harrisonlimh commented on code in PR #24529:
URL: https://github.com/apache/beam/pull/24529#discussion_r1040251037
##########
examples/notebooks/beam-ml/run_inference_tensorflow_hub.ipynb:
##########
@@ -0,0 +1,307 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "634de4ce-07f7-4003-8ff6-d3f58b41a79d"
+ },
+ "source": [
+ "##### Copyright 2022 Google Inc.\n",
+ "\n",
+ "Licensed under the Apache License, 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.\n",
+ "-->\n"
+ ],
+ "id": "634de4ce-07f7-4003-8ff6-d3f58b41a79d"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3ac8fc4a-a0ef-47b9-bd80-10801eebe13e"
+ },
+ "source": [
+ "# RunInference with Sentenced T5 Model from TensorFlow Hub\n",
+ "\n",
+ "\n",
+ "In this notebook, we walk through the use of the RunInference
transform with a [sentence encoder built on T5
model](https://tfhub.dev/google/sentence-t5/st5-base/1) and testing it locally
with Interactive Runner.\n"
+ ],
+ "id": "3ac8fc4a-a0ef-47b9-bd80-10801eebe13e"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3402ecc9-28d6-4226-99b1-147a2d23b7a9"
+ },
+ "source": [
+ "## Install and import packages."
+ ],
+ "id": "3402ecc9-28d6-4226-99b1-147a2d23b7a9"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "b22e51ad-cbf2-4dfe-a1c6-3ad17d5a3749",
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "!pip install apache_beam[gcp]==2.41.0\n",
+ "!pip install apache-beam[interactive]==2.41.0\n",
+ "!pip install tensorflow==2.10.0\n",
+ "!pip install tensorflow_text==2.10.0\n",
+ "!pip install keras==2.10.0\n",
+ "!pip install tfx_bsl==1.10.0\n",
+ "!pip install pillow==8.4.0"
+ ],
+ "id": "b22e51ad-cbf2-4dfe-a1c6-3ad17d5a3749"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "f313a508-59ea-47ed-86eb-c9c8e67785f2",
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import importlib\n",
+ "\n",
+ "import numpy as np\n",
+ "import tensorflow as tf\n",
+ "import tensorflow_hub as hub\n",
+ "import tensorflow_text\n",
+ "\n",
+ "from tensorflow import keras\n",
+ "\n",
+ "from typing import Any\n",
+ "from typing import Dict\n",
+ "from typing import Iterable\n",
+ "from typing import Optional\n",
+ "from typing import Sequence\n",
+ "\n",
+ "import apache_beam as beam\n",
+ "import apache_beam.runners.interactive.interactive_beam as ib\n",
+ "\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.ml.inference.base import ModelHandler\n",
+ "from apache_beam.ml.inference.base import PredictionResult\n",
+ "from apache_beam.internal import pickler\n",
+ "from apache_beam.runners.runner import PipelineResult\n",
+ "from apache_beam.runners.interactive.interactive_runner import
InteractiveRunner\n",
+ "\n",
+ "from tfx_bsl.public.beam.run_inference import CreateModelHandler\n",
+ "from tfx_bsl.public.proto import model_spec_pb2"
+ ],
+ "id": "f313a508-59ea-47ed-86eb-c9c8e67785f2"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "74db0203-3d26-4bc4-8271-81fad9756297"
+ },
+ "source": [
+ "## Create a Keras Model from TensorFlow Hub Image"
+ ],
+ "id": "74db0203-3d26-4bc4-8271-81fad9756297"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "r1fgn5I_-mzA"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import auth\n",
+ "auth.authenticate_user()"
+ ],
+ "id": "r1fgn5I_-mzA"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "2ff8e394-f577-4dea-bef9-a4f4528c1378"
+ },
+ "outputs": [],
+ "source": [
+ "PROJECT_ID = '<Project Id>'\n",
+ "GCS_BUCKET = '<GCS Bucket>'\n",
+ "\n",
+ "MODEL_PATH = f'{GCS_BUCKET}/st5-base/1'"
+ ],
+ "id": "2ff8e394-f577-4dea-bef9-a4f4528c1378"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ccaede25-1c1a-4ec4-9296-25c9a2ac43d7"
+ },
+ "outputs": [],
+ "source": [
+ "inp = tf.keras.layers.Input(shape=[], dtype=tf.string,
name='input')\n",
+ "hub_url = \"https://tfhub.dev/google/sentence-t5/st5-base/1\"\n";,
+ "imported = hub.KerasLayer(hub_url)\n",
+ "outp = imported(inp)\n",
+ "model = tf.keras.Model(inp, outp)\n",
+ "\n",
+ "# Sentenced-T5 model returns a 768-dimensional vector for an English
text input.\n",
+ "# Note the 'input' that we will pass in as example's feature key
name.\n",
+ "model.summary()"
+ ],
+ "id": "ccaede25-1c1a-4ec4-9296-25c9a2ac43d7"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "29803d5b-93b9-41fc-b414-f7c737c5d7bc"
+ },
+ "source": [
+ "## Save the model with a TF function definition for RunInference()"
+ ],
+ "id": "29803d5b-93b9-41fc-b414-f7c737c5d7bc"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "704abfca-5956-4fc1-9717-4c6d5bf2db8e"
+ },
+ "outputs": [],
+ "source": [
+ "RAW_DATA_PREDICT_SPEC = {\n",
+ " 'input': tf.io.FixedLenFeature([], tf.string),\n",
+ "}\n",
+ "\n",
+ "@tf.function(input_signature=[tf.TensorSpec(shape=[None],
dtype=tf.string)])\n",
+ "def call(serialized_examples):\n",
+ " features = tf.io.parse_example(serialized_examples,
RAW_DATA_PREDICT_SPEC)\n",
+ " return model(features)\n",
+ "\n",
+ "tf.saved_model.save(model, MODEL_PATH, signatures={'serving_default':
call})"
+ ],
+ "id": "704abfca-5956-4fc1-9717-4c6d5bf2db8e"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7b56569d-e540-44ed-a46a-9cec886522f6"
+ },
+ "source": [
+ "## Create and test a RunInference pipeline locally"
Review Comment:
Done.
##########
examples/notebooks/beam-ml/run_inference_tensorflow_hub.ipynb:
##########
@@ -0,0 +1,307 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "634de4ce-07f7-4003-8ff6-d3f58b41a79d"
+ },
+ "source": [
+ "##### Copyright 2022 Google Inc.\n",
+ "\n",
+ "Licensed under the Apache License, 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.\n",
+ "-->\n"
+ ],
+ "id": "634de4ce-07f7-4003-8ff6-d3f58b41a79d"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3ac8fc4a-a0ef-47b9-bd80-10801eebe13e"
+ },
+ "source": [
+ "# RunInference with Sentenced T5 Model from TensorFlow Hub\n",
+ "\n",
+ "\n",
+ "In this notebook, we walk through the use of the RunInference
transform with a [sentence encoder built on T5
model](https://tfhub.dev/google/sentence-t5/st5-base/1) and testing it locally
with Interactive Runner.\n"
+ ],
+ "id": "3ac8fc4a-a0ef-47b9-bd80-10801eebe13e"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3402ecc9-28d6-4226-99b1-147a2d23b7a9"
+ },
+ "source": [
+ "## Install and import packages."
+ ],
+ "id": "3402ecc9-28d6-4226-99b1-147a2d23b7a9"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "b22e51ad-cbf2-4dfe-a1c6-3ad17d5a3749",
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "!pip install apache_beam[gcp]==2.41.0\n",
+ "!pip install apache-beam[interactive]==2.41.0\n",
+ "!pip install tensorflow==2.10.0\n",
+ "!pip install tensorflow_text==2.10.0\n",
+ "!pip install keras==2.10.0\n",
+ "!pip install tfx_bsl==1.10.0\n",
+ "!pip install pillow==8.4.0"
+ ],
+ "id": "b22e51ad-cbf2-4dfe-a1c6-3ad17d5a3749"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "f313a508-59ea-47ed-86eb-c9c8e67785f2",
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import importlib\n",
+ "\n",
+ "import numpy as np\n",
+ "import tensorflow as tf\n",
+ "import tensorflow_hub as hub\n",
+ "import tensorflow_text\n",
+ "\n",
+ "from tensorflow import keras\n",
+ "\n",
+ "from typing import Any\n",
+ "from typing import Dict\n",
+ "from typing import Iterable\n",
+ "from typing import Optional\n",
+ "from typing import Sequence\n",
+ "\n",
+ "import apache_beam as beam\n",
+ "import apache_beam.runners.interactive.interactive_beam as ib\n",
+ "\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.ml.inference.base import ModelHandler\n",
+ "from apache_beam.ml.inference.base import PredictionResult\n",
+ "from apache_beam.internal import pickler\n",
+ "from apache_beam.runners.runner import PipelineResult\n",
+ "from apache_beam.runners.interactive.interactive_runner import
InteractiveRunner\n",
+ "\n",
+ "from tfx_bsl.public.beam.run_inference import CreateModelHandler\n",
+ "from tfx_bsl.public.proto import model_spec_pb2"
+ ],
+ "id": "f313a508-59ea-47ed-86eb-c9c8e67785f2"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "74db0203-3d26-4bc4-8271-81fad9756297"
+ },
+ "source": [
+ "## Create a Keras Model from TensorFlow Hub Image"
+ ],
+ "id": "74db0203-3d26-4bc4-8271-81fad9756297"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "r1fgn5I_-mzA"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import auth\n",
+ "auth.authenticate_user()"
+ ],
+ "id": "r1fgn5I_-mzA"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "2ff8e394-f577-4dea-bef9-a4f4528c1378"
+ },
+ "outputs": [],
+ "source": [
+ "PROJECT_ID = '<Project Id>'\n",
+ "GCS_BUCKET = '<GCS Bucket>'\n",
+ "\n",
+ "MODEL_PATH = f'{GCS_BUCKET}/st5-base/1'"
+ ],
+ "id": "2ff8e394-f577-4dea-bef9-a4f4528c1378"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ccaede25-1c1a-4ec4-9296-25c9a2ac43d7"
+ },
+ "outputs": [],
+ "source": [
+ "inp = tf.keras.layers.Input(shape=[], dtype=tf.string,
name='input')\n",
+ "hub_url = \"https://tfhub.dev/google/sentence-t5/st5-base/1\"\n";,
+ "imported = hub.KerasLayer(hub_url)\n",
+ "outp = imported(inp)\n",
+ "model = tf.keras.Model(inp, outp)\n",
+ "\n",
+ "# Sentenced-T5 model returns a 768-dimensional vector for an English
text input.\n",
+ "# Note the 'input' that we will pass in as example's feature key
name.\n",
+ "model.summary()"
+ ],
+ "id": "ccaede25-1c1a-4ec4-9296-25c9a2ac43d7"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "29803d5b-93b9-41fc-b414-f7c737c5d7bc"
+ },
+ "source": [
+ "## Save the model with a TF function definition for RunInference()"
+ ],
+ "id": "29803d5b-93b9-41fc-b414-f7c737c5d7bc"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "704abfca-5956-4fc1-9717-4c6d5bf2db8e"
+ },
+ "outputs": [],
+ "source": [
+ "RAW_DATA_PREDICT_SPEC = {\n",
+ " 'input': tf.io.FixedLenFeature([], tf.string),\n",
+ "}\n",
+ "\n",
+ "@tf.function(input_signature=[tf.TensorSpec(shape=[None],
dtype=tf.string)])\n",
+ "def call(serialized_examples):\n",
+ " features = tf.io.parse_example(serialized_examples,
RAW_DATA_PREDICT_SPEC)\n",
+ " return model(features)\n",
+ "\n",
+ "tf.saved_model.save(model, MODEL_PATH, signatures={'serving_default':
call})"
+ ],
+ "id": "704abfca-5956-4fc1-9717-4c6d5bf2db8e"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7b56569d-e540-44ed-a46a-9cec886522f6"
+ },
+ "source": [
+ "## Create and test a RunInference pipeline locally"
+ ],
+ "id": "7b56569d-e540-44ed-a46a-9cec886522f6"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "fad13b30-b159-425c-8c15-a41301abd3a4"
+ },
+ "outputs": [],
+ "source": [
+ "# Creates TensorFlow Example to feed to the ModelHandler.\n",
Review Comment:
Done.
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