From 4d580ac7c369f69e4143487b6e3a82fe77cca47e Mon Sep 17 00:00:00 2001 From: Michelle Casbon Date: Wed, 25 Oct 2023 11:35:02 -0700 Subject: [PATCH] Remove broken notebook deploy.ipynb. Relies on a call to `t5_mesh_transformer`, which uses an outdated pip package that is no longer maintained and contains broken references, e.g.: ``` AttributeError: module 'tensorflow.python.framework.ops' has no attribute 'register_tensor_conversion_function' ``` PiperOrigin-RevId: 576593757 --- notebooks/t5-deploy.ipynb | 271 -------------------------------------- 1 file changed, 271 deletions(-) delete mode 100644 notebooks/t5-deploy.ipynb diff --git a/notebooks/t5-deploy.ipynb b/notebooks/t5-deploy.ipynb deleted file mode 100644 index 0ba1faa9..00000000 --- a/notebooks/t5-deploy.ipynb +++ /dev/null @@ -1,271 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "zX6H255Agj68" - }, - "source": [ - "\u003ca href=\"https://colab.research.google.com/github/google-research/text-to-text-transfer-transfrormer/blob/main/notebooks/t5-deploy.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Yo_HOomXe1f2" - }, - "source": [ - "##### Copyright 2020 The T5 Authors\n", - "\n", - "Licensed under the Apache License, Version 2.0 (the \"License\");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Rz9fAJ8PexKB" - }, - "outputs": [], - "source": [ - "# Copyright 2020 The T5 Authors. All Rights Reserved.\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# 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, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "# ==============================================================================" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "geoZEiaLdGfR" - }, - "source": [ - "# T5 SavedModel Export and Inference\n", - "\n", - "This notebook guides you through the process of exporting a [T5](https://github.com/google-research/text-to-text-transformer) `SavedModel` for inference. It uses the fine-tuned checkpoints in the [T5 Closed Book QA](https://github.com/google-research/google-research/tree/main/t5_closed_book_qa) repository for the [Natural Questions](https://ai.google.com/research/NaturalQuestions/) task as an example, but the same process will work for any model trained with the `t5` library.\n", - "\n", - "For more general usage of the `t5` library, please see the main [github repo](https://github.com/google-research/text-to-text-transfer-transformer) and fine-tuning [colab notebook](https://goo.gle/t5-colab).\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "WtS5hODBKtR_" - }, - "source": [ - "## Install T5 Library" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "UHCx-R4M-D0a" - }, - "outputs": [], - "source": [ - "!pip install -q t5\n", - "!git clone https://github.com/google-research/google-research.git --depth=1\n", - "# Add closed-book qa library to Python path.\n", - "%env PYTHONPATH=\"/content/google-research/:/content/google-research/t5_closed_book_qa:${PYTHONPATH}\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "IFuyCiHpLCh7" - }, - "source": [ - "## Export `SavedModel` to local storage\n", - "\n", - "NOTE: This will take a while for XL and XXL." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "both", - "colab": {}, - "colab_type": "code", - "id": "-Y7QSepo9a8H" - }, - "outputs": [], - "source": [ - "MODEL = \"small_ssm_nq\" #@param[\"small_ssm_nq\", \"t5.1.1.xl_ssm_nq\", \"t5.1.1.xxl_ssm_nq\"]\n", - "\n", - "import os\n", - "\n", - "saved_model_dir = f\"/content/{MODEL}\"\n", - "\n", - "!t5_mesh_transformer \\\n", - " --module_import=\"t5_cbqa.tasks\" \\\n", - " --model_dir=\"gs://t5-data/pretrained_models/cbqa/{MODEL}\" \\\n", - " --use_model_api \\\n", - " --mode=\"export_predict\" \\\n", - " --export_dir=\"{saved_model_dir}\"\n", - "\n", - "saved_model_path = os.path.join(saved_model_dir, max(os.listdir(saved_model_dir)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "JUH5BkcYK3At" - }, - "source": [ - "## Load `SavedModel` and create helper functions for inference\n", - "\n", - "NOTE: This will take a while for XL and XXL." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "xiBSnGuu-em0" - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "import tensorflow_text # Required to run exported model.\n", - "\n", - "model = tf.saved_model.load(saved_model_path, [\"serve\"])\n", - "\n", - "def predict_fn(x):\n", - " return model.signatures['serving_default'](tf.constant(x))['outputs'].numpy()\n", - "\n", - "def answer(question):\n", - " return predict_fn([question])[0].decode('utf-8')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "HSlRnhz7VpTu" - }, - "source": [ - "## Ask some questions\n", - "\n", - "We must prefix each question with the `nq question:` prompt since T5 is a multitask model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "CE1bO4hw--Zh" - }, - "outputs": [], - "source": [ - "for question in [\"nq question: where is google's headquarters\",\n", - " \"nq question: what is the most populous country in the world\",\n", - " \"nq question: name a member of the beatles\",\n", - " \"nq question: how many teeth do humans have\"]:\n", - " print(answer(question))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "BlTOrC7iaCZD" - }, - "source": [ - "## Package in Docker image" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ndK6zIryaKTX" - }, - "source": [ - "```bash\n", - "MODEL_NAME=model-name\n", - "SAVED_MODEL_PATH=/path/to/export/dir\n", - "\n", - "# Download the TensorFlow Serving Docker image and repo:\n", - "docker pull tensorflow/serving:nightly\n", - "\n", - "# First, run a serving image as a daemon:\n", - "docker run -d --name serving_base tensorflow/serving:nightly\n", - "\n", - "# Next, copy your `SavedModel` to the container's model folder:\n", - "docker cp $SAVED_MODEL_PATH serving_base:/models/$MODEL_NAME\n", - "\n", - "# Now, commit the container that's serving your model:\n", - "docker commit --change \"ENV MODEL_NAME ${MODEL_NAME}\" serving_base $MODEL_NAME\n", - "\n", - "# Finally, save the image to a tar file:\n", - "docker save $MODEL_NAME -o $MODEL_NAME.tar\n", - "\n", - "# You can now stop `serving_base`:\n", - "docker kill serving_base\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "WYQiZke3nXD_" - }, - "source": [ - "```bash\n", - "docker run -t --rm -p 8501:8501 --name $MODEL_NAME-server $MODEL_NAME \u0026\n", - "\n", - "curl -d '{\"inputs\": [\"nq question: what is the most populous country?\"]}' \\\n", - " -X POST http://localhost:8501/v1/models/$MODEL_NAME:predict\n", - "\n", - "docker stop $MODEL_NAME-server\n", - "```" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [ - "Yo_HOomXe1f2" - ], - "name": "t5-deploy", - "private_outputs": true, - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -}