",
+ "\n",
+ "Tensorflow data uses protos. If you are loading from a file, helpers exist for this step. Because this example uses generated data, this code populates a proto."
+ ],
+ "metadata": {
+ "id": "dEmleqiH3t71"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XvKc9kQilPjx"
+ },
+ "source": [
+ "# This example shows a proto that converts the samples and labels into\n",
+ "# tensors usable by TensorFlow.\n",
+ "\n",
+ "class ExampleProcessor:\n",
+ " def create_example_with_label(self, feature: numpy.float32,\n",
+ " label: numpy.float32)-> tf.train.Example:\n",
+ " return tf.train.Example(\n",
+ " features=tf.train.Features(\n",
+ " feature={'x': self.create_feature(feature),\n",
+ " 'y' : self.create_feature(label)\n",
+ " }))\n",
+ "\n",
+ " def create_example(self, feature: numpy.float32):\n",
+ " return tf.train.Example(\n",
+ " features=tf.train.Features(\n",
+ " feature={'x' : self.create_feature(feature)})\n",
+ " )\n",
+ "\n",
+ " def create_feature(self, element: numpy.float32):\n",
+ " return tf.train.Feature(float_list=tf.train.FloatList(value=[element]))\n",
+ "\n",
+ "# Create a labeled example file for the 5 times table.\n",
+ "\n",
+ "example_five_times_table = 'example_five_times_table.tfrecord'\n",
+ "\n",
+ "with tf.io.TFRecordWriter(example_five_times_table) as writer:\n",
+ " for i in zip(x, y):\n",
+ " example = ExampleProcessor().create_example_with_label(\n",
+ " feature=i[0], label=i[1])\n",
+ " writer.write(example.SerializeToString())\n",
+ "\n",
+ "# Create a file containing the values to predict.\n",
+ "\n",
+ "predict_values_five_times_table = 'predict_values_five_times_table.tfrecord'\n",
+ "\n",
+ "with tf.io.TFRecordWriter(predict_values_five_times_table) as writer:\n",
+ " for i in value_to_predict:\n",
+ " example = ExampleProcessor().create_example(feature=i)\n",
+ " writer.write(example.SerializeToString())"
+ ],
+ "execution_count": 7,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Fit The Model\n",
+ "\n",
+ "This step builds a model. Because RunInference requires pretrained models, this segment builds a usable model."
+ ],
+ "metadata": {
+ "id": "G-sAu3cf31f3"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "AnbrxXPKeAOQ",
+ "outputId": "42439aac-3a10-4e86-829f-44332aad6173"
+ },
+ "source": [
+ "RAW_DATA_TRAIN_SPEC = {\n",
+ "'x': tf.io.FixedLenFeature([], tf.float32),\n",
+ "'y': tf.io.FixedLenFeature([], tf.float32)\n",
+ "}\n",
+ "\n",
+ "dataset = tf.data.TFRecordDataset(example_five_times_table)\n",
+ "dataset = dataset.map(lambda e : tf.io.parse_example(e, RAW_DATA_TRAIN_SPEC))\n",
+ "dataset = dataset.map(lambda t : (t['x'], t['y']))\n",
+ "dataset = dataset.batch(100)\n",
+ "dataset = dataset.repeat()\n",
+ "\n",
+ "model.fit(dataset, epochs=5000, steps_per_epoch=1, verbose=0)"
+ ],
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<keras.callbacks.History at 0x7f6960074c70>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Save the model\n",
+ "\n",
+ "This step shows how to save your model."
+ ],
+ "metadata": {
+ "id": "r4dpR6dQ4JwX"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fYvrIYO3qiJx"
+ },
+ "source": [
+ "RAW_DATA_PREDICT_SPEC = {\n",
+ "'x': tf.io.FixedLenFeature([], tf.float32),\n",
+ "}\n",
+ "\n",
+ "# tf.function compiles the function into a callable TF graph.\n",
+ "# RunInference relies on calling a TF graph as a model.\n",
+ "# Note: The input signature should be type tf.string as supported by\n",
+ "# tfx-bsl ModelHandlers.\n",
+ "@tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string , name='examples')])\n",
+ "def serve_tf_examples_fn(serialized_tf_examples):\n",
+ " \"\"\"Returns the output to be used in the serving signature.\"\"\"\n",
+ " features = tf.io.parse_example(serialized_tf_examples, RAW_DATA_PREDICT_SPEC)\n",
+ " return model(features, training=False)\n",
+ "\n",
+ "signature = {'serving_default': serve_tf_examples_fn}\n",
+ "\n",
+ "# Signatures define the input and output types for a computation. The optional\n",
+ "# save signatures argument controls which methods in obj will be available to\n",
+ "# programs which consume SavedModels, for example, serving APIs.\n",
+ "# See https://www.tensorflow.org/api_docs/python/tf/saved_model/save\n",
+ "tf.keras.models.save_model(model, save_model_dir_multiply, signatures=signature)"
+ ],
+ "execution_count": 9,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Run the Pipeline\n",
+ "Use the following code to run the pipeline.\n",
+ "\n",
+ "`FormatOutput` demonstrates how to extract values from the output protos.\n",
+ "\n",
+ "`CreateModelHandler` demonstrates the model handler that needs to be passed into the Apache Beam RunInference API."
+ ],
+ "metadata": {
+ "id": "P2UMmbNW4YQV"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 193
+ },
+ "id": "PzjmXM_KvqHY",
+ "outputId": "0aa60bef-52a0-4ce2-d228-3fac977d59e0"
+ },
+ "source": [
+ "from tfx_bsl.public.beam.run_inference import CreateModelHandler\n",
+ "\n",
+ "class FormatOutput(beam.DoFn):\n",
+ " def process(self, element: prediction_log_pb2.PredictionLog):\n",
+ " predict_log = element.predict_log\n",
+ " input_value = tf.train.Example.FromString(predict_log.request.inputs['examples'].string_val[0])\n",
+ " input_float_value = input_value.features.feature['x'].float_list.value[0]\n",
+ " output_value = predict_log.response.outputs\n",
+ " output_float_value = output_value['output_0'].float_val[0]\n",
+ " yield (f\"example is {input_float_value:.2f} prediction is {output_float_value:.2f}\")\n",
+ "\n",
+ "tfexample_beam_record = tfx_bsl.public.tfxio.TFExampleRecord(file_pattern=predict_values_five_times_table)\n",
+ "saved_model_spec = model_spec_pb2.SavedModelSpec(model_path=save_model_dir_multiply)\n",
+ "inference_spec_type = model_spec_pb2.InferenceSpecType(saved_model_spec=saved_model_spec)\n",
+ "model_handler = CreateModelHandler(inference_spec_type)\n",
+ "with beam.Pipeline() as p:\n",
+ " _ = (p | tfexample_beam_record.RawRecordBeamSource()\n",
+ " | RunInference(model_handler)\n",
+ " | beam.ParDo(FormatOutput())\n",
+ " | beam.Map(print)\n",
+ " )"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/javascript": [
+ "\n",
+ " if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+ " var jqueryScript = document.createElement('script');\n",
+ " jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+ " jqueryScript.type = 'text/javascript';\n",
+ " jqueryScript.onload = function() {\n",
+ " var datatableScript = document.createElement('script');\n",
+ " datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+ " datatableScript.type = 'text/javascript';\n",
+ " datatableScript.onload = function() {\n",
+ " window.interactive_beam_jquery = jQuery.noConflict(true);\n",
+ " window.interactive_beam_jquery(document).ready(function($){\n",
+ " \n",
+ " });\n",
+ " }\n",
+ " document.head.appendChild(datatableScript);\n",
+ " };\n",
+ " document.head.appendChild(jqueryScript);\n",
+ " } else {\n",
+ " window.interactive_beam_jquery(document).ready(function($){\n",
+ " \n",
+ " });\n",
+ " }"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "WARNING:tensorflow:From /usr/local/lib/python3.9/dist-packages/tfx_bsl/beam/run_inference.py:615: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Use `tf.saved_model.load` instead.\n",
+ "WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "example is 20.00 prediction is 104.36\n",
+ "example is 40.00 prediction is 202.62\n",
+ "example is 60.00 prediction is 300.87\n",
+ "example is 90.00 prediction is 448.26\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## KeyedModelHandler with TensorFlow using tfx-bsl\n",
Review Comment:
```suggestion
"## KeyedModelHandler with TensorFlow using `tfx-bsl`\n",
```
##########
examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb:
##########
@@ -0,0 +1,647 @@
+{
+ "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": {
+ "cellView": "form",
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow\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_tfx.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_tfx.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 the [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/) using [tfx-bsl](https://github.com/tensorflow/tfx-bsl).\n",
+ "\n",
+ "This approach should only be used when the trained model requires a `tf.Example` input. If you have `numpy` or `tf.Tensor` inputs, please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb) instead which demonstrates the use built-in model handlers for TensorFlow in Beam SDK starting 2.46.0.\n",
+ "\n",
+ "Beam's Runinference transform accepts a ModelHandler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) using `CreateModelHandler`.\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",
+ "- Import [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl).\n",
+ "- Build a simple TensorFlow model.\n",
+ "- Set up example data\n",
+ "- Run those examples with `tfx-bsl` model handler and get a prediction inside an Apache Beam pipeline."
Review Comment:
```suggestion
"- Run those examples with the `tfx-bsl` model handler and get a prediction inside an Apache Beam pipeline."
```
##########
examples/notebooks/beam-ml/run_inference_tensorflow.ipynb:
##########
@@ -67,21 +67,18 @@
"source": [
"This notebook demonstrates the use of the [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/).\n",
"\n",
- "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",
+ "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 `tf.Tensor` as an input.\n",
"\n",
- "Beam's Runinference transform also accepts a ModelHandler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) using `CreateModelHandler`.\n",
+ "If your model needs input of type `tf.Example` then please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb).\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",
Review Comment:
```suggestion
"a variety of machine learning models. For more information about the RunInference API, see [Get started with AI/ML pipelines](https://beam.apache.org/documentation/ml/overview/) in the Apache Beam documentation.\n",
```
We should update this link.
##########
examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb:
##########
@@ -0,0 +1,647 @@
+{
+ "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": {
+ "cellView": "form",
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow\n",
Review Comment:
The notebooks shouldn't have the same title. Maybe change this one to:
Apache Beam RunInference with tfx-bsl
##########
examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb:
##########
@@ -0,0 +1,647 @@
+{
+ "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": {
+ "cellView": "form",
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow\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_tfx.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_tfx.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 the [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/) using [tfx-bsl](https://github.com/tensorflow/tfx-bsl).\n",
+ "\n",
+ "This approach should only be used when the trained model requires a `tf.Example` input. If you have `numpy` or `tf.Tensor` inputs, please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb) instead which demonstrates the use built-in model handlers for TensorFlow in Beam SDK starting 2.46.0.\n",
+ "\n",
+ "Beam's Runinference transform accepts a ModelHandler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) using `CreateModelHandler`.\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",
Review Comment:
```suggestion
"a variety of machine learning models. For more information about the RunInference API, see [Get started with AI/ML pipelines](https://beam.apache.org/documentation/ml/overview/) in the Apache Beam documentation.\n",
```
##########
examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb:
##########
@@ -0,0 +1,647 @@
+{
+ "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": {
+ "cellView": "form",
+ "id": "fFjof1NgAJwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A8xNRyZMW1yK"
+ },
+ "source": [
+ "# Apache Beam RunInference with TensorFlow\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_tfx.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_tfx.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 the [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/) using [tfx-bsl](https://github.com/tensorflow/tfx-bsl).\n",
+ "\n",
+ "This approach should only be used when the trained model requires a `tf.Example` input. If you have `numpy` or `tf.Tensor` inputs, please look at this [notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb) instead which demonstrates the use built-in model handlers for TensorFlow in Beam SDK starting 2.46.0.\n",
+ "\n",
+ "Beam's Runinference transform accepts a ModelHandler generated from [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl) using `CreateModelHandler`.\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",
+ "- Import [`tfx-bsl`](https://github.com/tensorflow/tfx-bsl).\n",
+ "- Build a simple TensorFlow model.\n",
+ "- Set up example data\n",
+ "- Run those examples with `tfx-bsl` model handler and get a prediction inside an Apache Beam pipeline."
+ ],
+ "metadata": {
+ "id": "HrCtxslBGK8Z"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Before you begin\n",
+ "Complete the following setup steps.\n",
+ "\n",
+ "First, import `tfx-bsl`."
+ ],
+ "metadata": {
+ "id": "HrCtxslBGK8A"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Creation of a ModelHandler is supported in `tfx-bsl` versions 1.10 and later."
+ ],
+ "metadata": {
+ "id": "gVCtGOKTHMm4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jBakpNZnAhqk"
+ },
+ "source": [
+ "!pip install tfx_bsl==1.10.0 --quiet\n",
+ "!pip install protobuf --quiet\n",
+ "!pip install apache_beam --quiet"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "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": 2,
+ "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",
+ "from tensorflow_serving.apis import prediction_log_pb2\n",
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "import tfx_bsl\n",
+ "from tfx_bsl.public.beam.run_inference import CreateModelHandler\n",
+ "from tfx_bsl.public import tfxio\n",
+ "from tfx_bsl.public.proto import model_spec_pb2\n",
+ "from tensorflow_metadata.proto.v0 import schema_pb2\n",
+ "\n",
+ "import numpy\n",
+ "\n",
+ "from typing import Dict, Text, Any, Tuple, List\n",
+ "\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "project = \"PROJECT_ID\"\n",
+ "bucket = \"BUCKET_NAME\"\n",
+ "\n",
+ "save_model_dir_multiply = f'gs://{bucket}/tfx-inference/model/multiply_five/v1/'\n"
+ ],
+ "execution_count": 12,
+ "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": "rIwD_qEpX7Gu"
+ },
+ "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": "c5adb7ec-285b-401e-f9be-1e9b83c6d0ba"
+ },
+ "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": 4,
+ "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": "e3bb5079-5cb8-4fe4-eb8d-d3d13d5f9f0c"
+ },
+ "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": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1/1 [==============================] - 0s 94ms/step\n",
+ "Test Examples [20, 40, 60, 90]\n",
+ "Predictions [[ 9.201942]\n",
+ " [16.40566 ]\n",
+ " [23.609379]\n",
+ " [34.41496 ]]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## RunInference with Tensorflow using tfx-bsl\n",
+ "In versions 1.10.0 and later of `tfx-bsl`, you can\n",
+ "create a TensorFlow `ModelHandler` for use with Apache Beam. 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",
Review Comment:
```suggestion
"create a TensorFlow `ModelHandler` for use with Apache Beam. For more information about the RunInference API, see [Get started with AI/ML pipelines](https://beam.apache.org/documentation/ml/overview/) in the Apache Beam documentation.\n",
```
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[GitHub] [beam] riteshghorse commented on pull request #26105: [Python] Separate out notebooks for tensorflow with tfx and built-in model handler
Posted by "riteshghorse (via GitHub)" <gi...@apache.org>.
riteshghorse commented on PR #26105:
URL: https://github.com/apache/beam/pull/26105#issuecomment-1496501862
Thanks for the review! Addressed the comments.
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