From 8f6f07ec13993ee304d6eda53d7e24f0213f6d08 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Tue, 21 Dec 2021 08:52:14 -0600 Subject: [PATCH 01/30] Added sample for geospatial classification --- .../geospatial-classification/README.ipynb | 1384 ++++ .../geospatial-classification/e2e_test.py | 376 + .../geospatial-classification/img/inputs.png | Bin 0 -> 334925 bytes .../geospatial-classification/img/outputs.png | Bin 0 -> 172207 bytes .../geospatial-classification/img/padding.png | Bin 0 -> 54093 bytes .../img/sparse_labels.png | Bin 0 -> 249461 bytes .../img/training.png | Bin 0 -> 19905 bytes .../labeled_geospatial_data.csv | 6556 +++++++++++++++++ .../requirements-test.txt | 4 + .../serving_app/Dockerfile | 9 + .../serving_app/main.py | 56 + .../serving_app/predict.py | 28 + .../serving_app/requirements.txt | 3 + .../geospatial-classification/trainer/task.py | 132 + 14 files changed, 8548 insertions(+) create mode 100644 people-and-planet-ai/geospatial-classification/README.ipynb create mode 100644 people-and-planet-ai/geospatial-classification/e2e_test.py create mode 100644 people-and-planet-ai/geospatial-classification/img/inputs.png create mode 100644 people-and-planet-ai/geospatial-classification/img/outputs.png create mode 100644 people-and-planet-ai/geospatial-classification/img/padding.png create mode 100644 people-and-planet-ai/geospatial-classification/img/sparse_labels.png create mode 100644 people-and-planet-ai/geospatial-classification/img/training.png create mode 100644 people-and-planet-ai/geospatial-classification/labeled_geospatial_data.csv create mode 100644 people-and-planet-ai/geospatial-classification/requirements-test.txt create mode 100644 people-and-planet-ai/geospatial-classification/serving_app/Dockerfile create mode 100644 people-and-planet-ai/geospatial-classification/serving_app/main.py create mode 100644 people-and-planet-ai/geospatial-classification/serving_app/predict.py create mode 100644 people-and-planet-ai/geospatial-classification/serving_app/requirements.txt create mode 100644 people-and-planet-ai/geospatial-classification/trainer/task.py diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb new file mode 100644 index 000000000000..106ae1253b42 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -0,0 +1,1384 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 260, + "id": "77c0f36a", + "metadata": {}, + "outputs": [], + "source": [ + "###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n", + "\n", + "# Licensed to the Apache Software Foundation (ASF) under one\n", + "# or more contributor license agreements. See the NOTICE file\n", + "# distributed with this work for additional information\n", + "# regarding copyright ownership. The ASF licenses this file\n", + "# to you under the Apache License, Version 2.0 (the\n", + "# \"License\"); you may not use this file except in compliance\n", + "# with the License. You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing,\n", + "# software distributed under the License is distributed on an\n", + "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n", + "# KIND, either express or implied. See the License for the\n", + "# specific language governing permissions and limitations\n", + "# under the License." + ] + }, + { + "cell_type": "markdown", + "id": "3683a5f8", + "metadata": {}, + "source": [ + "# Power Plant Predictions\n", + "\n", + "* **Time estimate**: 1 hour\n", + "* **Cost estimate**: less than $5.00 USD\n", + "\n", + "This is an interactive notebook that contains all of the code necessary to train an ML model for geospatial classification." + ] + }, + { + "cell_type": "markdown", + "id": "922968ad", + "metadata": {}, + "source": [ + "# Overview\n", + "\n", + "This notebook leverages geospatial data from [Google Earth Engine](https://earthengine.google.com/) and labeled data provided by the organization [Climate TRACE](https://www.climatetrace.org/). By combining these two data sources, you'll build and train a model that predicts whether or not a power plant is turned on and producing emissions." + ] + }, + { + "cell_type": "markdown", + "id": "8f15adff", + "metadata": {}, + "source": [ + "### Data _(inputs)_\n", + "\n", + "The data in this example consists of satellite images from [Sentinel-2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description), a wide-swath, high-resolution, multi-spectral imaging mission.\n", + "\n", + "When working with satellite data, each input image has the dimensions `[width, height, bands]`. Bands are measurements from specific satellite instruments for different ranges of the electromagnetic spectrum. For example, Sentinel-2 contains [13 spectral bands](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#bands). If you are familiar with image classification problems, you can think of the bands as similar to an image's RGB channels. However, when working with satellite data we generally have more than just 3 channels.\n", + "\n", + "![satellite_inputs](img/inputs.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d15be296", + "metadata": {}, + "source": [ + "### Labels _(outputs)_\n", + "\n", + "For each image that we give to the model, the output will be a binary indicator for each pixel in the image indicating whether or not that pixel represents a power plant that is turned on.\n", + "\n", + "In this example, the output dimensions will be `[width, height, classes]`, where classes is 1 because this is a binary classification problem (on or off).\n", + "\n", + "![satellite_outputs](img/outputs.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4dce50bc", + "metadata": {}, + "source": [ + "### Model _(function)_\n", + "\n", + "One problem you often run into when doing geospatial classification is sparse labels, meaning that we only have labels for a selection of points within a region. \n", + "\n", + "![sparse_labels](img/sparse_labels.png)\n", + "\n", + "In this example, we have a CSV file of labels. Each row in this file represents a power plant at a specific lat/lon and timestamp. To deal with the sparsity, at training time we'll prepare a dataset where each input image is a single pixel that we have a label for. We will then add a padding around that image. These padded pixels will not get predictions, but will help our model to make better predictions for the center point that we have a label for.\n", + "\n", + "For example, with a padding of 16, each 1 pixel input point would become a 33x33 image after the padding is added.\n", + "\n", + "![training](img/training.png)\n", + "\n", + "\n", + "At prediction time, we can pass in an image of any size. We'll add a layer of padding around the image, and will get predictions for each pixel except for the padding.\n", + "\n", + "To handle the variable input size, we'll use a fully convolutional neural network. Using this type of architecture will allow us to have variable input image sizes. Note that while the height and width of the image can be variable, the number of bands must be constant." + ] + }, + { + "cell_type": "markdown", + "id": "3547aec6", + "metadata": {}, + "source": [ + "## Steps summary\n", + "\n", + "Here's a quick summary of what you’ll go through:\n", + "\n", + "1. **Get the training data**:\n", + " Extract satellite data from [Earth Engine](https://earthengine.google.com/), combine it with the labels from Climate TRACE, and export to\n", + " [Cloud Storage](https://cloud.google.com/storage).\n", + "\n", + "1. **Run custom training job**:\n", + " Run a custom training job on [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) using a [pre-built training container](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers).\n", + "\n", + "1. **Deploy to Cloud Run**:\n", + " Deploy a web service in\n", + " [Cloud Run](https://cloud.google.com/run)\n", + " to get predictions using the trained model.\n", + "\n", + "1. **Get Predictions**:\n", + " Use the web service to get predictions for new data.\n", + "\n", + "1. **Visualize predictions**:\n", + " Visualize the preductions on a map.\n", + "\n", + "1. (Optional) **Delete the project** to avoid ongoing costs." + ] + }, + { + "cell_type": "markdown", + "id": "f0033a40", + "metadata": {}, + "source": [ + "# Before you begin" + ] + }, + { + "cell_type": "markdown", + "id": "980b439f-03bb-427f-ae61-4d6d5f066d15", + "metadata": {}, + "source": [ + "### Make sure you provide a regional bucket!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "246702e7", + "metadata": {}, + "outputs": [], + "source": [ + "#@title My Google Cloud resources\n", + "project = '' #@param {type:\"string\"}\n", + "bucket = '' #@param {type:\"string\"}\n", + "region = 'us-central1' #@param {type:\"string\"}\n", + "\n", + "# Validate the inputs.\n", + "if not project:\n", + " raise ValueError(f\"Please provide a value for 'project'\")\n", + "if not bucket:\n", + " raise ValueError(f\"Please provide a value for 'bucket'\")\n", + "if not region:\n", + " raise ValueError(f\"Please provide a value for 'region'\")\n", + "\n", + "# Authenticate\n", + "from google.colab import auth\n", + "\n", + "auth.authenticate_user()\n", + "print('Authenticated')\n", + "\n", + "!gcloud config set project {project}" + ] + }, + { + "cell_type": "markdown", + "id": "156ab72c", + "metadata": {}, + "source": [ + "## Getting the code\n", + "\n", + "Run ▶️ the following cell to download the code sample's source code." + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "id": "c2848f6d", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the sample source code.\n", + "\n", + "## TODO" + ] + }, + { + "cell_type": "markdown", + "id": "8a363277-2195-42b4-83e7-3e3921bb3db1", + "metadata": {}, + "source": [ + "### Install the Vertex AI SDK" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc25399d-7d5d-4050-9442-2ef2c7d83e82", + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install --user --upgrade google-cloud-aiplatform" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63dffaaf-5321-47e7-9dfa-ff82cd0e6303", + "metadata": {}, + "outputs": [], + "source": [ + "from google.cloud import aiplatform" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45965fcc-2ff1-4b27-8c20-6f6af7667271", + "metadata": {}, + "outputs": [], + "source": [ + "aiplatform.init(project=project, staging_bucket=bucket)" + ] + }, + { + "cell_type": "markdown", + "id": "4ec73dd9", + "metadata": {}, + "source": [ + "### Authenticate to Earth Engine\n", + "\n", + "In order to use the Earth Engine API, you'll need to have an Earth Engine account.\n", + "\n", + "To create an account, fill out the [registration form here.](https://signup.earthengine.google.com/#!/)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4cb4cd13", + "metadata": {}, + "outputs": [], + "source": [ + "import ee\n", + "ee.Authenticate()\n", + "ee.Initialize()" + ] + }, + { + "cell_type": "markdown", + "id": "980b0418", + "metadata": {}, + "source": [ + "# Get the training data\n", + "\n", + "The training data in this sample comes from two places: \n", + "\n", + "1. The satellite images will be extracted from *Earth Engine*.\n", + "\n", + "2. The labels are provided in a *csv file* that indicates whether a power plant is turned on or off at a particular timestamp. \n", + "\n", + "For each row in the CSV file, we need to extract the corresponding Sentinel image taken at that specific latitude/longitude and timestamp. We'll export this image data, along with the corresponding label (on/off), to Cloud Storage.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9b0dd3fd", + "metadata": {}, + "outputs": [], + "source": [ + "# Import libraries\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "94f60a82", + "metadata": {}, + "outputs": [], + "source": [ + "# Define constants\n", + "\n", + "BANDS = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']\n", + "LABEL = 'label'" + ] + }, + { + "cell_type": "markdown", + "id": "beba81d1", + "metadata": {}, + "source": [ + "### Import labels\n", + "\n", + "First, we import the csv file that contains the labels." + ] + }, + { + "cell_type": "code", + "execution_count": 584, + "id": "42190dcf", + "metadata": {}, + "outputs": [], + "source": [ + "labels_dataframe = pd.read_csv('labeled_geospatial_data.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "1cdb7d16-a9c7-4be8-b403-ccb10dcad261", + "metadata": {}, + "source": [ + "Each row in this dataframe represents a power plant at a particular timestamp. \n", + "\n", + "The label column indicates whether or not the power plant was turned **on (1)** or **off (0)** at that timestamp." + ] + }, + { + "cell_type": "code", + "execution_count": 585, + "id": "3a0e80f5-23ce-46df-b81d-742068d46181", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " timestamp lat lon label\n", + "0 2020-07-03 16:32:41.397000+00:00 39.11613 -84.80529 1\n", + "1 2018-06-09 16:25:19.280000+00:00 39.11613 -84.80529 1\n", + "2 2017-11-24 16:36:14.460000+00:00 39.11613 -84.80529 0\n", + "3 2019-11-01 16:32:42.327000+00:00 39.11613 -84.80529 0\n", + "4 2020-05-09 16:32:43.614000+00:00 39.11613 -84.80529 1" + ] + }, + "execution_count": 585, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels_dataframe.head()" + ] + }, + { + "cell_type": "markdown", + "id": "3aa51b23", + "metadata": {}, + "source": [ + "Next, we import this data into an Earth Engine [`Feature Collection`](https://developers.google.com/earth-engine/apidocs/ee-featurecollection).\n", + "\n", + "In Earth Engine, a [`Feature`](https://developers.google.com/earth-engine/guides/features) is an object with a _geometry property_ storing a [`Geometry`](https://developers.google.com/earth-engine/guides/geometries) object, and a _properties property_ storing a dictionary of other properties. Groups of related `Features` can be combined into a `FeatureCollection` to enable additional operations on the entire set such as filtering, sorting, and rendering." + ] + }, + { + "cell_type": "code", + "execution_count": 659, + "id": "3a8b42dd", + "metadata": {}, + "outputs": [], + "source": [ + "def create_label_fc(path):\n", + " '''Creates a FeatureCollection from the label dataframe.'''\n", + "\n", + " labels_dataframe = pd.read_csv(path)\n", + " num_examples = dataframe.shape[0]\n", + " data_dict = dataframe.to_dict()\n", + " feats = []\n", + " properties = ['timestamp', 'label']\n", + " for idx in np.arange(num_examples):\n", + " feat_dict = {}\n", + " geometry = ee.Geometry.Point([data_dict['lon'][idx], data_dict['lat'][idx]]) \n", + " for feature in properties:\n", + " feat_dict[feature] = data_dict[feature][idx]\n", + " feat = ee.Feature(geometry, feat_dict)\n", + " feats.append(feat)\n", + " return ee.FeatureCollection(feats)" + ] + }, + { + "cell_type": "code", + "execution_count": 660, + "id": "6eb9f78d", + "metadata": {}, + "outputs": [], + "source": [ + "label_fc = create_label_fc('labeled_geospatial_data.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "cb421ba4-c9b1-4d81-b543-656766fac47c", + "metadata": {}, + "source": [ + "Let's take a look at the first element in the `FeatureCollection`. You can see that this is equivalent to the first element in our dataframe, but just in a different format." + ] + }, + { + "cell_type": "code", + "execution_count": 662, + "id": "eeb5b6c2-4f53-481b-96d1-07a39415988a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'geometry': {'coordinates': [-84.80529, 39.11613], 'type': 'Point'},\n", + " 'id': '0',\n", + " 'properties': {'label': 1, 'timestamp': '2020-07-03 16:32:41.397000+00:00'},\n", + " 'type': 'Feature'}\n" + ] + } + ], + "source": [ + "pprint(label_fc.first().getInfo())" + ] + }, + { + "cell_type": "markdown", + "id": "1cc8b4a2", + "metadata": {}, + "source": [ + "Next, we define a preprocessing function that will reformat the timestamp into two new properties: `start` and `end`." + ] + }, + { + "cell_type": "code", + "execution_count": 663, + "id": "264aea7a", + "metadata": {}, + "outputs": [], + "source": [ + "def format_date(feature):\n", + " '''Creates start date and end date properties.'''\n", + " \n", + " # Extract start and end dates\n", + " timestamp = ee.String(feature.get('timestamp')).split(' ').get(0)\n", + " year = ee.Number.parse(ee.String(timestamp).split('-').get(0))\n", + " month = ee.Number.parse(ee.String(timestamp).split('-').get(1))\n", + " day = ee.Number.parse(ee.String(timestamp).split('-').get(2))\n", + " start = ee.Date.fromYMD(year, month, day)\n", + " end = start.advance(1, 'day')\n", + "\n", + " # Create new feature\n", + " feature = feature.set({'start': start, 'end': end})\n", + "\n", + " return feature" + ] + }, + { + "cell_type": "markdown", + "id": "61a35408-f7f1-46bd-a73f-db4a48538313", + "metadata": {}, + "source": [ + "We map this preprocessing function across our `FeatureCollection`." + ] + }, + { + "cell_type": "code", + "execution_count": 664, + "id": "3132cdda", + "metadata": {}, + "outputs": [], + "source": [ + "label_fc = label_fc.map(format_date)" + ] + }, + { + "cell_type": "markdown", + "id": "f4524d75", + "metadata": {}, + "source": [ + "Let's examine the first feature in this collection to see what it looks like after the map operation. \n", + "\n", + "You can see that each feature has a `Geometry`, which is a specific latitude and longitude, as well as a set of properties:\n", + "\n", + "* `label` indicates whether the power plant is on (1) or off (0)\n", + "* `start` is the datetime for when this observation was made\n", + "* `end` is a datetime one day after `start`\n", + "* `timestamp` is the original timestamp pulled from the CSV file\n", + "\n", + "The `label` property is what we want our model to predict. And the `start` and `end` timestamps will be helpful when we extract the corresponding Sentinel images because each label is for a specific point in time." + ] + }, + { + "cell_type": "code", + "execution_count": 665, + "id": "01cc0ad2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'geometry': {'coordinates': [-84.80529, 39.11613], 'type': 'Point'},\n", + " 'id': '0',\n", + " 'properties': {'end': {'type': 'Date', 'value': 1593820800000},\n", + " 'label': 1,\n", + " 'start': {'type': 'Date', 'value': 1593734400000},\n", + " 'timestamp': '2020-07-03 16:32:41.397000+00:00'},\n", + " 'type': 'Feature'}\n" + ] + } + ], + "source": [ + "pprint(label_fc.first().getInfo())" + ] + }, + { + "cell_type": "markdown", + "id": "96967303", + "metadata": {}, + "source": [ + "### Merge labels + Sentinel image data" + ] + }, + { + "cell_type": "markdown", + "id": "fa236af4", + "metadata": {}, + "source": [ + "In Earth Engine, an [`ImageCollection`](https://developers.google.com/earth-engine/guides/ic_creating) is a stack or sequence of images. An [`Image`](https://developers.google.com/earth-engine/guides/image_overview) is composed of one or more bands and each band has its own name, data type, scale, mask and projection. The [`Sentinel-2`](https://developers.google.com/earth-engine/guides/ic_creating) dataset is represented as an `ImageCollection`, where each image in the collection is of a specific geographic location at a particular time.\n", + "\n", + "In the cell below, we write a function to extract the Sentinel image taken at the specific latitude/longitude and timestamp for each `Feature` in our `FeatureCollection`. \n", + "\n", + "To do this, we first filter the Sentinel-2 `ImageCollection` at the start/end dates for a particular `Feature`.\n", + "\n", + "Then, using the [`neighorboodToArray`](https://developers.google.com/earth-engine/api_docs#eeimageneighborhoodtoarray) method we create a new `FeatureCollection` that contains the satellite data for each band at the latitude and longitude of interest as well as a 16 pixel padding around that point.\n", + "\n", + "In the image below you can think of the purple box representing the lat/lon where the power plant is located. And around this pixel, we add the padding.\n", + "\n", + "![training](img/training.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 592, + "id": "a93f1947", + "metadata": {}, + "outputs": [], + "source": [ + "def get_neighboring_patch(feature):\n", + " '''Gets image pixel values for patch.'''\n", + " \n", + " # filter ImageCollection at start/end dates. \n", + " image = ee.ImageCollection('COPERNICUS/S2').filterDate(\n", + " feature.get('start'), feature.get('end')).select(BANDS).median()\n", + " \n", + " # extract pixel values at the lat/lon with a 16x16 padding\n", + " return ee.FeatureCollection([\n", + " image.neighborhoodToArray(ee.Kernel.square(16)).sampleRegions(\n", + " collection=ee.FeatureCollection([feature]), scale=10).first()\n", + " ])" + ] + }, + { + "cell_type": "markdown", + "id": "4d7a95a7", + "metadata": {}, + "source": [ + "We map this function across the `FeatureCollection` and flatten it so we have a single `FeatureCollection`." + ] + }, + { + "cell_type": "code", + "execution_count": 593, + "id": "6d85ec4a", + "metadata": {}, + "outputs": [], + "source": [ + "data = label_fc.map(get_neighboring_patch).flatten()" + ] + }, + { + "cell_type": "markdown", + "id": "21267b05-38cc-4f14-ab09-197baef26713", + "metadata": {}, + "source": [ + "Let's look at the properties for the first element in this new `FeatureCollection`. You can see that it still contains the `start`, `end`, `label`, and `timestamp` properties, but with has 13 additional properies, one for each spectral band." + ] + }, + { + "cell_type": "code", + "execution_count": 594, + "id": "69933ee9-8aa0-49f7-ad33-c0b17db329bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['system:index',\n", + " 'start',\n", + " 'end',\n", + " 'label',\n", + " 'timestamp',\n", + " 'B10',\n", + " 'B11',\n", + " 'B12',\n", + " 'B8A',\n", + " 'B1',\n", + " 'B2',\n", + " 'B3',\n", + " 'B4',\n", + " 'B5',\n", + " 'B6',\n", + " 'B7',\n", + " 'B8',\n", + " 'B9']" + ] + }, + "execution_count": 594, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.first().propertyNames().getInfo()" + ] + }, + { + "cell_type": "markdown", + "id": "8c79c514-ad63-4b85-8da2-ab8967fe8d5a", + "metadata": {}, + "source": [ + "The data contained in each band property is an array of shape 33x33.\n", + "\n", + "For example, here is the data for band B1 in the first element of our `FeatureCollection` expressed as a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 595, + "id": "a70c127e-7c45-4eab-914d-17a0c3a9e42c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1803 1803 1803 ... 1797 1797 1797]\n", + " [1803 1803 1803 ... 1797 1797 1797]\n", + " [1803 1803 1803 ... 1797 1797 1797]\n", + " ...\n", + " [1519 1519 1519 ... 1560 1560 1560]\n", + " [1519 1519 1519 ... 1560 1560 1560]\n", + " [1519 1519 1519 ... 1560 1560 1560]]\n", + "shape: (33, 33)\n" + ] + } + ], + "source": [ + "example_feature = np.array(data.first().get('B1').getInfo())\n", + "print(example_feature)\n", + "print('shape: ' + str(example_feature.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "2ab1318d", + "metadata": {}, + "source": [ + "### Create train/validation splits" + ] + }, + { + "cell_type": "markdown", + "id": "86cca925", + "metadata": {}, + "source": [ + "Before we can train an ML model, we need to split this data into training and validation datasets. We can easily do this by adding a random column to our `FeatureCollection` and then filtering the collection on that column." + ] + }, + { + "cell_type": "code", + "execution_count": 623, + "id": "6b4483b6", + "metadata": {}, + "outputs": [], + "source": [ + "data = data.randomColumn()" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "id": "4f2b67ae", + "metadata": {}, + "outputs": [], + "source": [ + "# 70% of data for training and 30% for validation\n", + "\n", + "training = data.filter(ee.Filter.lt('random', 0.7))\n", + "validation = data.filter(ee.Filter.gte('random', 0.7))" + ] + }, + { + "cell_type": "markdown", + "id": "c02607c8", + "metadata": {}, + "source": [ + "### Export data" + ] + }, + { + "cell_type": "markdown", + "id": "10c87eb9", + "metadata": {}, + "source": [ + "Lastly, we'll export the data to a Cloud Storage bucket. We'll export the data as [TFRecords](https://www.tensorflow.org/tutorials/load_data/tfrecord) and only export the bands and label properties.\n", + "\n", + "Later when we run the training job, we'll parse these TFRecords and feed them to the model." + ] + }, + { + "cell_type": "code", + "execution_count": 602, + "id": "53f2d867", + "metadata": {}, + "outputs": [], + "source": [ + "# Export data\n", + "\n", + "training_task = ee.batch.Export.table.toCloudStorage(\n", + " collection=training,\n", + " description=\"Training image export\",\n", + " bucket=bucket,\n", + " fileNamePrefix=\"geospatial_training\",\n", + " selectors= BANDS + [LABEL],\n", + " fileFormat='TFRecord')\n", + "\n", + "training_task.start()\n", + "\n", + "validation_task = ee.batch.Export.table.toCloudStorage(\n", + " collection=validation,\n", + " description=\"Validation image export\",\n", + " bucket=bucket,\n", + " fileNamePrefix=\"geospatial_validation\",\n", + " selectors= BANDS + [LABEL],\n", + " fileFormat='TFRecord')\n", + "\n", + "validation_task.start()" + ] + }, + { + "cell_type": "markdown", + "id": "dadeab16", + "metadata": {}, + "source": [ + "This export will take around 35 minutes. You can monitor the progress with the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4d111251", + "metadata": {}, + "outputs": [], + "source": [ + "pprint(ee.batch.Task.list())" + ] + }, + { + "cell_type": "markdown", + "id": "02b1b9dd", + "metadata": {}, + "source": [ + "# Run custom training job\n", + "\n", + "Once the export jobs have finished, we're ready to use that data to train a model on Vertex AI Training.\n", + "\n", + "The complete training code can be found in the `task.py` file.\n", + "\n", + "To run our custom training job on Vertex AI Training, we'll use the [pre-built containers](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers) provided by Vertex AI to run our training script.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4b99054-b843-485e-9cf0-c9f8ae739d1c", + "metadata": {}, + "outputs": [], + "source": [ + "job = aiplatform.CustomTrainingJob(\n", + " display_name=\"geospatial_model_training\",\n", + " script_path=\"task.py\",\n", + " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-5:latest\")" + ] + }, + { + "cell_type": "markdown", + "id": "a4baff79-0347-4452-afaa-9c042472f837", + "metadata": {}, + "source": [ + "The job will take around 10 minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5893ce1d-cdbe-43fa-98cc-221a7a2530d3", + "metadata": {}, + "outputs": [], + "source": [ + "model = job.run(args=[f'--bucket={bucket}'])" + ] + }, + { + "cell_type": "markdown", + "id": "c75499ed", + "metadata": {}, + "source": [ + "# Deploy to Cloud Run" + ] + }, + { + "cell_type": "markdown", + "id": "86cbab39", + "metadata": {}, + "source": [ + "Next, we use\n", + "[Cloud Run](https://cloud.google.com/run)\n", + "to deploy a web service that exposes a\n", + "[REST API](https://en.wikipedia.org/wiki/Representational_state_transfer) to\n", + "get predictions from our trained model.\n", + "\n", + "To run the web service, we configure Cloud Run to launch\n", + "[`gunicorn`](https://gunicorn.org)\n", + "on the container image we built.\n", + "\n", + "We also provide environment variables with the Google Cloud project ID, a Cloud Storage path, a Compute Engine region, as well as the container image itself.\n", + "These are the Google Cloud resources we want the web service to use when launching new jobs or creating new resources.\n", + "\n", + "Since calls to this web service could launch potentially expensive jobs in our project, we configure it to _only_ accept authenticated calls." + ] + }, + { + "cell_type": "markdown", + "id": "a0e28bff-0804-4093-acf2-739700b24c5f", + "metadata": {}, + "source": [ + "## Build container\n", + "\n", + "We use Cloud Build to build the container image. After the image is built, it is available in Container Registry." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c95ba749-b325-4700-ace9-c1c5af3996aa", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the Container Registry path for the sample container image.\n", + "app_container_image = f\"gcr.io/{project}/geospatial-classification/app\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56c8774f-b90d-47d8-8453-f01e05594751", + "metadata": {}, + "outputs": [], + "source": [ + "# Build and push the container image.\n", + "# https://cloud.google.com/sdk/gcloud/reference/builds/submit\n", + "!gcloud builds submit serving_app \\\n", + " --tag=\"{app_container_image}\" \\\n", + " --machine-type \"e2-highcpu-8\"" + ] + }, + { + "cell_type": "markdown", + "id": "9978056f-4467-4dda-85a5-84655ade9a11", + "metadata": {}, + "source": [ + "## Deploy app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13c299f1-e108-46bd-8b12-06af56a88444", + "metadata": {}, + "outputs": [], + "source": [ + "# Deploy the web service to Cloud Run.\n", + "# https://cloud.google.com/sdk/gcloud/reference/run/deploy\n", + "!gcloud run deploy \"geospatial-service\" \\\n", + " --image=\"{app_container_image}\" \\\n", + " --command=\"gunicorn\" \\\n", + " --args=\"--threads=8,--timeout=0,main:app\" \\\n", + " --region=\"us-central1\" \\\n", + " --memory=\"1G\" \\\n", + " --no-allow-unauthenticated" + ] + }, + { + "cell_type": "markdown", + "id": "5a32d4bf-4345-43f7-9371-aa151ad4f521", + "metadata": {}, + "source": [ + "Now we need the web service URL to make calls to the REST API we just exposed. We can use `gcloud run services describe` to get the web service URL.\n", + "\n", + "Since we only accept authorized calls in our web service, we also need to authenticate each call.\n", + "`gcloud` is already authenticated, so we can use `gcloud auth print-identity-token` to get quick access.\n", + "\n", + "> ℹ️ For more information on how to do authenticated calls in Cloud Run, see the\n", + "> [Authentication overview](https://cloud.google.com/run/docs/authenticating/overview) page." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "53f5e5e3-d5dd-4cb7-bb38-5b2e3b439ecd", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "# Get the web service URL.\n", + "# https://cloud.google.com/sdk/gcloud/reference/run/services/describe\n", + "service_url = subprocess.run(\n", + " [ 'gcloud', 'run', 'services', 'describe', 'geospatial-service',\n", + " f'--region={region}',\n", + " f'--format=get(status.url)',\n", + " ],\n", + " capture_output=True,\n", + ").stdout.decode('utf-8').strip()\n", + "print(f\"service_url: {service_url}\")\n", + "\n", + "# Get an identity token for authorized calls to our web service.\n", + "# https://cloud.google.com/sdk/gcloud/reference/auth/print-identity-token\n", + "identity_token = subprocess.run(\n", + " ['gcloud', 'auth', 'print-identity-token'],\n", + " capture_output=True,\n", + ").stdout.decode('utf-8').strip()\n", + "print(f\"identity_token: {identity_token}\")" + ] + }, + { + "cell_type": "markdown", + "id": "aa2c4dfe-b55d-4f80-a7eb-9ee86542ce59", + "metadata": {}, + "source": [ + "Finally, we can test that everything is working.\n", + "\n", + "We included a `ping` method in our web service just to make sure everything is working as expected.\n", + "It simply returns back the arguments we passed to the call, as well as a response saying that the call was successful.\n", + "\n", + "> ℹ️ This is a convenient way to make sure the web service is reachable, the authentication is working as expected, and the request arguments are passed correctly.\n", + "\n", + "We can use Python's\n", + "[`requests`](https://docs.python-requests.org)\n", + "library.\n", + "The web service was built to always accept [JSON](https://www.w3schools.com/whatis/whatis_json.asp)-encoded requests, and returns JSON-encoded responses.\n", + "\n", + "For a request to be successful, it must:\n", + "\n", + "* Be an [`HTTP POST`](https://developer.mozilla.org/en-US/docs/Web/HTTP/Methods/POST) request\n", + "* Contain the following headers:\n", + " * `Authorization: Bearer IDENTITY_TOKEN`\n", + " * `Content-Type: application/json`\n", + "* The data must be valid JSON, if no arguments are needed we can pass `{}` as an empty object.\n", + "\n", + "For ease of use, `requests.post` has a\n", + "[`json` parameter](https://docs.python-requests.org/en/master/user/quickstart/#more-complicated-post-requests)\n", + "that automatically attaches the header `Content-Type: application/json` and encodes our data into a JSON string." + ] + }, + { + "cell_type": "code", + "execution_count": 557, + "id": "1395bd18-e6eb-47cf-b0ca-7d7d04cce8f0", + "metadata": {}, + "outputs": [], + "source": [ + "import requests" + ] + }, + { + "cell_type": "code", + "execution_count": 558, + "id": "9025db3b-d409-46cf-9ce2-6f0209dfbea3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'args': 'Hello world!', 'response': 'Your request was successful! 🎉'}" + ] + }, + "execution_count": 558, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "requests.post(\n", + " url=f'{service_url}/ping',\n", + " headers={'Authorization': f'Bearer {identity_token}'},\n", + " json={'x': 42, 'message': 'Hello world!'},\n", + ").json()" + ] + }, + { + "cell_type": "markdown", + "id": "45d64a22-887a-4469-9ba3-668ec23f8667", + "metadata": {}, + "source": [ + "# Get Predictions" + ] + }, + { + "cell_type": "markdown", + "id": "462b7bf1-cf07-4a93-9cf3-0f9f31c13fa9", + "metadata": {}, + "source": [ + "Now that we know our app is up and running, we can use it to make predictions.\n", + "\n", + "Let's start by making a prediction for a particular power plant. To do this we will need to extract the Sentinel data from Earth Engine and send it in the body of the post requst to the prediction service.\n", + "\n", + "We'll start with a plant located at the coordinates -84.80529, 39.11613, and then extract the satellite data from October 2021." + ] + }, + { + "cell_type": "code", + "execution_count": 559, + "id": "a7f6b8c2-cab3-4030-8528-9afe644a800d", + "metadata": {}, + "outputs": [], + "source": [ + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 560, + "id": "570f7e8f-6f68-4433-aa29-ce0e3e4cd604", + "metadata": {}, + "outputs": [], + "source": [ + "plant_location = ee.Feature(ee.Geometry.Point([-84.80529, 39.11613]))" + ] + }, + { + "cell_type": "code", + "execution_count": 561, + "id": "9e477289-398c-4bcb-ab23-aee65ac92388", + "metadata": {}, + "outputs": [], + "source": [ + "# Extract image data\n", + "\n", + "def get_prediction_data(feature, start, end):\n", + " '''Extracts Sentinel image as json at specific lat/lon and timestamp.'''\n", + "\n", + " image = ee.ImageCollection('COPERNICUS/S2').filterDate(start, end).select(BANDS).mosaic()\n", + "\n", + " fc = ee.FeatureCollection([\n", + " image.neighborhoodToArray(ee.Kernel.square(16)).sampleRegions(\n", + " collection=ee.FeatureCollection([feature]), scale=10).first()\n", + " ])\n", + " \n", + " # download FeatureCollection as JSON\n", + " url = fc.getDownloadURL('geojson')\n", + " bytes = requests.get(url).content\n", + " values=bytes.decode(\"utf-8\")\n", + " json_values = json.loads(values)\n", + " return json_values['features'][0]['properties']" + ] + }, + { + "cell_type": "code", + "execution_count": 562, + "id": "86d53ea1-b09e-4afb-8d04-60b73ec59c7c", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_data = get_prediction_data(plant_location, '2021-10-01', '2021-10-31')" + ] + }, + { + "cell_type": "markdown", + "id": "9d93aaa8", + "metadata": {}, + "source": [ + "The prediction service expects the data was want a prediction for as well as the bucket where our saved model assets are stored." + ] + }, + { + "cell_type": "code", + "execution_count": 563, + "id": "5c01631e-5607-45c8-a42e-354fb38a87e1", + "metadata": {}, + "outputs": [], + "source": [ + "requests.post(\n", + " url=f'{service_url}/predict',\n", + " headers={'Authorization': f'Bearer {identity_token}'},\n", + " json={'data': prediction_data, 'bucket': bucket},\n", + ").json()" + ] + }, + { + "cell_type": "markdown", + "id": "2222aaf9", + "metadata": {}, + "source": [ + "# Visualize Results" + ] + }, + { + "cell_type": "markdown", + "id": "e70fd0a5-a1ae-4cac-91cc-342809d9fb3b", + "metadata": {}, + "source": [ + "Let's visualize the results of a power plant in Spain. First, we get predictions for the four towers at this power plant." + ] + }, + { + "cell_type": "code", + "execution_count": 533, + "id": "ac680e7f-3598-422a-af5e-bb666425dbd0", + "metadata": {}, + "outputs": [], + "source": [ + "locations = [ee.Feature(ee.Geometry.Point([-7.86444, 43.43717])),\n", + " ee.Feature(ee.Geometry.Point([-7.86376, 43.43827])),\n", + " ee.Feature(ee.Geometry.Point([-7.85755, 43.44075])),\n", + " ee.Feature(ee.Geometry.Point([-7.85587, 43.44114])),]" + ] + }, + { + "cell_type": "code", + "execution_count": 534, + "id": "9fc56972-47bb-4b37-b447-400cbea5e90a", + "metadata": {}, + "outputs": [], + "source": [ + "plant_predictions = []\n", + "for location in locations:\n", + " prediction_data = get_prediction_data(location, '2021-10-01', '2021-10-31')\n", + " result = requests.post(\n", + " url=f'{service_url}/predict',\n", + " headers={'Authorization': f'Bearer {identity_token}'},\n", + " json={'data': prediction_data, 'bucket': bucket},).json()\n", + " preds.append(result['predictions']['predictions'][0][0][0][0])" + ] + }, + { + "cell_type": "markdown", + "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd", + "metadata": {}, + "source": [ + "Next, we can plot these points on a map. Red means our model predicts that the towers are on, and blue means that it predicts the towers are off." + ] + }, + { + "cell_type": "code", + "execution_count": 522, + "id": "98651a74", + "metadata": {}, + "outputs": [], + "source": [ + "import folium\n", + "import folium.plugins as folium_plugins\n", + "import branca.colormap as cm" + ] + }, + { + "cell_type": "code", + "execution_count": 523, + "id": "4a0b6bc2-e187-4d0c-90de-636f47db6b20", + "metadata": {}, + "outputs": [], + "source": [ + "lon = [-7.86444, -7.86376, -7.85755, -7.85587]\n", + "lat = [43.43717, 43.43827, 43.44075, 43.44114]" + ] + }, + { + "cell_type": "code", + "execution_count": 539, + "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colormap = cm.LinearColormap(colors=['red','lightblue'], index=[90,100], vmin=90, vmax=100)\n", + "map = folium.Map([43.45, -7.87], zoom_start=14, tiles='CartoDB positron')\n", + "for loc, p in zip(zip(lat, lon), preds):\n", + " folium.Circle(\n", + " location=loc,\n", + " radius=20,\n", + " fill=True,\n", + " color=colormap(p),\n", + " ).add_to(map)\n", + "\n", + "map.add_child(colormap)\n", + "\n", + "display(map)" + ] + }, + { + "cell_type": "markdown", + "id": "efcabd53-e81e-4d72-a453-e2fb6dcaa7ae", + "metadata": {}, + "source": [ + "# Clean Up" + ] + }, + { + "cell_type": "markdown", + "id": "b002e48a-035c-4369-a161-aa2d800a70b2", + "metadata": {}, + "source": [ + "To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources." + ] + }, + { + "cell_type": "markdown", + "id": "9fc97850-80dd-4c24-80d0-f93825d96e0e", + "metadata": {}, + "source": [ + "## Deleting the project\n", + "\n", + "The easiest way to eliminate billing is to delete the project that you created for the tutorial.\n", + "\n", + "To delete the project:\n", + "\n", + "> ⚠️ Deleting a project has the following effects:\n", + ">\n", + "> * **Everything in the project is deleted.** If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.\n", + ">\n", + "> * **Custom project IDs are lost.** When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.\n", + ">\n", + "> If you plan to explore multiple tutorials and quickstarts, reusing projects can help you avoid exceeding project quota limits.\n", + "\n", + "1. In the Cloud Console, go to the **Manage resources** page.\n", + "\n", + " \n", + "\n", + "1. In the project list, select the project that you want to delete, and then click **Delete**.\n", + "\n", + "1. In the dialog, type the project ID, and then click **Shut down** to delete the project." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7c1c51a-ef84-4645-91ae-b997e1643170", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "environment": { + "kernel": "python3", + "name": "tf2-gpu.2-6.m82", + "type": "gcloud", + "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m82" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py new file mode 100644 index 000000000000..2ab0e53351f7 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -0,0 +1,376 @@ +# Copyright 2021 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import platform +import subprocess +import time +import uuid +import json +import pandas as pd +import numpy as np + +from google.cloud import aiplatform +from google.cloud import storage +import pytest +import requests + +import ee +import google.auth + +credentials, project = google.auth.default() +ee.Initialize(credentials, project=project) + +PYTHON_VERSION = "".join(platform.python_version_tuple()[0:2]) + +NAME = f"ppai/geospatial-classification-py{PYTHON_VERSION}" + +UUID = uuid.uuid4().hex[0:6] +PROJECT = os.environ["GOOGLE_CLOUD_PROJECT"] +REGION = "us-central1" + +TIMEOUT_SEC = 30 * 60 # 30 minutes in seconds +POLL_INTERVAL_SEC = 60 # 1 minute in seconds + +VERTEX_AI_SUCCESS_STATE = "PIPELINE_STATE_SUCCEEDED" +VERTEX_AI_FINISHED_STATE = { + "PIPELINE_STATE_SUCCEEDED", + "PIPELINE_STATE_FAILED", + "PIPELINE_STATE_CANCELLED", +} + +EARTH_ENGINE_SUCCESS_STATE = "SUCCEEDED" +EARTH_ENGINE_FINISHED_STATE = {"SUCCEEDED"} + +BANDS = [ + "B1", + "B2", + "B3", + "B4", + "B5", + "B6", + "B7", + "B8", + "B8A", + "B9", + "B10", + "B11", + "B12", +] +LABEL = "label" + +logging.getLogger().setLevel(logging.INFO) + + +@pytest.fixture(scope="session") +def bucket_name(): + storage_client = storage.Client() + + bucket_name = f"{NAME.replace('/', '-')}-{UUID}" + bucket = storage_client.create_bucket(bucket_name, location=REGION) + + logging.info(f"bucket_name: {bucket_name}") + yield bucket_name + + bucket.delete(force=True) + + +@pytest.fixture(scope="session") +def test_data(bucket_name): + label_fc = create_label_fc("labeled_geospatial_data.csv") + label_fc = label_fc.map(format_date) + data = label_fc.map(get_neighboring_patch).flatten() + data = data.randomColumn() + data = data.filter(ee.Filter.gte("random", 0.03)) + + training_task = ee.batch.Export.table.toCloudStorage( + collection=data, + description="Training image export: test", + bucket=bucket_name, + fileNamePrefix="geospatial_training", + selectors=BANDS + [LABEL], + fileFormat="TFRecord", + ) + + training_task.start() + + validation_task = ee.batch.Export.table.toCloudStorage( + collection=data, + description="Validation image export: test", + bucket=bucket_name, + fileNamePrefix="geospatial_validation", + selectors=BANDS + [LABEL], + fileFormat="TFRecord", + ) + + validation_task.start() + + train_status = None + val_status = None + + logging.info("Waiting for data export to complete.") + for _ in range(0, TIMEOUT_SEC, POLL_INTERVAL_SEC): + train_status = ee.data.getOperation(training_task.name)["metadata"]["state"] + val_status = ee.data.getOperation(validation_task.name)["metadata"]["state"] + if train_status and val_status in EARTH_ENGINE_FINISHED_STATE: + break + time.sleep(POLL_INTERVAL_SEC) + + assert train_status == EARTH_ENGINE_SUCCESS_STATE + assert val_status == EARTH_ENGINE_SUCCESS_STATE + logging.info(f"Export finished with status {train_status}") + + yield training_task.name + + +def create_label_fc(path): + """Creates a FeatureCollection from the label dataframe.""" + + dataframe = pd.read_csv(path) + num_examples = dataframe.shape[0] + data_dict = dataframe.to_dict() + feats = [] + properties = ["timestamp", "label"] + for idx in np.arange(num_examples): + feat_dict = {} + geometry = ee.Geometry.Point([data_dict["lon"][idx], data_dict["lat"][idx]]) + for feature in properties: + feat_dict[feature] = data_dict[feature][idx] + feat = ee.Feature(geometry, feat_dict) + feats.append(feat) + return ee.FeatureCollection(feats) + + +def format_date(feature): + """Creates start date and end date properties.""" + + # Extract start and end dates + timestamp = ee.String(feature.get("timestamp")).split(" ").get(0) + year = ee.Number.parse(ee.String(timestamp).split("-").get(0)) + month = ee.Number.parse(ee.String(timestamp).split("-").get(1)) + day = ee.Number.parse(ee.String(timestamp).split("-").get(2)) + start = ee.Date.fromYMD(year, month, day) + end = start.advance(1, "day") + + # Create new feature + feature = feature.set({"start": start, "end": end}) + + return feature + + +def get_neighboring_patch(feature): + """Gets image pixel values for patch.""" + + # filter ImageCollection at start/end dates. + image = ( + ee.ImageCollection("COPERNICUS/S2") + .filterDate(feature.get("start"), feature.get("end")) + .select(BANDS) + .median() + ) + + # extract pixel values at the lat/lon with a 16x16 padding + return ee.FeatureCollection( + [ + image.neighborhoodToArray(ee.Kernel.square(16)) + .sampleRegions(collection=ee.FeatureCollection([feature]), scale=10) + .first() + ] + ) + + +@pytest.fixture(scope="session") +def container_image(): + # https://cloud.google.com/sdk/gcloud/reference/builds/submit + container_image = f"gcr.io/{PROJECT}/{NAME}:{UUID}" + subprocess.check_call( + [ + "gcloud", + "builds", + "submit", + "serving_app", + f"--tag={container_image}", + f"--project={PROJECT}", + "--machine-type=e2-highcpu-8", + "--timeout=15m", + "--quiet", + ] + ) + + logging.info(f"container_image: {container_image}") + yield container_image + + # https://cloud.google.com/sdk/gcloud/reference/container/images/delete + subprocess.check_call( + [ + "gcloud", + "container", + "images", + "delete", + container_image, + f"--project={PROJECT}", + "--force-delete-tags", + "--quiet", + ] + ) + + +@pytest.fixture(scope="session") +def service_url(bucket_name, container_image): + # https://cloud.google.com/sdk/gcloud/reference/run/deploy + service_name = f"{NAME.replace('/', '-')}-{UUID}" + subprocess.check_call( + [ + "gcloud", + "run", + "deploy", + service_name, + f"--image={container_image}", + "--command=gunicorn", + "--args=--threads=8,--timeout=0,main:app", + "--platform=managed", + f"--project={PROJECT}", + f"--region={REGION}", + "--memory=1G", + f"--set-env-vars=PROJECT={PROJECT}", + f"--set-env-vars=STORAGE_PATH=gs://{bucket_name}", + f"--set-env-vars=REGION={REGION}", + f"--set-env-vars=CONTAINER_IMAGE={container_image}", + "--no-allow-unauthenticated", + ] + ) + + # https://cloud.google.com/sdk/gcloud/reference/run/services/describe + service_url = ( + subprocess.run( + [ + "gcloud", + "run", + "services", + "describe", + service_name, + "--platform=managed", + f"--project={PROJECT}", + f"--region={REGION}", + "--format=get(status.url)", + ], + capture_output=True, + ) + .stdout.decode("utf-8") + .strip() + ) + + logging.info(f"service_url: {service_url}") + yield service_url + + # https://cloud.google.com/sdk/gcloud/reference/run/services/delete + subprocess.check_call( + [ + "gcloud", + "run", + "services", + "delete", + service_name, + "--platform=managed", + f"--project={PROJECT}", + f"--region={REGION}", + "--quiet", + ] + ) + + +@pytest.fixture(scope="session") +def identity_token(): + yield ( + subprocess.run( + ["gcloud", "auth", "print-identity-token", f"--project={PROJECT}"], + capture_output=True, + ) + .stdout.decode("utf-8") + .strip() + ) + + +@pytest.fixture(scope="session") +def train_model(bucket_name): + aiplatform.init(project=PROJECT, staging_bucket=bucket_name) + job = aiplatform.CustomTrainingJob( + display_name="climate_script_colab", + script_path="task.py", + container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-5:latest", + ) + + model = job.run(args=[f"--bucket={bucket_name}"]) + resource_name = job.resource_name + logging.info(f"train_model resource_name: {resource_name}") + + # Wait until the model training job finishes. + status = None + logging.info("Waiting for model to train.") + for _ in range(0, TIMEOUT_SEC, POLL_INTERVAL_SEC): + # https://googleapis.dev/python/aiplatform/latest/aiplatform_v1/job_service.html + status = job.state.name + if status in VERTEX_AI_FINISHED_STATE: + break + time.sleep(POLL_INTERVAL_SEC) + + logging.info(f"Model job finished with status {status}") + assert status == VERTEX_AI_SUCCESS_STATE + yield resource_name + + +def get_prediction_data(feature, start, end): + """Extracts Sentinel image as json at specific lat/lon and timestamp.""" + + image = ( + ee.ImageCollection("COPERNICUS/S2") + .filterDate(start, end) + .select(BANDS) + .mosaic() + ) + + fc = ee.FeatureCollection( + [ + image.neighborhoodToArray(ee.Kernel.square(16)) + .sampleRegions(collection=ee.FeatureCollection([feature]), scale=10) + .first() + ] + ) + + # download FeatureCollection as JSON + url = fc.getDownloadURL("geojson") + bytes = requests.get(url).content + values = bytes.decode("utf-8") + json_values = json.loads(values) + return json_values["features"][0]["properties"] + + +def test_predict(bucket_name, test_data, train_model, service_url, identity_token): + + # Test point + plant_location = ee.Feature(ee.Geometry.Point([-84.80529, 39.11613])) + prediction_data = get_prediction_data(plant_location, "2021-10-01", "2021-10-31") + logging.info(f"plant location: {plant_location})") + + # Make prediction + response = requests.post( + url=f"{service_url}/predict", + headers={"Authorization": f"Bearer {identity_token}"}, + json={"data": prediction_data, "bucket": bucket_name}, + ).json() + + # Check that we get non-empty predictions. + assert "predictions" in response["predictions"] + assert len(response["predictions"]) > 0 diff --git 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a/people-and-planet-ai/geospatial-classification/requirements-test.txt b/people-and-planet-ai/geospatial-classification/requirements-test.txt new file mode 100644 index 000000000000..deaa7f5dbf7a --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/requirements-test.txt @@ -0,0 +1,4 @@ +earthengine-api==0.1.292 +pytest==6.2.4 +google-cloud-aiplatform==1.8.0 +pandas==1.3.5 diff --git a/people-and-planet-ai/geospatial-classification/serving_app/Dockerfile b/people-and-planet-ai/geospatial-classification/serving_app/Dockerfile new file mode 100644 index 000000000000..0fac63570511 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/serving_app/Dockerfile @@ -0,0 +1,9 @@ +FROM python:3.9-slim + +WORKDIR /app + +COPY *.py ./ +COPY requirements.txt ./ + +RUN pip install --no-cache-dir -r requirements.txt +RUN pip install tensorflow diff --git a/people-and-planet-ai/geospatial-classification/serving_app/main.py b/people-and-planet-ai/geospatial-classification/serving_app/main.py new file mode 100644 index 000000000000..8c9211a4ff4b --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/serving_app/main.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Copyright 2021 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import flask + +app = flask.Flask(__name__) + + +@app.route("/ping", methods=["POST"]) +def run_root(): + args = flask.request.get_json() or {} + return { + "response": "Your request was successful! 🎉", + "args": args["message"], + } + + +@app.route("/predict", methods=["POST"]) +def run_predict(): + import predict + + try: + args = flask.request.get_json() or {} + bucket = args["bucket"] + model_dir = f"gs://{bucket}/model_output" + data = args["data"] + predictions = predict.run(data, model_dir) + + return { + "method": "predict", + "model_dir": model_dir, + "predictions": predictions, + } + except Exception as e: + return {"error": f"{type(e).__name__}: {e}"} + + +if __name__ == "__main__": + import os + + app.run(debug=True, host="0.0.0.0", port=int(os.environ.get("PORT", 8080))) diff --git a/people-and-planet-ai/geospatial-classification/serving_app/predict.py b/people-and-planet-ai/geospatial-classification/serving_app/predict.py new file mode 100644 index 000000000000..2dba4adddc8e --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/serving_app/predict.py @@ -0,0 +1,28 @@ +# Copyright 2021 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +import tensorflow as tf +import os + + +def run(data, model_dir): + + model = tf.keras.models.load_model(model_dir) + prediction_values = np.array(list(data.values())) + transposed = np.transpose(prediction_values, (1, 2, 0)) + predictions = model.predict(np.expand_dims(transposed, axis=0)).tolist() + + return {"predictions": predictions} diff --git a/people-and-planet-ai/geospatial-classification/serving_app/requirements.txt b/people-and-planet-ai/geospatial-classification/serving_app/requirements.txt new file mode 100644 index 000000000000..df6b8ae0f008 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/serving_app/requirements.txt @@ -0,0 +1,3 @@ +Flask==2.0.1 +gunicorn==20.1.0 +numpy==1.21.4 diff --git a/people-and-planet-ai/geospatial-classification/trainer/task.py b/people-and-planet-ai/geospatial-classification/trainer/task.py new file mode 100644 index 000000000000..b99dc342d3f8 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/trainer/task.py @@ -0,0 +1,132 @@ +# Copyright 2021 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf +import argparse + +BANDS = [ + "B1", + "B2", + "B3", + "B4", + "B5", + "B6", + "B7", + "B8", + "B8A", + "B9", + "B10", + "B11", + "B12", +] +LABEL = "label" + + +def get_args(): + """Parses args.""" + + parser = argparse.ArgumentParser() + parser.add_argument("--bucket", required=True, type=str, help="GCS Bucket") + args = parser.parse_args() + return args + + +def parse_tfrecord(example_proto, features_dict): + """Parses a single tf.train.Example.""" + + return tf.io.parse_single_example(example_proto, features_dict) + + +def create_features_dict(): + """Creates dict of features.""" + + features_dict = { + name: tf.io.FixedLenFeature(shape=[33, 33], dtype=tf.float32) for name in BANDS + } + + features_dict[LABEL] = tf.io.FixedLenFeature(shape=[1, 1], dtype=tf.float32) + + return features_dict + + +def get_feature_and_label_vectors(inputs, features_dict): + """Formats data.""" + + label_value = tf.cast(inputs.pop(LABEL), tf.int32) + features_vec = [inputs[name] for name in BANDS] + # (bands, x, y) -> (x, y, bands) + features_vec = tf.transpose(features_vec, [1, 2, 0]) + return features_vec, label_value + + +def create_datasets(bucket): + """Creates training and validation datasets.""" + + train_data_dir = f"gs://{bucket}/geospatial_training.tfrecord.gz" + eval_data_dir = f"gs://{bucket}/geospatial_validation.tfrecord.gz" + features_dict = create_features_dict() + + training_dataset = tf.data.TFRecordDataset(train_data_dir, compression_type="GZIP") + training_dataset = training_dataset.map( + lambda example_proto: parse_tfrecord(example_proto, features_dict) + ) + training_dataset = training_dataset.map( + lambda inputs: get_feature_and_label_vectors(inputs, features_dict) + ) + training_dataset = training_dataset.batch(64) + + validation_dataset = tf.data.TFRecordDataset(eval_data_dir, compression_type="GZIP") + validation_dataset = validation_dataset.map( + lambda example_proto: parse_tfrecord(example_proto, features_dict) + ) + validation_dataset = validation_dataset.map( + lambda inputs: get_feature_and_label_vectors(inputs, features_dict) + ) + validation_dataset = validation_dataset.batch(64) + + return training_dataset, validation_dataset + + +def create_model(training_dataset): + """Creates model.""" + + feature_ds = training_dataset.map(lambda x, y: x) + normalizer = tf.keras.layers.experimental.preprocessing.Normalization() + normalizer.adapt(feature_ds) + + inputs = tf.keras.Input(shape=(None, None, 13)) + x = normalizer(inputs) + x = tf.keras.layers.Conv2D(filters=32, kernel_size=33, activation="relu")(x) + outputs = tf.keras.layers.Dense(1, activation="sigmoid")(x) + + model = tf.keras.Model(inputs=inputs, outputs=outputs) + + model.compile( + optimizer=tf.keras.optimizers.Adam(0.0001), + loss="binary_crossentropy", + metrics=["accuracy"], + ) + return model + + +def main(): + args = get_args() + training_dataset, validation_dataset = create_datasets(args.bucket) + model = create_model(training_dataset) + model.fit(training_dataset, validation_data=validation_dataset, epochs=20) + model.save(f"gs://{args.bucket}/model_output") + + +if __name__ == "__main__": + main() From 8ab2d196a662446e9ff6102c93cfe54a646f57fd Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Tue, 21 Dec 2021 11:01:33 -0600 Subject: [PATCH 02/30] Added noxfile --- .../noxfile_config.py | 47 +++++++++++++++++++ .../serving_app/main.py | 1 - .../serving_app/predict.py | 3 +- 3 files changed, 49 insertions(+), 2 deletions(-) create mode 100644 people-and-planet-ai/geospatial-classification/noxfile_config.py diff --git a/people-and-planet-ai/geospatial-classification/noxfile_config.py b/people-and-planet-ai/geospatial-classification/noxfile_config.py new file mode 100644 index 000000000000..5cf936758585 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/noxfile_config.py @@ -0,0 +1,47 @@ +# Copyright 2021 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Default TEST_CONFIG_OVERRIDE for python repos. + +# You can copy this file into your directory, then it will be imported from +# the noxfile.py. + +# The source of truth: +# https://github.com/GoogleCloudPlatform/python-docs-samples/blob/main/noxfile_config.py + +TEST_CONFIG_OVERRIDE = { + # You can opt out from the test for specific Python versions. + # > ℹ️ We're opting out of all Python versions except 3.8. + # > The Python version used is defined by the Dockerfile, so it's redundant + # > to run multiple tests since they would all be running the same Dockerfile. + "ignored_versions": ["2.7", "3.6", "3.7", "3.9", "3.10"], + # Old samples are opted out of enforcing Python type hints + # All new samples should feature them + # "enforce_type_hints": True, + # An envvar key for determining the project id to use. Change it + # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a + # build specific Cloud project. You can also use your own string + # to use your own Cloud project. + "gcloud_project_env": "GOOGLE_CLOUD_PROJECT", + # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT', + # If you need to use a specific version of pip, + # change pip_version_override to the string representation + # of the version number, for example, "20.2.4" + "pip_version_override": None, + # A dictionary you want to inject into your test. Don't put any + # secrets here. These values will override predefined values. + "envs": { + "PYTEST_ADDOPTS": "-n=8", # parallelize tests in multiple CPUs + }, +} diff --git a/people-and-planet-ai/geospatial-classification/serving_app/main.py b/people-and-planet-ai/geospatial-classification/serving_app/main.py index 8c9211a4ff4b..04df0f7c0dd9 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/main.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/main.py @@ -15,7 +15,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os import flask app = flask.Flask(__name__) diff --git a/people-and-planet-ai/geospatial-classification/serving_app/predict.py b/people-and-planet-ai/geospatial-classification/serving_app/predict.py index 2dba4adddc8e..24a9e5cefd87 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/predict.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/predict.py @@ -13,9 +13,10 @@ # limitations under the License. +import os + import numpy as np import tensorflow as tf -import os def run(data, model_dir): From 22794350d4f3439415c3c3a435c3b82d551ba8f5 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Fri, 24 Dec 2021 15:13:06 -0600 Subject: [PATCH 03/30] added emojis to notebook --- .../geospatial-classification/README.ipynb | 174 +++++++++++------- .../serving_app/predict.py | 2 - 2 files changed, 103 insertions(+), 73 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index 106ae1253b42..40c07e2877dd 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -32,12 +32,19 @@ "id": "3683a5f8", "metadata": {}, "source": [ - "# Power Plant Predictions\n", + "# 🏭 Power Plant ON/OFF: Predictions\n", "\n", "* **Time estimate**: 1 hour\n", - "* **Cost estimate**: less than $5.00 USD\n", + "* **Cost estimate**: less than **5**.00 USD\n", "\n", - "This is an interactive notebook that contains all of the code necessary to train an ML model for geospatial classification." + "This is an **interactive** notebook that contains **all** of the **code** necessary to train an ML model from satellite images for a geospatial classification of whether a power plant is on/off. \n", + "\n", + "This is a first step **introductory example** of how this **satellite images** can be used to detect **carbon pollution** from power plants.\n", + "\n", + "--------------------------------------------\n", + "\n", + "\n", + "💚 This is one of many **machine learning how-to samples** inspired from **real climate solutions** aired on the [People and Planet AI 🎥 series](https://www.youtube.com/playlist?list=PLIivdWyY5sqI-llB35Dcb187ZG155Rs_7)." ] }, { @@ -45,7 +52,7 @@ "id": "922968ad", "metadata": {}, "source": [ - "# Overview\n", + "# 🚏 Overview\n", "\n", "This notebook leverages geospatial data from [Google Earth Engine](https://earthengine.google.com/) and labeled data provided by the organization [Climate TRACE](https://www.climatetrace.org/). By combining these two data sources, you'll build and train a model that predicts whether or not a power plant is turned on and producing emissions." ] @@ -55,11 +62,11 @@ "id": "8f15adff", "metadata": {}, "source": [ - "### Data _(inputs)_\n", + "### 🛰️ Data _(inputs)_\n", "\n", - "The data in this example consists of satellite images from [Sentinel-2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description), a wide-swath, high-resolution, multi-spectral imaging mission.\n", + "The data in this example consists of images from a satellite called [Sentinel-2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description), for a wide-swath, **high-resolution**, multi-spectral imaging for land monitoring studies.\n", "\n", - "When working with satellite data, each input image has the dimensions `[width, height, bands]`. Bands are measurements from specific satellite instruments for different ranges of the electromagnetic spectrum. For example, Sentinel-2 contains [13 spectral bands](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#bands). If you are familiar with image classification problems, you can think of the bands as similar to an image's RGB channels. However, when working with satellite data we generally have more than just 3 channels.\n", + "When working with satellite data, each input image has the **dimensions** `[width, height, bands]`. **Bands** are measurements from specific satellite instruments for different ranges of the **electromagnetic spectrum**. For example, Sentinel-2 contains [🌈 13 spectral bands](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#bands). If you are familiar with image classification problems, you can think of the bands as similar to an image's RGB (red, green, blue) channels. However, when working with satellite data we generally have **more than just 3** channels.\n", "\n", "![satellite_inputs](img/inputs.png)\n", "\n" @@ -70,11 +77,11 @@ "id": "d15be296", "metadata": {}, "source": [ - "### Labels _(outputs)_\n", + "### 🏷️ Labels _(outputs)_\n", "\n", - "For each image that we give to the model, the output will be a binary indicator for each pixel in the image indicating whether or not that pixel represents a power plant that is turned on.\n", + "For each patch of pixels (an image of a power plant) that we give to the model, it performs **binary classification**, which indicates whether the power plant is on or off.\n", "\n", - "In this example, the output dimensions will be `[width, height, classes]`, where classes is 1 because this is a binary classification problem (on or off).\n", + "In this example, the **output** is a single number between *0 (Off) and 1 (On)*, representing the **probability** of that power plant being ON.\n", "\n", "![satellite_outputs](img/outputs.png)\n" ] @@ -86,9 +93,8 @@ "source": [ "### Model _(function)_\n", "\n", - "One problem you often run into when doing geospatial classification is sparse labels, meaning that we only have labels for a selection of points within a region. \n", - "\n", - "![sparse_labels](img/sparse_labels.png)\n", + "**TL;DR**\n", + "*The model will receive a patch of pixels, in the center is the power plant tower. We then take 16 pixels as padding creating a 33x33 patch. This returns a classification of ON/OFF*\n", "\n", "In this example, we have a CSV file of labels. Each row in this file represents a power plant at a specific lat/lon and timestamp. To deal with the sparsity, at training time we'll prepare a dataset where each input image is a single pixel that we have a label for. We will then add a padding around that image. These padded pixels will not get predictions, but will help our model to make better predictions for the center point that we have a label for.\n", "\n", @@ -107,21 +113,21 @@ "id": "3547aec6", "metadata": {}, "source": [ - "## Steps summary\n", + "## 🚴‍♀️ Steps summary\n", "\n", "Here's a quick summary of what you’ll go through:\n", "\n", "1. **Get the training data**:\n", - " Extract satellite data from [Earth Engine](https://earthengine.google.com/), combine it with the labels from Climate TRACE, and export to\n", + " Extract satellite images from [Earth Engine](https://earthengine.google.com/), combine it with the data that was labeled and contains lat/long coordinates from [Climate TRACE](https://climatetrace.org) in a CSV, and export to\n", " [Cloud Storage](https://cloud.google.com/storage).\n", "\n", - "1. **Run custom training job**:\n", - " Run a custom training job on [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) using a [pre-built training container](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers).\n", + "1. **Run a custom training job**:\n", + " Using [Tensorflow Keras](https://keras.io/) on [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) using a [pre-built training container](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers).\n", "\n", - "1. **Deploy to Cloud Run**:\n", - " Deploy a web service in\n", + "1. **Deploy a web service to host the trained model**:\n", + " On\n", " [Cloud Run](https://cloud.google.com/run)\n", - " to get predictions using the trained model.\n", + " and get predictions using the model.\n", "\n", "1. **Get Predictions**:\n", " Use the web service to get predictions for new data.\n", @@ -132,12 +138,48 @@ "1. (Optional) **Delete the project** to avoid ongoing costs." ] }, + { + "cell_type": "markdown", + "id": "46b21577", + "metadata": {}, + "source": [ + "## 🙈 Using this interactive notebook\n", + "\n", + "Click the **run** icons ▶️ of each section within this notebook. \n", + "\n", + "This notebook code lets you train and deploy an ML model, as well as test its accuracy, from end-to-end. When you run a code cell, the code runs in the notebook's runtime, so you're not making any changes to your personal computer.\n", + "\n", + "> 🛎️ **To avoid any errors**, wait for each section to finish in their order before clicking the next “run” icon.\n", + "\n", + "This sample must be connected to a **Google Cloud project**, but nothing else is needed other than your Google Cloud project.\n", + "You can use an existing project and the cost will be less than **$5.00**. Alternatively, you can create a new Cloud project with cloud credits for free" + ] + }, { "cell_type": "markdown", "id": "f0033a40", "metadata": {}, "source": [ - "# Before you begin" + "## ✨ Before you begin, you need to…\n", + "\n", + "1. Decide on creating a new\n", + " [free project](https://cloud.google.com/free/docs/gcp-free-tier)\n", + " _(recommended)_ or using an existing one.\n", + " Then **copy the project ID** and paste it in the `google_cloud_project` field in the \"Entering project details” section below.\n", + "\n", + " > 💡 If you _don't plan to keep the resources_ that you create via this sample, we recommend creating a new project instead of selecting an existing project.\n", + " > After you finish these steps, you can delete the project, removing all the resources associated in bulk.\n", + "\n", + "1. [_Click here_](https://console.cloud.google.com/flows/enableapi?apiid=dataflow,aiplatform.googleapis.com)\n", + " to **enable the following APIs** in your Google Cloud project:\n", + " _Dataflow_ and _AI Platform_.\n", + "\n", + "1. Make sure that **billing is enabled** for your Google Cloud project,\n", + " [_click here_](https://cloud.google.com/billing/docs/how-to/modify-project)\n", + " to learn how to confirm that billing is enabled.\n", + "\n", + "1. Have an **Earth Engine** account (it's FREE) or create a new one.\n", + " To create an account, fill out the [registration form here.](https://signup.earthengine.google.com/#!/). Please note this can take from 0-24 hours...but it's worth it! Come back to this sample after you have this." ] }, { @@ -145,7 +187,7 @@ "id": "980b439f-03bb-427f-ae61-4d6d5f066d15", "metadata": {}, "source": [ - "### Make sure you provide a regional bucket!" + "### ✏️ Enter your Cloud project's details. Ensure you provide a regional bucket!" ] }, { @@ -182,9 +224,12 @@ "id": "156ab72c", "metadata": {}, "source": [ - "## Getting the code\n", + "### ⛏️ Preparing the project environment\n", "\n", - "Run ▶️ the following cell to download the code sample's source code." + "Click the run ▶️ icons in order for the cells to download and install the necessary code, libraries, and resources for this solution.\n", + "\n", + "> 💡 You can _optionally_ view the entire\n", + "> [code in GitHub](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai/geospatial-classification)." ] }, { @@ -204,7 +249,7 @@ "id": "8a363277-2195-42b4-83e7-3e3921bb3db1", "metadata": {}, "source": [ - "### Install the Vertex AI SDK" + "### ↘️ Install the Vertex AI SDK" ] }, { @@ -242,9 +287,9 @@ "id": "4ec73dd9", "metadata": {}, "source": [ - "### Authenticate to Earth Engine\n", + "### 🗺️ Authenticate to Earth Engine\n", "\n", - "In order to use the Earth Engine API, you'll need to have an Earth Engine account.\n", + "In order to use the Earth Engine API, you'll **need** to have an **Earth Engine account**.\n", "\n", "To create an account, fill out the [registration form here.](https://signup.earthengine.google.com/#!/)" ] @@ -266,15 +311,15 @@ "id": "980b0418", "metadata": {}, "source": [ - "# Get the training data\n", + "# 1. 🛰️ Get the training data\n", "\n", - "The training data in this sample comes from two places: \n", + "The training data in this sample comes from **two places**: \n", "\n", "1. The satellite images will be extracted from *Earth Engine*.\n", "\n", - "2. The labels are provided in a *csv file* that indicates whether a power plant is turned on or off at a particular timestamp. \n", + "2. The **labels** are provided in a *CSV file* that indicates whether a *power plant* is turned *on or off* at a **particular timestamp**. \n", "\n", - "For each row in the CSV file, we need to extract the corresponding Sentinel image taken at that specific latitude/longitude and timestamp. We'll export this image data, along with the corresponding label (on/off), to Cloud Storage.\n" + "For each row in the CSV file, we need to extract the corresponding Sentinel image taken at that specific latitude/longitude and timestamp. We'll **export** this image data, along with the corresponding label (on/off), to Cloud Storage." ] }, { @@ -309,9 +354,9 @@ "id": "beba81d1", "metadata": {}, "source": [ - "### Import labels\n", + "### 🏷️ Import labels\n", "\n", - "First, we import the csv file that contains the labels." + "First, we **import** the **CSV** file that contains the labels." ] }, { @@ -595,7 +640,7 @@ "id": "96967303", "metadata": {}, "source": [ - "### Merge labels + Sentinel image data" + "### Merge 🏷️ labels + 🛰️ Sentinel image data" ] }, { @@ -743,7 +788,7 @@ "id": "2ab1318d", "metadata": {}, "source": [ - "### Create train/validation splits" + "### 🎛️ Create train/validation splits" ] }, { @@ -782,7 +827,7 @@ "id": "c02607c8", "metadata": {}, "source": [ - "### Export data" + "### 💾 Export data" ] }, { @@ -848,9 +893,9 @@ "id": "02b1b9dd", "metadata": {}, "source": [ - "# Run custom training job\n", + "# 2. 👟 Run a custom training job\n", "\n", - "Once the export jobs have finished, we're ready to use that data to train a model on Vertex AI Training.\n", + "Once the export jobs have finished, we're **ready to use** that data to train a model on Vertex AI Training.\n", "\n", "The complete training code can be found in the `task.py` file.\n", "\n", @@ -893,7 +938,7 @@ "id": "c75499ed", "metadata": {}, "source": [ - "# Deploy to Cloud Run" + "# 3. 💻Deploy a web service to host the trained model" ] }, { @@ -922,7 +967,7 @@ "id": "a0e28bff-0804-4093-acf2-739700b24c5f", "metadata": {}, "source": [ - "## Build container\n", + "## 🏗️ Build container\n", "\n", "We use Cloud Build to build the container image. After the image is built, it is available in Container Registry." ] @@ -957,7 +1002,7 @@ "id": "9978056f-4467-4dda-85a5-84655ade9a11", "metadata": {}, "source": [ - "## Deploy app" + "## 🐣 Deploy app" ] }, { @@ -1028,27 +1073,27 @@ "source": [ "Finally, we can test that everything is working.\n", "\n", - "We included a `ping` method in our web service just to make sure everything is working as expected.\n", + "We included a `ping` method in our web service just to *make sure everything is working* as expected.\n", "It simply returns back the arguments we passed to the call, as well as a response saying that the call was successful.\n", "\n", - "> ℹ️ This is a convenient way to make sure the web service is reachable, the authentication is working as expected, and the request arguments are passed correctly.\n", + "> 🛎️ This is a convenient way to make sure the web service is reachable, the authentication is working as expected, and the request arguments are passed correctly.\n", "\n", "We can use Python's\n", "[`requests`](https://docs.python-requests.org)\n", "library.\n", "The web service was built to always accept [JSON](https://www.w3schools.com/whatis/whatis_json.asp)-encoded requests, and returns JSON-encoded responses.\n", "\n", - "For a request to be successful, it must:\n", + "For a **request to be successful**, it **must**:\n", "\n", "* Be an [`HTTP POST`](https://developer.mozilla.org/en-US/docs/Web/HTTP/Methods/POST) request\n", - "* Contain the following headers:\n", + "* Contain the following **headers**:\n", " * `Authorization: Bearer IDENTITY_TOKEN`\n", " * `Content-Type: application/json`\n", - "* The data must be valid JSON, if no arguments are needed we can pass `{}` as an empty object.\n", + "* The data must be **valid JSON**, if *no arguments* are needed we can pass `{}` as an **empty object**.\n", "\n", - "For ease of use, `requests.post` has a\n", + "For **ease of use**, `requests.post` has a\n", "[`json` parameter](https://docs.python-requests.org/en/master/user/quickstart/#more-complicated-post-requests)\n", - "that automatically attaches the header `Content-Type: application/json` and encodes our data into a JSON string." + "that **automatically attaches the header** `Content-Type: application/json` and encodes our data into a JSON string." ] }, { @@ -1091,7 +1136,7 @@ "id": "45d64a22-887a-4469-9ba3-668ec23f8667", "metadata": {}, "source": [ - "# Get Predictions" + "# 4.🔮 Get Predictions" ] }, { @@ -1168,7 +1213,7 @@ "id": "9d93aaa8", "metadata": {}, "source": [ - "The prediction service expects the data was want a prediction for as well as the bucket where our saved model assets are stored." + "The prediction service **expects two things** the **input data** for the prediction as well as the Cloud Storage **path** where the model is stored." ] }, { @@ -1190,7 +1235,7 @@ "id": "2222aaf9", "metadata": {}, "source": [ - "# Visualize Results" + "# 4. 🗺️ Visualize predictions" ] }, { @@ -1236,7 +1281,7 @@ "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd", "metadata": {}, "source": [ - "Next, we can plot these points on a map. Red means our model predicts that the towers are on, and blue means that it predicts the towers are off." + "Next, we can **plot** these points on a map. **Red** means our model predicts that the towers are **\"on\"**, and **blue** means that it predicts the towers are **\"off\"**." ] }, { @@ -1264,23 +1309,10 @@ }, { "cell_type": "code", - "execution_count": 539, + "execution_count": 1, "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "colormap = cm.LinearColormap(colors=['red','lightblue'], index=[90,100], vmin=90, vmax=100)\n", "map = folium.Map([43.45, -7.87], zoom_start=14, tiles='CartoDB positron')\n", @@ -1302,7 +1334,7 @@ "id": "efcabd53-e81e-4d72-a453-e2fb6dcaa7ae", "metadata": {}, "source": [ - "# Clean Up" + "# 6. 🧹 Clean Up" ] }, { @@ -1310,7 +1342,7 @@ "id": "b002e48a-035c-4369-a161-aa2d800a70b2", "metadata": {}, "source": [ - "To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources." + "To **avoid incurring charges** to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources." ] }, { @@ -1320,7 +1352,7 @@ "source": [ "## Deleting the project\n", "\n", - "The easiest way to eliminate billing is to delete the project that you created for the tutorial.\n", + "The **easiest** way to **eliminate billing** is to delete the project that you created for the tutorial.\n", "\n", "To delete the project:\n", "\n", @@ -1330,7 +1362,7 @@ ">\n", "> * **Custom project IDs are lost.** When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.\n", ">\n", - "> If you plan to explore multiple tutorials and quickstarts, reusing projects can help you avoid exceeding project quota limits.\n", + "> If you plan to explore multiple tutorials and quickstarts, **reusing** projects can help you avoid exceeding project **quota limits**.\n", "\n", "1. In the Cloud Console, go to the **Manage resources** page.\n", "\n", diff --git a/people-and-planet-ai/geospatial-classification/serving_app/predict.py b/people-and-planet-ai/geospatial-classification/serving_app/predict.py index 24a9e5cefd87..47d023125517 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/predict.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/predict.py @@ -13,8 +13,6 @@ # limitations under the License. -import os - import numpy as np import tensorflow as tf From 37a78981cef414e719478775db3e1dfb0ac76a34 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Tue, 4 Jan 2022 16:45:08 -0600 Subject: [PATCH 04/30] Small updates to geospatial sample --- .../geospatial-classification/README.ipynb | 201 ++++++------------ .../geospatial-classification/e2e_test.py | 15 +- .../labeled_geospatial_data.csv | 4 +- .../geospatial-classification/trainer/task.py | 17 +- 4 files changed, 91 insertions(+), 146 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index 40c07e2877dd..ec9585bda5a5 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -184,102 +184,112 @@ }, { "cell_type": "markdown", - "id": "980b439f-03bb-427f-ae61-4d6d5f066d15", + "id": "156ab72c", "metadata": {}, "source": [ - "### ✏️ Enter your Cloud project's details. Ensure you provide a regional bucket!" + "### ⛏️ Preparing the project environment\n", + "\n", + "Click the run ▶️ icons in order for the cells to download and install the necessary code, libraries, and resources for this solution.\n", + "\n", + "> 💡 You can _optionally_ view the entire\n", + "> [code in GitHub](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai/geospatial-classification)." + ] + }, + { + "cell_type": "markdown", + "id": "8a363277-2195-42b4-83e7-3e3921bb3db1", + "metadata": {}, + "source": [ + "### ↘️ Install the Vertex AI SDK\n", + "\n", + "Note that you will be prompted to restart the runtime after installation." ] }, { "cell_type": "code", "execution_count": null, - "id": "246702e7", + "id": "fc25399d-7d5d-4050-9442-2ef2c7d83e82", "metadata": {}, "outputs": [], "source": [ - "#@title My Google Cloud resources\n", - "project = '' #@param {type:\"string\"}\n", - "bucket = '' #@param {type:\"string\"}\n", - "region = 'us-central1' #@param {type:\"string\"}\n", - "\n", - "# Validate the inputs.\n", - "if not project:\n", - " raise ValueError(f\"Please provide a value for 'project'\")\n", - "if not bucket:\n", - " raise ValueError(f\"Please provide a value for 'bucket'\")\n", - "if not region:\n", - " raise ValueError(f\"Please provide a value for 'region'\")\n", - "\n", - "# Authenticate\n", - "from google.colab import auth\n", - "\n", - "auth.authenticate_user()\n", - "print('Authenticated')\n", - "\n", - "!gcloud config set project {project}" + "!pip3 install --user --upgrade google-cloud-aiplatform" ] }, { - "cell_type": "markdown", - "id": "156ab72c", + "cell_type": "code", + "execution_count": null, + "id": "63dffaaf-5321-47e7-9dfa-ff82cd0e6303", "metadata": {}, + "outputs": [], "source": [ - "### ⛏️ Preparing the project environment\n", - "\n", - "Click the run ▶️ icons in order for the cells to download and install the necessary code, libraries, and resources for this solution.\n", - "\n", - "> 💡 You can _optionally_ view the entire\n", - "> [code in GitHub](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai/geospatial-classification)." + "from google.cloud import aiplatform" ] }, { "cell_type": "code", - "execution_count": 332, - "id": "c2848f6d", + "execution_count": null, + "id": "45965fcc-2ff1-4b27-8c20-6f6af7667271", "metadata": {}, "outputs": [], "source": [ - "# Get the sample source code.\n", - "\n", - "## TODO" + "aiplatform.init(project=project, staging_bucket=bucket)" ] }, { "cell_type": "markdown", - "id": "8a363277-2195-42b4-83e7-3e3921bb3db1", + "id": "fa34fdd8", "metadata": {}, "source": [ - "### ↘️ Install the Vertex AI SDK" + "### ↘️ Get the code" ] }, { "cell_type": "code", - "execution_count": null, - "id": "fc25399d-7d5d-4050-9442-2ef2c7d83e82", + "execution_count": 332, + "id": "c2848f6d", "metadata": {}, "outputs": [], "source": [ - "!pip3 install --user --upgrade google-cloud-aiplatform" + "# Get the sample source code.\n", + "\n", + "## TODO" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "63dffaaf-5321-47e7-9dfa-ff82cd0e6303", + "cell_type": "markdown", + "id": "980b439f-03bb-427f-ae61-4d6d5f066d15", "metadata": {}, - "outputs": [], "source": [ - "from google.cloud import aiplatform" + "### ✏️ Enter your Cloud project's details. Ensure you provide a regional bucket!" ] }, { "cell_type": "code", "execution_count": null, - "id": "45965fcc-2ff1-4b27-8c20-6f6af7667271", + "id": "246702e7", "metadata": {}, "outputs": [], "source": [ - "aiplatform.init(project=project, staging_bucket=bucket)" + "#@title My Google Cloud resources\n", + "project = '' #@param {type:\"string\"}\n", + "bucket = '' #@param {type:\"string\"}\n", + "region = 'us-central1' #@param {type:\"string\"}\n", + "\n", + "# Validate the inputs.\n", + "if not project:\n", + " raise ValueError(f\"Please provide a value for 'project'\")\n", + "if not bucket:\n", + " raise ValueError(f\"Please provide a value for 'bucket'\")\n", + "if not region:\n", + " raise ValueError(f\"Please provide a value for 'region'\")\n", + "\n", + "# Authenticate\n", + "from google.colab import auth\n", + "\n", + "auth.authenticate_user()\n", + "print('Authenticated')\n", + "\n", + "!gcloud config set project {project}" ] }, { @@ -346,7 +356,7 @@ "# Define constants\n", "\n", "BANDS = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']\n", - "LABEL = 'label'" + "LABEL = 'is_powered_on'" ] }, { @@ -381,91 +391,10 @@ }, { "cell_type": "code", - "execution_count": 585, - "id": "3a0e80f5-23ce-46df-b81d-742068d46181", + "execution_count": null, + "id": "fe94b4d3", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " timestamp lat lon label\n", - "0 2020-07-03 16:32:41.397000+00:00 39.11613 -84.80529 1\n", - "1 2018-06-09 16:25:19.280000+00:00 39.11613 -84.80529 1\n", - "2 2017-11-24 16:36:14.460000+00:00 39.11613 -84.80529 0\n", - "3 2019-11-01 16:32:42.327000+00:00 39.11613 -84.80529 0\n", - "4 2020-05-09 16:32:43.614000+00:00 39.11613 -84.80529 1" - ] - }, - "execution_count": 585, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "labels_dataframe.head()" ] @@ -490,11 +419,11 @@ "def create_label_fc(path):\n", " '''Creates a FeatureCollection from the label dataframe.'''\n", "\n", - " labels_dataframe = pd.read_csv(path)\n", + " dataframe = pd.read_csv(path)\n", " num_examples = dataframe.shape[0]\n", " data_dict = dataframe.to_dict()\n", " feats = []\n", - " properties = ['timestamp', 'label']\n", + " properties = ['timestamp', 'is_powered_on']\n", " for idx in np.arange(num_examples):\n", " feat_dict = {}\n", " geometry = ee.Geometry.Point([data_dict['lon'][idx], data_dict['lat'][idx]]) \n", @@ -912,7 +841,7 @@ "job = aiplatform.CustomTrainingJob(\n", " display_name=\"geospatial_model_training\",\n", " script_path=\"task.py\",\n", - " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-5:latest\")" + " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest\")" ] }, { diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 2ab0e53351f7..1cbf764351c3 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -30,8 +30,6 @@ import ee import google.auth -credentials, project = google.auth.default() -ee.Initialize(credentials, project=project) PYTHON_VERSION = "".join(platform.python_version_tuple()[0:2]) @@ -69,7 +67,10 @@ "B11", "B12", ] -LABEL = "label" +LABEL = "is_powered_on" + +credentials, project = google.auth.default() +ee.Initialize(credentials, project=project) logging.getLogger().setLevel(logging.INFO) @@ -142,7 +143,7 @@ def create_label_fc(path): num_examples = dataframe.shape[0] data_dict = dataframe.to_dict() feats = [] - properties = ["timestamp", "label"] + properties = ["timestamp", "is_powered_on"] for idx in np.arange(num_examples): feat_dict = {} geometry = ee.Geometry.Point([data_dict["lon"][idx], data_dict["lat"][idx]]) @@ -309,12 +310,12 @@ def train_model(bucket_name): job = aiplatform.CustomTrainingJob( display_name="climate_script_colab", script_path="task.py", - container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-5:latest", + container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest", ) model = job.run(args=[f"--bucket={bucket_name}"]) - resource_name = job.resource_name - logging.info(f"train_model resource_name: {resource_name}") + + logging.info(f"train_model resource_name: {job.resource_name}") # Wait until the model training job finishes. status = None diff --git a/people-and-planet-ai/geospatial-classification/labeled_geospatial_data.csv b/people-and-planet-ai/geospatial-classification/labeled_geospatial_data.csv index e8a90051b017..2e446bbb7cc7 100644 --- a/people-and-planet-ai/geospatial-classification/labeled_geospatial_data.csv +++ b/people-and-planet-ai/geospatial-classification/labeled_geospatial_data.csv @@ -1,4 +1,4 @@ -timestamp,lat,lon,label +timestamp,lat,lon,is_powered_on 2020-07-03 16:32:41.397000+00:00,39.11613,-84.80529,1 2018-06-09 16:25:19.280000+00:00,39.11613,-84.80529,1 2017-11-24 16:36:14.460000+00:00,39.11613,-84.80529,0 @@ -6553,4 +6553,4 @@ timestamp,lat,lon,label 2017-12-01 11:24:25.460000+00:00,52.86516,-1.2614,1 2017-12-01 11:24:25.460000+00:00,52.86588,-1.25977,1 2017-12-01 11:24:25.460000+00:00,52.86646,-1.26157,1 -2017-12-01 11:24:25.460000+00:00,52.86717,-1.2599,1 \ No newline at end of file +2017-12-01 11:24:25.460000+00:00,52.86717,-1.2599,1 diff --git a/people-and-planet-ai/geospatial-classification/trainer/task.py b/people-and-planet-ai/geospatial-classification/trainer/task.py index b99dc342d3f8..4cd214a60d70 100644 --- a/people-and-planet-ai/geospatial-classification/trainer/task.py +++ b/people-and-planet-ai/geospatial-classification/trainer/task.py @@ -12,6 +12,21 @@ # See the License for the specific language governing permissions and # limitations under the License. + +"""This training script trains binary classifier on Sentinel-2 satellite images. +The model is a fully convolutional neural network that predicts whether a power +plant is turned on or off. + +A Sentinel-2 image consists of 13 bands. Each band contains the data for a +specific range of the electromagnetic spectrum. + +A JPEG image consists of three channels: Red, Green, and Blue. For Sentinel-2 +images, these correspond to Band 4 (red), Band 3 (green), and Band 2 (blue). +These bands contain the raw pixel data directly from the satellite sensors. +For more information on the Sentinel-2 dataset: +https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description +""" + import tensorflow as tf import argparse @@ -30,7 +45,7 @@ "B11", "B12", ] -LABEL = "label" +LABEL = "is_powered_on" def get_args(): From 387b7b850bd50836aabfa78e8c1d88e7ef5a6a5f Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Tue, 4 Jan 2022 18:00:32 -0600 Subject: [PATCH 05/30] updates to requirements txt --- .../requirements-test.txt | 1 + .../serving_app/Dockerfile | 1 - .../serving_app/requirements.txt | 1 + .../geospatial-classification/trainer/task.py | 25 ++++++++----------- 4 files changed, 13 insertions(+), 15 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/requirements-test.txt b/people-and-planet-ai/geospatial-classification/requirements-test.txt index deaa7f5dbf7a..29a288fa7a6e 100644 --- a/people-and-planet-ai/geospatial-classification/requirements-test.txt +++ b/people-and-planet-ai/geospatial-classification/requirements-test.txt @@ -1,4 +1,5 @@ earthengine-api==0.1.292 pytest==6.2.4 +pytest-xdist==2.5.0 google-cloud-aiplatform==1.8.0 pandas==1.3.5 diff --git a/people-and-planet-ai/geospatial-classification/serving_app/Dockerfile b/people-and-planet-ai/geospatial-classification/serving_app/Dockerfile index 0fac63570511..24fe02c4ab23 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/Dockerfile +++ b/people-and-planet-ai/geospatial-classification/serving_app/Dockerfile @@ -6,4 +6,3 @@ COPY *.py ./ COPY requirements.txt ./ RUN pip install --no-cache-dir -r requirements.txt -RUN pip install tensorflow diff --git a/people-and-planet-ai/geospatial-classification/serving_app/requirements.txt b/people-and-planet-ai/geospatial-classification/serving_app/requirements.txt index df6b8ae0f008..2aefd41b7984 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/requirements.txt +++ b/people-and-planet-ai/geospatial-classification/serving_app/requirements.txt @@ -1,3 +1,4 @@ Flask==2.0.1 gunicorn==20.1.0 numpy==1.21.4 +tensorflow==2.7.0 diff --git a/people-and-planet-ai/geospatial-classification/trainer/task.py b/people-and-planet-ai/geospatial-classification/trainer/task.py index 4cd214a60d70..0a437355bdb6 100644 --- a/people-and-planet-ai/geospatial-classification/trainer/task.py +++ b/people-and-planet-ai/geospatial-classification/trainer/task.py @@ -46,6 +46,7 @@ "B12", ] LABEL = "is_powered_on" +BATCH_SIZE = 64 def get_args(): @@ -92,23 +93,19 @@ def create_datasets(bucket): eval_data_dir = f"gs://{bucket}/geospatial_validation.tfrecord.gz" features_dict = create_features_dict() - training_dataset = tf.data.TFRecordDataset(train_data_dir, compression_type="GZIP") - training_dataset = training_dataset.map( - lambda example_proto: parse_tfrecord(example_proto, features_dict) + training_dataset = ( + tf.data.TFRecordDataset(train_data_dir, compression_type="GZIP") + .map(lambda example_proto: parse_tfrecord(example_proto, features_dict)) + .map(lambda inputs: get_feature_and_label_vectors(inputs, features_dict)) + .batch(64) ) - training_dataset = training_dataset.map( - lambda inputs: get_feature_and_label_vectors(inputs, features_dict) - ) - training_dataset = training_dataset.batch(64) - validation_dataset = tf.data.TFRecordDataset(eval_data_dir, compression_type="GZIP") - validation_dataset = validation_dataset.map( - lambda example_proto: parse_tfrecord(example_proto, features_dict) - ) - validation_dataset = validation_dataset.map( - lambda inputs: get_feature_and_label_vectors(inputs, features_dict) + validation_dataset = ( + tf.data.TFRecordDataset(eval_data_dir, compression_type="GZIP") + .map(lambda example_proto: parse_tfrecord(example_proto, features_dict)) + .map(lambda inputs: get_feature_and_label_vectors(inputs, features_dict)) + .batch(64) ) - validation_dataset = validation_dataset.batch(64) return training_dataset, validation_dataset From 78638a1791514a052b3201faeb5d9b98cc5b446d Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Tue, 4 Jan 2022 19:01:23 -0600 Subject: [PATCH 06/30] fix minor bugs in notebook --- .../geospatial-classification/README.ipynb | 33 ++++++++----------- .../geospatial-classification/e2e_test.py | 10 ++---- 2 files changed, 16 insertions(+), 27 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index ec9585bda5a5..9c0885d50c94 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -225,16 +225,6 @@ "from google.cloud import aiplatform" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "45965fcc-2ff1-4b27-8c20-6f6af7667271", - "metadata": {}, - "outputs": [], - "source": [ - "aiplatform.init(project=project, staging_bucket=bucket)" - ] - }, { "cell_type": "markdown", "id": "fa34fdd8", @@ -292,6 +282,16 @@ "!gcloud config set project {project}" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "b96b7201", + "metadata": {}, + "outputs": [], + "source": [ + "aiplatform.init(project=project, staging_bucket=bucket)" + ] + }, { "cell_type": "markdown", "id": "4ec73dd9", @@ -840,7 +840,7 @@ "source": [ "job = aiplatform.CustomTrainingJob(\n", " display_name=\"geospatial_model_training\",\n", - " script_path=\"task.py\",\n", + " script_path=\"trainer/task.py\",\n", " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest\")" ] }, @@ -1119,12 +1119,7 @@ " collection=ee.FeatureCollection([feature]), scale=10).first()\n", " ])\n", " \n", - " # download FeatureCollection as JSON\n", - " url = fc.getDownloadURL('geojson')\n", - " bytes = requests.get(url).content\n", - " values=bytes.decode(\"utf-8\")\n", - " json_values = json.loads(values)\n", - " return json_values['features'][0]['properties']" + " return fc.getInfo()['features'][0]['properties']" ] }, { @@ -1202,7 +1197,7 @@ " url=f'{service_url}/predict',\n", " headers={'Authorization': f'Bearer {identity_token}'},\n", " json={'data': prediction_data, 'bucket': bucket},).json()\n", - " preds.append(result['predictions']['predictions'][0][0][0][0])" + " plant_predictions.append(result['predictions']['predictions'][0][0][0][0])" ] }, { @@ -1245,7 +1240,7 @@ "source": [ "colormap = cm.LinearColormap(colors=['red','lightblue'], index=[90,100], vmin=90, vmax=100)\n", "map = folium.Map([43.45, -7.87], zoom_start=14, tiles='CartoDB positron')\n", - "for loc, p in zip(zip(lat, lon), preds):\n", + "for loc, p in zip(zip(lat, lon), plant_predictions):\n", " folium.Circle(\n", " location=loc,\n", " radius=20,\n", diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 1cbf764351c3..36b9c878814e 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -309,7 +309,7 @@ def train_model(bucket_name): aiplatform.init(project=PROJECT, staging_bucket=bucket_name) job = aiplatform.CustomTrainingJob( display_name="climate_script_colab", - script_path="task.py", + script_path="trainer/task.py", container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest", ) @@ -350,13 +350,7 @@ def get_prediction_data(feature, start, end): ] ) - # download FeatureCollection as JSON - url = fc.getDownloadURL("geojson") - bytes = requests.get(url).content - values = bytes.decode("utf-8") - json_values = json.loads(values) - return json_values["features"][0]["properties"] - + return fc.getInfo()['features'][0]['properties'] def test_predict(bucket_name, test_data, train_model, service_url, identity_token): From 0d344cf1af402bc61a7a3206f60c89b71d8191ea Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 5 Jan 2022 08:22:29 -0600 Subject: [PATCH 07/30] added requirmenets file --- people-and-planet-ai/geospatial-classification/e2e_test.py | 5 ----- .../geospatial-classification/requirements.txt | 7 +++++++ 2 files changed, 7 insertions(+), 5 deletions(-) create mode 100644 people-and-planet-ai/geospatial-classification/requirements.txt diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 36b9c878814e..a265c1a2f1d5 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -203,7 +203,6 @@ def container_image(): "submit", "serving_app", f"--tag={container_image}", - f"--project={PROJECT}", "--machine-type=e2-highcpu-8", "--timeout=15m", "--quiet", @@ -245,10 +244,6 @@ def service_url(bucket_name, container_image): f"--project={PROJECT}", f"--region={REGION}", "--memory=1G", - f"--set-env-vars=PROJECT={PROJECT}", - f"--set-env-vars=STORAGE_PATH=gs://{bucket_name}", - f"--set-env-vars=REGION={REGION}", - f"--set-env-vars=CONTAINER_IMAGE={container_image}", "--no-allow-unauthenticated", ] ) diff --git a/people-and-planet-ai/geospatial-classification/requirements.txt b/people-and-planet-ai/geospatial-classification/requirements.txt new file mode 100644 index 000000000000..d5754cf508f0 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/requirements.txt @@ -0,0 +1,7 @@ +numpy==1.21.4 +tensorflow==2.7.0 +earthengine-api==0.1.292 +pytest==6.2.4 +pytest-xdist==2.5.0 +google-cloud-aiplatform==1.8.0 +pandas==1.3.5 From ffbd00d9f8025516fdf6448e9e1cc5519d345f05 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 5 Jan 2022 21:23:52 -0600 Subject: [PATCH 08/30] simplify prediction and data extraction logic --- .../geospatial-classification/README.ipynb | 407 +++++------------- .../{trainer => }/task.py | 0 2 files changed, 102 insertions(+), 305 deletions(-) rename people-and-planet-ai/geospatial-classification/{trainer => }/task.py (100%) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index 9c0885d50c94..d675e8a1c84f 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -343,7 +343,8 @@ "\n", "import pandas as pd\n", "import numpy as np\n", - "from pprint import pprint" + "from pprint import pprint\n", + "from datetime import datetime, timedelta" ] }, { @@ -355,8 +356,14 @@ "source": [ "# Define constants\n", "\n", + "LABEL = 'is_powered_on'\n", + "IMAGE_COLLECTION = \"COPERNICUS/S2\"\n", "BANDS = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']\n", - "LABEL = 'is_powered_on'" + "SCALE = 10\n", + "\n", + "TRAIN_VALIDATION_SPLIT = 0.7\n", + "\n", + "PATCH_SIZE = 16" ] }, { @@ -386,7 +393,7 @@ "source": [ "Each row in this dataframe represents a power plant at a particular timestamp. \n", "\n", - "The label column indicates whether or not the power plant was turned **on (1)** or **off (0)** at that timestamp." + "The \"is_powered_on\" column indicates whether or not the power plant was turned **on (1)** or **off (0)** at that timestamp." ] }, { @@ -401,167 +408,29 @@ }, { "cell_type": "markdown", - "id": "3aa51b23", - "metadata": {}, - "source": [ - "Next, we import this data into an Earth Engine [`Feature Collection`](https://developers.google.com/earth-engine/apidocs/ee-featurecollection).\n", - "\n", - "In Earth Engine, a [`Feature`](https://developers.google.com/earth-engine/guides/features) is an object with a _geometry property_ storing a [`Geometry`](https://developers.google.com/earth-engine/guides/geometries) object, and a _properties property_ storing a dictionary of other properties. Groups of related `Features` can be combined into a `FeatureCollection` to enable additional operations on the entire set such as filtering, sorting, and rendering." - ] - }, - { - "cell_type": "code", - "execution_count": 659, - "id": "3a8b42dd", - "metadata": {}, - "outputs": [], - "source": [ - "def create_label_fc(path):\n", - " '''Creates a FeatureCollection from the label dataframe.'''\n", - "\n", - " dataframe = pd.read_csv(path)\n", - " num_examples = dataframe.shape[0]\n", - " data_dict = dataframe.to_dict()\n", - " feats = []\n", - " properties = ['timestamp', 'is_powered_on']\n", - " for idx in np.arange(num_examples):\n", - " feat_dict = {}\n", - " geometry = ee.Geometry.Point([data_dict['lon'][idx], data_dict['lat'][idx]]) \n", - " for feature in properties:\n", - " feat_dict[feature] = data_dict[feature][idx]\n", - " feat = ee.Feature(geometry, feat_dict)\n", - " feats.append(feat)\n", - " return ee.FeatureCollection(feats)" - ] - }, - { - "cell_type": "code", - "execution_count": 660, - "id": "6eb9f78d", - "metadata": {}, - "outputs": [], - "source": [ - "label_fc = create_label_fc('labeled_geospatial_data.csv')" - ] - }, - { - "cell_type": "markdown", - "id": "cb421ba4-c9b1-4d81-b543-656766fac47c", - "metadata": {}, - "source": [ - "Let's take a look at the first element in the `FeatureCollection`. You can see that this is equivalent to the first element in our dataframe, but just in a different format." - ] - }, - { - "cell_type": "code", - "execution_count": 662, - "id": "eeb5b6c2-4f53-481b-96d1-07a39415988a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'geometry': {'coordinates': [-84.80529, 39.11613], 'type': 'Point'},\n", - " 'id': '0',\n", - " 'properties': {'label': 1, 'timestamp': '2020-07-03 16:32:41.397000+00:00'},\n", - " 'type': 'Feature'}\n" - ] - } - ], - "source": [ - "pprint(label_fc.first().getInfo())" - ] - }, - { - "cell_type": "markdown", - "id": "1cc8b4a2", - "metadata": {}, - "source": [ - "Next, we define a preprocessing function that will reformat the timestamp into two new properties: `start` and `end`." - ] - }, - { - "cell_type": "code", - "execution_count": 663, - "id": "264aea7a", + "id": "da94bcd4", "metadata": {}, - "outputs": [], "source": [ - "def format_date(feature):\n", - " '''Creates start date and end date properties.'''\n", - " \n", - " # Extract start and end dates\n", - " timestamp = ee.String(feature.get('timestamp')).split(' ').get(0)\n", - " year = ee.Number.parse(ee.String(timestamp).split('-').get(0))\n", - " month = ee.Number.parse(ee.String(timestamp).split('-').get(1))\n", - " day = ee.Number.parse(ee.String(timestamp).split('-').get(2))\n", - " start = ee.Date.fromYMD(year, month, day)\n", - " end = start.advance(1, 'day')\n", - "\n", - " # Create new feature\n", - " feature = feature.set({'start': start, 'end': end})\n", - "\n", - " return feature" + "### 🎛️ Create train/validation splits" ] }, { "cell_type": "markdown", - "id": "61a35408-f7f1-46bd-a73f-db4a48538313", + "id": "6c0efe81", "metadata": {}, "source": [ - "We map this preprocessing function across our `FeatureCollection`." + "Before we can train an ML model, we need to split this data into training and validation datasets. We will do this by creating two new dataframes with a 70/30 training validation split." ] }, { "cell_type": "code", - "execution_count": 664, - "id": "3132cdda", + "execution_count": null, + "id": "d86e899d", "metadata": {}, "outputs": [], "source": [ - "label_fc = label_fc.map(format_date)" - ] - }, - { - "cell_type": "markdown", - "id": "f4524d75", - "metadata": {}, - "source": [ - "Let's examine the first feature in this collection to see what it looks like after the map operation. \n", - "\n", - "You can see that each feature has a `Geometry`, which is a specific latitude and longitude, as well as a set of properties:\n", - "\n", - "* `label` indicates whether the power plant is on (1) or off (0)\n", - "* `start` is the datetime for when this observation was made\n", - "* `end` is a datetime one day after `start`\n", - "* `timestamp` is the original timestamp pulled from the CSV file\n", - "\n", - "The `label` property is what we want our model to predict. And the `start` and `end` timestamps will be helpful when we extract the corresponding Sentinel images because each label is for a specific point in time." - ] - }, - { - "cell_type": "code", - "execution_count": 665, - "id": "01cc0ad2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'geometry': {'coordinates': [-84.80529, 39.11613], 'type': 'Point'},\n", - " 'id': '0',\n", - " 'properties': {'end': {'type': 'Date', 'value': 1593820800000},\n", - " 'label': 1,\n", - " 'start': {'type': 'Date', 'value': 1593734400000},\n", - " 'timestamp': '2020-07-03 16:32:41.397000+00:00'},\n", - " 'type': 'Feature'}\n" - ] - } - ], - "source": [ - "pprint(label_fc.first().getInfo())" + "train_dataframe=labels_dataframe.sample(frac=TRAIN_VALIDATION_SPLIT,random_state=200) #random state is a seed value\n", + "validation_dataframe =labels_dataframe.drop(train_dataframe.index).sample(frac=1.0)" ] }, { @@ -579,9 +448,11 @@ "source": [ "In Earth Engine, an [`ImageCollection`](https://developers.google.com/earth-engine/guides/ic_creating) is a stack or sequence of images. An [`Image`](https://developers.google.com/earth-engine/guides/image_overview) is composed of one or more bands and each band has its own name, data type, scale, mask and projection. The [`Sentinel-2`](https://developers.google.com/earth-engine/guides/ic_creating) dataset is represented as an `ImageCollection`, where each image in the collection is of a specific geographic location at a particular time.\n", "\n", - "In the cell below, we write a function to extract the Sentinel image taken at the specific latitude/longitude and timestamp for each `Feature` in our `FeatureCollection`. \n", + "In the cell below, we write a function to extract the Sentinel image taken at the specific latitude/longitude and timestamp for each row of our dataframe.\n", "\n", - "To do this, we first filter the Sentinel-2 `ImageCollection` at the start/end dates for a particular `Feature`.\n", + "We will store all of this information as an Earth Engine [`Feature Collection`](https://developers.google.com/earth-engine/apidocs/ee-featurecollection). In Earth Engine, a [`Feature`](https://developers.google.com/earth-engine/guides/features) is an object with a _geometry property_ storing a [`Geometry`](https://developers.google.com/earth-engine/guides/geometries) object, and a _properties property_ storing a dictionary of other properties. Groups of related `Features` can be combined into a `FeatureCollection` to enable additional operations on the entire set such as filtering, sorting, and rendering. \n", + "\n", + "We first filter the Sentinel-2 `ImageCollection` at the start/end dates for a particular row in our dataframe.\n", "\n", "Then, using the [`neighorboodToArray`](https://developers.google.com/earth-engine/api_docs#eeimageneighborhoodtoarray) method we create a new `FeatureCollection` that contains the satellite data for each band at the latitude and longitude of interest as well as a 16 pixel padding around that point.\n", "\n", @@ -592,41 +463,47 @@ }, { "cell_type": "code", - "execution_count": 592, - "id": "a93f1947", + "execution_count": 659, + "id": "3a8b42dd", "metadata": {}, "outputs": [], "source": [ - "def get_neighboring_patch(feature):\n", - " '''Gets image pixel values for patch.'''\n", - " \n", - " # filter ImageCollection at start/end dates. \n", - " image = ee.ImageCollection('COPERNICUS/S2').filterDate(\n", - " feature.get('start'), feature.get('end')).select(BANDS).median()\n", - " \n", - " # extract pixel values at the lat/lon with a 16x16 padding\n", - " return ee.FeatureCollection([\n", - " image.neighborhoodToArray(ee.Kernel.square(16)).sampleRegions(\n", - " collection=ee.FeatureCollection([feature]), scale=10).first()\n", - " ])" - ] - }, - { - "cell_type": "markdown", - "id": "4d7a95a7", - "metadata": {}, - "source": [ - "We map this function across the `FeatureCollection` and flatten it so we have a single `FeatureCollection`." + "def labeled_feature(row):\n", + " start = datetime.fromisoformat(row.timestamp)\n", + " end = start + timedelta(days=1)\n", + " image = (\n", + " ee.ImageCollection(IMAGE_COLLECTION)\n", + " .filterDate(start.strftime('%Y-%m-%d'), end.strftime('%Y-%m-%d'))\n", + " .select(BANDS)\n", + " .mosaic()\n", + " )\n", + " point = ee.Feature(\n", + " ee.Geometry.Point([row.lon, row.lat]),\n", + " {LABEL: row.is_powered_on},\n", + " )\n", + " return (\n", + " image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE))\n", + " .sampleRegions(ee.FeatureCollection([point]), scale=SCALE)\n", + " .first()\n", + " )" ] }, { "cell_type": "code", - "execution_count": 593, - "id": "6d85ec4a", + "execution_count": null, + "id": "02d13dc8", "metadata": {}, "outputs": [], "source": [ - "data = label_fc.map(get_neighboring_patch).flatten()" + "train_features = [\n", + " labeled_feature(row)\n", + " for row in train_dataframe.itertuples()\n", + "]\n", + "\n", + "validation_features = [\n", + " labeled_feature(row)\n", + " for row in validation_dataframe.itertuples()\n", + "]" ] }, { @@ -634,45 +511,17 @@ "id": "21267b05-38cc-4f14-ab09-197baef26713", "metadata": {}, "source": [ - "Let's look at the properties for the first element in this new `FeatureCollection`. You can see that it still contains the `start`, `end`, `label`, and `timestamp` properties, but with has 13 additional properies, one for each spectral band." + "To get a better sense of what's going on, let's look at the properties for first `Feature` in the `train_features` list. You can see that it contains a property for the label `is_powered_on`, and 13 additional properies, one for each spectral band." ] }, { "cell_type": "code", - "execution_count": 594, - "id": "69933ee9-8aa0-49f7-ad33-c0b17db329bc", + "execution_count": null, + "id": "92fb1154", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['system:index',\n", - " 'start',\n", - " 'end',\n", - " 'label',\n", - " 'timestamp',\n", - " 'B10',\n", - " 'B11',\n", - " 'B12',\n", - " 'B8A',\n", - " 'B1',\n", - " 'B2',\n", - " 'B3',\n", - " 'B4',\n", - " 'B5',\n", - " 'B6',\n", - " 'B7',\n", - " 'B8',\n", - " 'B9']" - ] - }, - "execution_count": 594, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data.first().propertyNames().getInfo()" + "ee.FeatureCollection(train_features[0]).propertyNames().getInfo()" ] }, { @@ -682,7 +531,7 @@ "source": [ "The data contained in each band property is an array of shape 33x33.\n", "\n", - "For example, here is the data for band B1 in the first element of our `FeatureCollection` expressed as a numpy array." + "For example, here is the data for band B1 in the first element in our list expressed as a numpy array." ] }, { @@ -707,50 +556,11 @@ } ], "source": [ - "example_feature = np.array(data.first().get('B1').getInfo())\n", + "example_feature = np.array(train_features[0].get('B1').getInfo())\n", "print(example_feature)\n", "print('shape: ' + str(example_feature.shape))" ] }, - { - "cell_type": "markdown", - "id": "2ab1318d", - "metadata": {}, - "source": [ - "### 🎛️ Create train/validation splits" - ] - }, - { - "cell_type": "markdown", - "id": "86cca925", - "metadata": {}, - "source": [ - "Before we can train an ML model, we need to split this data into training and validation datasets. We can easily do this by adding a random column to our `FeatureCollection` and then filtering the collection on that column." - ] - }, - { - "cell_type": "code", - "execution_count": 623, - "id": "6b4483b6", - "metadata": {}, - "outputs": [], - "source": [ - "data = data.randomColumn()" - ] - }, - { - "cell_type": "code", - "execution_count": 296, - "id": "4f2b67ae", - "metadata": {}, - "outputs": [], - "source": [ - "# 70% of data for training and 30% for validation\n", - "\n", - "training = data.filter(ee.Filter.lt('random', 0.7))\n", - "validation = data.filter(ee.Filter.gte('random', 0.7))" - ] - }, { "cell_type": "markdown", "id": "c02607c8", @@ -779,17 +589,18 @@ "# Export data\n", "\n", "training_task = ee.batch.Export.table.toCloudStorage(\n", - " collection=training,\n", - " description=\"Training image export\",\n", - " bucket=bucket,\n", - " fileNamePrefix=\"geospatial_training\",\n", - " selectors= BANDS + [LABEL],\n", - " fileFormat='TFRecord')\n", + " collection=ee.FeatureCollection(train_features),\n", + " description=\"Training image export: test\",\n", + " bucket=bucket,\n", + " fileNamePrefix=\"geospatial_training\",\n", + " selectors=BANDS + [LABEL],\n", + " fileFormat=\"TFRecord\",\n", + ")\n", "\n", "training_task.start()\n", "\n", "validation_task = ee.batch.Export.table.toCloudStorage(\n", - " collection=validation,\n", + " collection=ee.FeatureCollection(validation_features),\n", " description=\"Validation image export\",\n", " bucket=bucket,\n", " fileNamePrefix=\"geospatial_validation\",\n", @@ -804,7 +615,7 @@ "id": "dadeab16", "metadata": {}, "source": [ - "This export will take around 35 minutes. You can monitor the progress with the following command:" + "This export will take around 10 minutes. You can monitor the progress with the following command:" ] }, { @@ -840,7 +651,7 @@ "source": [ "job = aiplatform.CustomTrainingJob(\n", " display_name=\"geospatial_model_training\",\n", - " script_path=\"trainer/task.py\",\n", + " script_path=\"task.py\",\n", " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest\")" ] }, @@ -1090,16 +901,6 @@ "import json" ] }, - { - "cell_type": "code", - "execution_count": 560, - "id": "570f7e8f-6f68-4433-aa29-ce0e3e4cd604", - "metadata": {}, - "outputs": [], - "source": [ - "plant_location = ee.Feature(ee.Geometry.Point([-84.80529, 39.11613]))" - ] - }, { "cell_type": "code", "execution_count": 561, @@ -1109,17 +910,17 @@ "source": [ "# Extract image data\n", "\n", - "def get_prediction_data(feature, start, end):\n", + "def get_prediction_data(lon, lat, start, end):\n", " '''Extracts Sentinel image as json at specific lat/lon and timestamp.'''\n", "\n", - " image = ee.ImageCollection('COPERNICUS/S2').filterDate(start, end).select(BANDS).mosaic()\n", + " location = ee.Feature(ee.Geometry.Point([lon, lat]))\n", + " image = ee.ImageCollection(IMAGE_COLLECTION).filterDate(start, end).select(BANDS).mosaic()\n", "\n", - " fc = ee.FeatureCollection([\n", - " image.neighborhoodToArray(ee.Kernel.square(16)).sampleRegions(\n", - " collection=ee.FeatureCollection([feature]), scale=10).first()\n", - " ])\n", + " feature = (\n", + " image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE)).sampleRegions(\n", + " collection=ee.FeatureCollection([location]), scale=SCALE))\n", " \n", - " return fc.getInfo()['features'][0]['properties']" + " return feature.getInfo()['features'][0]['properties']" ] }, { @@ -1129,7 +930,7 @@ "metadata": {}, "outputs": [], "source": [ - "prediction_data = get_prediction_data(plant_location, '2021-10-01', '2021-10-31')" + "prediction_data = get_prediction_data(-84.80529, 39.11613, '2021-10-01', '2021-10-31')" ] }, { @@ -1172,15 +973,18 @@ }, { "cell_type": "code", - "execution_count": 533, - "id": "ac680e7f-3598-422a-af5e-bb666425dbd0", + "execution_count": null, + "id": "210f81c0", "metadata": {}, "outputs": [], "source": [ - "locations = [ee.Feature(ee.Geometry.Point([-7.86444, 43.43717])),\n", - " ee.Feature(ee.Geometry.Point([-7.86376, 43.43827])),\n", - " ee.Feature(ee.Geometry.Point([-7.85755, 43.44075])),\n", - " ee.Feature(ee.Geometry.Point([-7.85587, 43.44114])),]" + "def get_prediction(lon, lat, start, end):\n", + " prediction_data = get_prediction_data(lon, lat, start, end)\n", + " result = requests.post(\n", + " url=f'{service_url}/predict',\n", + " headers={'Authorization': f'Bearer {identity_token}'},\n", + " json={'data': prediction_data, 'bucket': bucket},).json()\n", + " return result['predictions']['predictions'][0][0][0][0]" ] }, { @@ -1190,14 +994,18 @@ "metadata": {}, "outputs": [], "source": [ - "plant_predictions = []\n", - "for location in locations:\n", - " prediction_data = get_prediction_data(location, '2021-10-01', '2021-10-31')\n", - " result = requests.post(\n", - " url=f'{service_url}/predict',\n", - " headers={'Authorization': f'Bearer {identity_token}'},\n", - " json={'data': prediction_data, 'bucket': bucket},).json()\n", - " plant_predictions.append(result['predictions']['predictions'][0][0][0][0])" + "lons = [-7.86444, -7.86376, -7.85755, -7.85587]\n", + "lats = [43.43717, 43.43827, 43.44075, 43.44114]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5148711", + "metadata": {}, + "outputs": [], + "source": [ + "plant_predictions = [get_prediction(lon , lat, '2021-10-01', '2021-10-31') for lon, lat in zip(lons, lats)]" ] }, { @@ -1205,7 +1013,7 @@ "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd", "metadata": {}, "source": [ - "Next, we can **plot** these points on a map. **Red** means our model predicts that the towers are **\"on\"**, and **blue** means that it predicts the towers are **\"off\"**." + "Next, we can **plot** these points on a map. **Red** means our model predicts that the towers are **\"off\"**, and **blue** means that it predicts the towers are **\"on\"**." ] }, { @@ -1220,17 +1028,6 @@ "import branca.colormap as cm" ] }, - { - "cell_type": "code", - "execution_count": 523, - "id": "4a0b6bc2-e187-4d0c-90de-636f47db6b20", - "metadata": {}, - "outputs": [], - "source": [ - "lon = [-7.86444, -7.86376, -7.85755, -7.85587]\n", - "lat = [43.43717, 43.43827, 43.44075, 43.44114]" - ] - }, { "cell_type": "code", "execution_count": 1, @@ -1238,9 +1035,9 @@ "metadata": {}, "outputs": [], "source": [ - "colormap = cm.LinearColormap(colors=['red','lightblue'], index=[90,100], vmin=90, vmax=100)\n", + "colormap = cm.LinearColormap(colors=['red','lightblue'], index=[0,1], vmin=0, vmax=1)\n", "map = folium.Map([43.45, -7.87], zoom_start=14, tiles='CartoDB positron')\n", - "for loc, p in zip(zip(lat, lon), plant_predictions):\n", + "for loc, p in zip(zip(lats, lons), plant_predictions):\n", " folium.Circle(\n", " location=loc,\n", " radius=20,\n", diff --git a/people-and-planet-ai/geospatial-classification/trainer/task.py b/people-and-planet-ai/geospatial-classification/task.py similarity index 100% rename from people-and-planet-ai/geospatial-classification/trainer/task.py rename to people-and-planet-ai/geospatial-classification/task.py From 3e94ff612b4d8f4af99ef8bd60551dcb455f2361 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 5 Jan 2022 22:22:07 -0600 Subject: [PATCH 09/30] update tests --- .../geospatial-classification/README.ipynb | 92 +++++++------ .../geospatial-classification/e2e_test.py | 122 +++++++----------- 2 files changed, 94 insertions(+), 120 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index d675e8a1c84f..5b5c5052e5eb 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -408,7 +408,7 @@ }, { "cell_type": "markdown", - "id": "da94bcd4", + "id": "897f8b1a", "metadata": {}, "source": [ "### 🎛️ Create train/validation splits" @@ -416,7 +416,7 @@ }, { "cell_type": "markdown", - "id": "6c0efe81", + "id": "d832f816", "metadata": {}, "source": [ "Before we can train an ML model, we need to split this data into training and validation datasets. We will do this by creating two new dataframes with a 70/30 training validation split." @@ -425,7 +425,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d86e899d", + "id": "05398bb7", "metadata": {}, "outputs": [], "source": [ @@ -469,41 +469,34 @@ "outputs": [], "source": [ "def labeled_feature(row):\n", - " start = datetime.fromisoformat(row.timestamp)\n", - " end = start + timedelta(days=1)\n", - " image = (\n", - " ee.ImageCollection(IMAGE_COLLECTION)\n", - " .filterDate(start.strftime('%Y-%m-%d'), end.strftime('%Y-%m-%d'))\n", - " .select(BANDS)\n", - " .mosaic()\n", - " )\n", - " point = ee.Feature(\n", - " ee.Geometry.Point([row.lon, row.lat]),\n", - " {LABEL: row.is_powered_on},\n", - " )\n", - " return (\n", - " image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE))\n", - " .sampleRegions(ee.FeatureCollection([point]), scale=SCALE)\n", - " .first()\n", - " )" + " start = datetime.fromisoformat(row.timestamp)\n", + " end = start + timedelta(days=1)\n", + " image = (\n", + " ee.ImageCollection(IMAGE_COLLECTION)\n", + " .filterDate(start.strftime(\"%Y-%m-%d\"), end.strftime(\"%Y-%m-%d\"))\n", + " .select(BANDS)\n", + " .mosaic()\n", + " )\n", + " point = ee.Feature(\n", + " ee.Geometry.Point([row.lon, row.lat]),\n", + " {LABEL: row.is_powered_on},\n", + " )\n", + " return (\n", + " image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE))\n", + " .sampleRegions(ee.FeatureCollection([point]), scale=SCALE)\n", + " .first()\n", + " )" ] }, { "cell_type": "code", "execution_count": null, - "id": "02d13dc8", + "id": "52762ae1", "metadata": {}, "outputs": [], "source": [ - "train_features = [\n", - " labeled_feature(row)\n", - " for row in train_dataframe.itertuples()\n", - "]\n", - "\n", - "validation_features = [\n", - " labeled_feature(row)\n", - " for row in validation_dataframe.itertuples()\n", - "]" + "train_features = [labeled_feature(row) for row in train_dataframe.itertuples()]\n", + "validation_features = [labeled_feature(row) for row in validation_dataframe.itertuples()]" ] }, { @@ -517,7 +510,7 @@ { "cell_type": "code", "execution_count": null, - "id": "92fb1154", + "id": "7f230357", "metadata": {}, "outputs": [], "source": [ @@ -600,12 +593,12 @@ "training_task.start()\n", "\n", "validation_task = ee.batch.Export.table.toCloudStorage(\n", - " collection=ee.FeatureCollection(validation_features),\n", - " description=\"Validation image export\",\n", - " bucket=bucket,\n", - " fileNamePrefix=\"geospatial_validation\",\n", - " selectors= BANDS + [LABEL],\n", - " fileFormat='TFRecord')\n", + " collection=ee.FeatureCollection(validation_features),\n", + " description=\"Validation image export\",\n", + " bucket=bucket,\n", + " fileNamePrefix=\"geospatial_validation\",\n", + " selectors= BANDS + [LABEL],\n", + " fileFormat='TFRecord')\n", "\n", "validation_task.start()" ] @@ -911,16 +904,21 @@ "# Extract image data\n", "\n", "def get_prediction_data(lon, lat, start, end):\n", - " '''Extracts Sentinel image as json at specific lat/lon and timestamp.'''\n", + " \"\"\"Extracts Sentinel image as json at specific lat/lon and timestamp.\"\"\"\n", + "\n", + " location = ee.Feature(ee.Geometry.Point([lon, lat]))\n", + " image = (\n", + " ee.ImageCollection(IMAGE_COLLECTION)\n", + " .filterDate(start, end)\n", + " .select(BANDS)\n", + " .mosaic()\n", + " )\n", "\n", - " location = ee.Feature(ee.Geometry.Point([lon, lat]))\n", - " image = ee.ImageCollection(IMAGE_COLLECTION).filterDate(start, end).select(BANDS).mosaic()\n", + " feature = image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE)).sampleRegions(\n", + " collection=ee.FeatureCollection([location]), scale=SCALE\n", + " )\n", "\n", - " feature = (\n", - " image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE)).sampleRegions(\n", - " collection=ee.FeatureCollection([location]), scale=SCALE))\n", - " \n", - " return feature.getInfo()['features'][0]['properties']" + " return feature.getInfo()[\"features\"][0][\"properties\"]" ] }, { @@ -974,7 +972,7 @@ { "cell_type": "code", "execution_count": null, - "id": "210f81c0", + "id": "b9835c8d", "metadata": {}, "outputs": [], "source": [ @@ -1001,7 +999,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e5148711", + "id": "316d4190", "metadata": {}, "outputs": [], "source": [ diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index a265c1a2f1d5..c610154ebb7d 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -19,6 +19,7 @@ import time import uuid import json +from datetime import datetime, timedelta import pandas as pd import numpy as np @@ -69,6 +70,13 @@ ] LABEL = "is_powered_on" +IMAGE_COLLECTION = "COPERNICUS/S2" +SCALE = 10 + +TRAIN_VALIDATION_SPLIT = 0.7 + +PATCH_SIZE = 16 + credentials, project = google.auth.default() ee.Initialize(credentials, project=project) @@ -90,14 +98,20 @@ def bucket_name(): @pytest.fixture(scope="session") def test_data(bucket_name): - label_fc = create_label_fc("labeled_geospatial_data.csv") - label_fc = label_fc.map(format_date) - data = label_fc.map(get_neighboring_patch).flatten() - data = data.randomColumn() - data = data.filter(ee.Filter.gte("random", 0.03)) + labels_dataframe = pd.read_csv("labeled_geospatial_data.csv") + train_dataframe = labels_dataframe.sample( + frac=TRAIN_VALIDATION_SPLIT, random_state=200 + ) # random state is a seed value + validation_dataframe = labels_dataframe.drop(train_dataframe.index).sample(frac=1.0) + + train_features = [labeled_feature(row) for row in train_dataframe.itertuples()] + + validation_features = [ + labeled_feature(row) for row in validation_dataframe.itertuples() + ] training_task = ee.batch.Export.table.toCloudStorage( - collection=data, + collection=ee.FeatureCollection(train_features), description="Training image export: test", bucket=bucket_name, fileNamePrefix="geospatial_training", @@ -108,8 +122,8 @@ def test_data(bucket_name): training_task.start() validation_task = ee.batch.Export.table.toCloudStorage( - collection=data, - description="Validation image export: test", + collection=ee.FeatureCollection(validation_features), + description="Validation image export", bucket=bucket_name, fileNamePrefix="geospatial_validation", selectors=BANDS + [LABEL], @@ -125,7 +139,7 @@ def test_data(bucket_name): for _ in range(0, TIMEOUT_SEC, POLL_INTERVAL_SEC): train_status = ee.data.getOperation(training_task.name)["metadata"]["state"] val_status = ee.data.getOperation(validation_task.name)["metadata"]["state"] - if train_status and val_status in EARTH_ENGINE_FINISHED_STATE: + if train_status in EARTH_ENGINE_FINISHED_STATE and val_status in EARTH_ENGINE_FINISHED_STATE: break time.sleep(POLL_INTERVAL_SEC) @@ -136,59 +150,23 @@ def test_data(bucket_name): yield training_task.name -def create_label_fc(path): - """Creates a FeatureCollection from the label dataframe.""" - - dataframe = pd.read_csv(path) - num_examples = dataframe.shape[0] - data_dict = dataframe.to_dict() - feats = [] - properties = ["timestamp", "is_powered_on"] - for idx in np.arange(num_examples): - feat_dict = {} - geometry = ee.Geometry.Point([data_dict["lon"][idx], data_dict["lat"][idx]]) - for feature in properties: - feat_dict[feature] = data_dict[feature][idx] - feat = ee.Feature(geometry, feat_dict) - feats.append(feat) - return ee.FeatureCollection(feats) - - -def format_date(feature): - """Creates start date and end date properties.""" - - # Extract start and end dates - timestamp = ee.String(feature.get("timestamp")).split(" ").get(0) - year = ee.Number.parse(ee.String(timestamp).split("-").get(0)) - month = ee.Number.parse(ee.String(timestamp).split("-").get(1)) - day = ee.Number.parse(ee.String(timestamp).split("-").get(2)) - start = ee.Date.fromYMD(year, month, day) - end = start.advance(1, "day") - - # Create new feature - feature = feature.set({"start": start, "end": end}) - - return feature - - -def get_neighboring_patch(feature): - """Gets image pixel values for patch.""" - - # filter ImageCollection at start/end dates. +def labeled_feature(row): + start = datetime.fromisoformat(row.timestamp) + end = start + timedelta(days=1) image = ( - ee.ImageCollection("COPERNICUS/S2") - .filterDate(feature.get("start"), feature.get("end")) + ee.ImageCollection(IMAGE_COLLECTION) + .filterDate(start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")) .select(BANDS) - .median() + .mosaic() ) - - # extract pixel values at the lat/lon with a 16x16 padding - return ee.FeatureCollection( - [ - image.neighborhoodToArray(ee.Kernel.square(16)) - .sampleRegions(collection=ee.FeatureCollection([feature]), scale=10) - .first() - ] + point = ee.Feature( + ee.Geometry.Point([row.lon, row.lat]), + {LABEL: row.is_powered_on}, + ) + return ( + image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE)) + .sampleRegions(ee.FeatureCollection([point]), scale=SCALE) + .first() ) @@ -304,7 +282,7 @@ def train_model(bucket_name): aiplatform.init(project=PROJECT, staging_bucket=bucket_name) job = aiplatform.CustomTrainingJob( display_name="climate_script_colab", - script_path="trainer/task.py", + script_path="task.py", container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest", ) @@ -324,35 +302,33 @@ def train_model(bucket_name): logging.info(f"Model job finished with status {status}") assert status == VERTEX_AI_SUCCESS_STATE - yield resource_name + yield job.resource_name -def get_prediction_data(feature, start, end): +def get_prediction_data(lon, lat, start, end): """Extracts Sentinel image as json at specific lat/lon and timestamp.""" + location = ee.Feature(ee.Geometry.Point([lon, lat])) image = ( - ee.ImageCollection("COPERNICUS/S2") + ee.ImageCollection(IMAGE_COLLECTION) .filterDate(start, end) .select(BANDS) .mosaic() ) - fc = ee.FeatureCollection( - [ - image.neighborhoodToArray(ee.Kernel.square(16)) - .sampleRegions(collection=ee.FeatureCollection([feature]), scale=10) - .first() - ] + feature = image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE)).sampleRegions( + collection=ee.FeatureCollection([location]), scale=SCALE ) - return fc.getInfo()['features'][0]['properties'] + return feature.getInfo()["features"][0]["properties"] + def test_predict(bucket_name, test_data, train_model, service_url, identity_token): # Test point - plant_location = ee.Feature(ee.Geometry.Point([-84.80529, 39.11613])) - prediction_data = get_prediction_data(plant_location, "2021-10-01", "2021-10-31") - logging.info(f"plant location: {plant_location})") + prediction_data = get_prediction_data( + -84.80529, 39.11613, "2021-10-01", "2021-10-31" + ) # Make prediction response = requests.post( From b2b939c63442b587c42e50fe28001aee2da5cd92 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 6 Jan 2022 08:39:49 -0600 Subject: [PATCH 10/30] fix linter fail --- .../geospatial-classification/e2e_test.py | 16 ++++++++-------- .../geospatial-classification/noxfile_config.py | 2 +- .../serving_app/main.py | 2 +- .../serving_app/predict.py | 2 +- .../geospatial-classification/task.py | 3 ++- 5 files changed, 13 insertions(+), 12 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index c610154ebb7d..df53a363f159 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -18,18 +18,15 @@ import subprocess import time import uuid -import json from datetime import datetime, timedelta -import pandas as pd -import numpy as np from google.cloud import aiplatform from google.cloud import storage -import pytest -import requests - +import pandas as pd import ee import google.auth +import pytest +import requests PYTHON_VERSION = "".join(platform.python_version_tuple()[0:2]) @@ -139,7 +136,10 @@ def test_data(bucket_name): for _ in range(0, TIMEOUT_SEC, POLL_INTERVAL_SEC): train_status = ee.data.getOperation(training_task.name)["metadata"]["state"] val_status = ee.data.getOperation(validation_task.name)["metadata"]["state"] - if train_status in EARTH_ENGINE_FINISHED_STATE and val_status in EARTH_ENGINE_FINISHED_STATE: + if ( + train_status in EARTH_ENGINE_FINISHED_STATE + and val_status in EARTH_ENGINE_FINISHED_STATE + ): break time.sleep(POLL_INTERVAL_SEC) @@ -286,7 +286,7 @@ def train_model(bucket_name): container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest", ) - model = job.run(args=[f"--bucket={bucket_name}"]) + job.run(args=[f"--bucket={bucket_name}"]) logging.info(f"train_model resource_name: {job.resource_name}") diff --git a/people-and-planet-ai/geospatial-classification/noxfile_config.py b/people-and-planet-ai/geospatial-classification/noxfile_config.py index 5cf936758585..ab4c1c9d82f5 100644 --- a/people-and-planet-ai/geospatial-classification/noxfile_config.py +++ b/people-and-planet-ai/geospatial-classification/noxfile_config.py @@ -1,4 +1,4 @@ -# Copyright 2021 Google LLC +# Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/people-and-planet-ai/geospatial-classification/serving_app/main.py b/people-and-planet-ai/geospatial-classification/serving_app/main.py index 04df0f7c0dd9..15e938c1d298 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/main.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/main.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# Copyright 2021 Google LLC +# Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/people-and-planet-ai/geospatial-classification/serving_app/predict.py b/people-and-planet-ai/geospatial-classification/serving_app/predict.py index 47d023125517..e74315ec1b12 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/predict.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/predict.py @@ -1,4 +1,4 @@ -# Copyright 2021 Google LLC +# Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/people-and-planet-ai/geospatial-classification/task.py b/people-and-planet-ai/geospatial-classification/task.py index 0a437355bdb6..a3b3b33f3005 100644 --- a/people-and-planet-ai/geospatial-classification/task.py +++ b/people-and-planet-ai/geospatial-classification/task.py @@ -27,9 +27,10 @@ https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description """ -import tensorflow as tf import argparse +import tensorflow as tf + BANDS = [ "B1", "B2", From 964453cb9c3d487280cfde9e262f60bafef8bcd3 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 6 Jan 2022 08:49:48 -0600 Subject: [PATCH 11/30] fix linter and header fails --- .../geospatial-classification/e2e_test.py | 6 +++--- people-and-planet-ai/geospatial-classification/task.py | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index df53a363f159..29243498b1de 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -1,4 +1,4 @@ -# Copyright 2021 Google LLC +# Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -17,13 +17,13 @@ import platform import subprocess import time -import uuid from datetime import datetime, timedelta +import uuid from google.cloud import aiplatform from google.cloud import storage -import pandas as pd import ee +import pandas as pd import google.auth import pytest import requests diff --git a/people-and-planet-ai/geospatial-classification/task.py b/people-and-planet-ai/geospatial-classification/task.py index a3b3b33f3005..842da4974741 100644 --- a/people-and-planet-ai/geospatial-classification/task.py +++ b/people-and-planet-ai/geospatial-classification/task.py @@ -1,4 +1,4 @@ -# Copyright 2021 Google LLC +# Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -14,7 +14,7 @@ """This training script trains binary classifier on Sentinel-2 satellite images. -The model is a fully convolutional neural network that predicts whether a power +The model is a fully convolutional neural network that predicts whether a power plant is turned on or off. A Sentinel-2 image consists of 13 bands. Each band contains the data for a @@ -22,9 +22,9 @@ A JPEG image consists of three channels: Red, Green, and Blue. For Sentinel-2 images, these correspond to Band 4 (red), Band 3 (green), and Band 2 (blue). -These bands contain the raw pixel data directly from the satellite sensors. +These bands contain the raw pixel data directly from the satellite sensors. For more information on the Sentinel-2 dataset: -https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description +https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 """ import argparse From bfbcb6d17c320dc8e9cfb2d178045b86c491bf51 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 6 Jan 2022 12:50:57 -0600 Subject: [PATCH 12/30] auth fix for tests --- people-and-planet-ai/geospatial-classification/e2e_test.py | 2 +- .../geospatial-classification/noxfile_config.py | 6 ++---- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 29243498b1de..283c2c80d9ca 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -75,7 +75,7 @@ PATCH_SIZE = 16 credentials, project = google.auth.default() -ee.Initialize(credentials, project=project) +ee.Initialize(credentials, project=PROJECT) logging.getLogger().setLevel(logging.INFO) diff --git a/people-and-planet-ai/geospatial-classification/noxfile_config.py b/people-and-planet-ai/geospatial-classification/noxfile_config.py index ab4c1c9d82f5..b41e2f3352af 100644 --- a/people-and-planet-ai/geospatial-classification/noxfile_config.py +++ b/people-and-planet-ai/geospatial-classification/noxfile_config.py @@ -22,10 +22,8 @@ TEST_CONFIG_OVERRIDE = { # You can opt out from the test for specific Python versions. - # > ℹ️ We're opting out of all Python versions except 3.8. - # > The Python version used is defined by the Dockerfile, so it's redundant - # > to run multiple tests since they would all be running the same Dockerfile. - "ignored_versions": ["2.7", "3.6", "3.7", "3.9", "3.10"], + # > ℹ️ We're opting out of all Python versions except 3.9 which is latest version TensorFlow supports + "ignored_versions": ["2.7", "3.6", "3.7", "3.8", "3.10"], # Old samples are opted out of enforcing Python type hints # All new samples should feature them # "enforce_type_hints": True, From 0d2696cd1623f5e049030c70b44f7a2dfa55c2e8 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 6 Jan 2022 14:30:40 -0600 Subject: [PATCH 13/30] add noxfile to serving app --- .../serving_app/noxfile_config.py | 46 +++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py diff --git a/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py b/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py new file mode 100644 index 000000000000..a128da606af1 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py @@ -0,0 +1,46 @@ +# Copyright 2022 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Default TEST_CONFIG_OVERRIDE for python repos. + +# You can copy this file into your directory, then it will be imported from +# the noxfile.py. + +# The source of truth: +# https://github.com/GoogleCloudPlatform/python-docs-samples/blob/main/noxfile_config.py + +TEST_CONFIG_OVERRIDE = { + # You can opt out from the test for specific Python versions. + # ℹ️ We're opting out of all Python versions as a workaround since there + # are no tests in this directory. This is a temporary fix. + "ignored_versions": ["2.7", "3.6", "3.7", "3.8", "3.9", "3.10"], + # Old samples are opted out of enforcing Python type hints + # All new samples should feature them + # "enforce_type_hints": True, + # An envvar key for determining the project id to use. Change it + # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a + # build specific Cloud project. You can also use your own string + # to use your own Cloud project. + "gcloud_project_env": "GOOGLE_CLOUD_PROJECT", + # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT', + # If you need to use a specific version of pip, + # change pip_version_override to the string representation + # of the version number, for example, "20.2.4" + "pip_version_override": None, + # A dictionary you want to inject into your test. Don't put any + # secrets here. These values will override predefined values. + "envs": { + "PYTEST_ADDOPTS": "-n=8", # parallelize tests in multiple CPUs + }, +} From c06e0e42787680355c726d9b8b0c12e16d9dc788 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 6 Jan 2022 20:15:36 -0600 Subject: [PATCH 14/30] minor updates based on comments --- .../geospatial-classification/README.ipynb | 826 ++++++++++++------ .../geospatial-classification/e2e_test.py | 6 +- .../geospatial-classification/img/outputs.png | Bin 172207 -> 0 bytes .../requirements-test.txt | 3 - .../requirements.txt | 3 +- .../serving_app/noxfile_config.py | 2 +- 6 files changed, 586 insertions(+), 254 deletions(-) delete mode 100644 people-and-planet-ai/geospatial-classification/img/outputs.png diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index 5b5c5052e5eb..ab58cbceed71 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -2,9 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 260, + "execution_count": null, "id": "77c0f36a", - "metadata": {}, + "metadata": { + "id": "77c0f36a" + }, "outputs": [], "source": [ "###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n", @@ -30,16 +32,18 @@ { "cell_type": "markdown", "id": "3683a5f8", - "metadata": {}, + "metadata": { + "id": "3683a5f8" + }, "source": [ "# 🏭 Power Plant ON/OFF: Predictions\n", "\n", "* **Time estimate**: 1 hour\n", - "* **Cost estimate**: less than **5**.00 USD\n", + "* **Cost estimate**: less than $**5**.00 USD\n", "\n", "This is an **interactive** notebook that contains **all** of the **code** necessary to train an ML model from satellite images for a geospatial classification of whether a power plant is on/off. \n", "\n", - "This is a first step **introductory example** of how this **satellite images** can be used to detect **carbon pollution** from power plants.\n", + "This is a first step **introductory example** of how these **satellite images** can be used to detect **carbon pollution** from power plants.\n", "\n", "--------------------------------------------\n", "\n", @@ -50,23 +54,27 @@ { "cell_type": "markdown", "id": "922968ad", - "metadata": {}, + "metadata": { + "id": "922968ad" + }, "source": [ "# 🚏 Overview\n", "\n", - "This notebook leverages geospatial data from [Google Earth Engine](https://earthengine.google.com/) and labeled data provided by the organization [Climate TRACE](https://www.climatetrace.org/). By combining these two data sources, you'll build and train a model that predicts whether or not a power plant is turned on and producing emissions." + "This notebook leverages geospatial data from [Google Earth Engine](https://earthengine.google.com/), and labeled data provided by the organization [Climate TRACE](https://www.climatetrace.org/). By combining these two data sources, you'll build and train a model that predicts whether or not a power plant is turned on and producing emissions." ] }, { "cell_type": "markdown", "id": "8f15adff", - "metadata": {}, + "metadata": { + "id": "8f15adff" + }, "source": [ "### 🛰️ Data _(inputs)_\n", "\n", - "The data in this example consists of images from a satellite called [Sentinel-2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description), for a wide-swath, **high-resolution**, multi-spectral imaging for land monitoring studies.\n", + "The data in this example consists of images from a satellite called [Sentinel-2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description), a wide-swath, **high-resolution**, multi-spectral imaging mission for land monitoring studies.\n", "\n", - "When working with satellite data, each input image has the **dimensions** `[width, height, bands]`. **Bands** are measurements from specific satellite instruments for different ranges of the **electromagnetic spectrum**. For example, Sentinel-2 contains [🌈 13 spectral bands](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#bands). If you are familiar with image classification problems, you can think of the bands as similar to an image's RGB (red, green, blue) channels. However, when working with satellite data we generally have **more than just 3** channels.\n", + "When working with satellite data, each input image has the **dimensions** `[width, height, bands]`. **Bands** are measurements from specific satellite instruments for different ranges of the **electromagnetic spectrum**. For example, Sentinel-2 contains [🌈 13 spectral bands](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#bands). If you're familiar with image classification problems, you can think of the bands as similar to an image's RGB (red, green, blue) channels. However, when working with satellite data we generally have **more than just 3** channels.\n", "\n", "![satellite_inputs](img/inputs.png)\n", "\n" @@ -75,43 +83,44 @@ { "cell_type": "markdown", "id": "d15be296", - "metadata": {}, + "metadata": { + "id": "d15be296" + }, "source": [ "### 🏷️ Labels _(outputs)_\n", "\n", "For each patch of pixels (an image of a power plant) that we give to the model, it performs **binary classification**, which indicates whether the power plant is on or off.\n", "\n", - "In this example, the **output** is a single number between *0 (Off) and 1 (On)*, representing the **probability** of that power plant being ON.\n", - "\n", - "![satellite_outputs](img/outputs.png)\n" + "In this example, the **output** is a single number between *0 (Off) and 1 (On)*, representing the **probability** of that power plant being ON." ] }, { "cell_type": "markdown", "id": "4dce50bc", - "metadata": {}, + "metadata": { + "id": "4dce50bc" + }, "source": [ "### Model _(function)_\n", "\n", "**TL;DR**\n", - "*The model will receive a patch of pixels, in the center is the power plant tower. We then take 16 pixels as padding creating a 33x33 patch. This returns a classification of ON/OFF*\n", + "*The model will receive a patch of pixels, in the center is the power plant tower. We then take 16 pixels as padding creating a 33x33 patch. The model returns a classification of ON/OFF*\n", "\n", - "In this example, we have a CSV file of labels. Each row in this file represents a power plant at a specific lat/lon and timestamp. To deal with the sparsity, at training time we'll prepare a dataset where each input image is a single pixel that we have a label for. We will then add a padding around that image. These padded pixels will not get predictions, but will help our model to make better predictions for the center point that we have a label for.\n", + "In this example, we have a CSV file of labels. Each row in this file represents a power plant at a specific lat/lon and timestamp. At training time we'll prepare a dataset where each input image is a single pixel that we have a label for. We will then add a padding around that image. These padded pixels will not get predictions, but will help our model to make better predictions for the center point that we have a label for.\n", "\n", "For example, with a padding of 16, each 1 pixel input point would become a 33x33 image after the padding is added.\n", "\n", "![training](img/training.png)\n", "\n", - "\n", - "At prediction time, we can pass in an image of any size. We'll add a layer of padding around the image, and will get predictions for each pixel except for the padding.\n", - "\n", - "To handle the variable input size, we'll use a fully convolutional neural network. Using this type of architecture will allow us to have variable input image sizes. Note that while the height and width of the image can be variable, the number of bands must be constant." + "The model in this sample is trained for image patches where a power plant is located in the center, and the dimensions must be 33x33 pixels where each pixel has a constant number of bands." ] }, { "cell_type": "markdown", "id": "3547aec6", - "metadata": {}, + "metadata": { + "id": "3547aec6" + }, "source": [ "## 🚴‍♀️ Steps summary\n", "\n", @@ -133,7 +142,7 @@ " Use the web service to get predictions for new data.\n", "\n", "1. **Visualize predictions**:\n", - " Visualize the preductions on a map.\n", + " Visualize the predictions on a map.\n", "\n", "1. (Optional) **Delete the project** to avoid ongoing costs." ] @@ -141,24 +150,28 @@ { "cell_type": "markdown", "id": "46b21577", - "metadata": {}, + "metadata": { + "id": "46b21577" + }, "source": [ "## 🙈 Using this interactive notebook\n", "\n", "Click the **run** icons ▶️ of each section within this notebook. \n", "\n", - "This notebook code lets you train and deploy an ML model, as well as test its accuracy, from end-to-end. When you run a code cell, the code runs in the notebook's runtime, so you're not making any changes to your personal computer.\n", + "This notebook code lets you train and deploy an ML model from end-to-end. When you run a code cell, the code runs in the notebook's runtime, so you're not making any changes to your personal computer.\n", "\n", "> 🛎️ **To avoid any errors**, wait for each section to finish in their order before clicking the next “run” icon.\n", "\n", "This sample must be connected to a **Google Cloud project**, but nothing else is needed other than your Google Cloud project.\n", - "You can use an existing project and the cost will be less than **$5.00**. Alternatively, you can create a new Cloud project with cloud credits for free" + "You can use an existing project and the cost will be less than **$5.00**. Alternatively, you can create a new Cloud project with cloud credits for free." ] }, { "cell_type": "markdown", "id": "f0033a40", - "metadata": {}, + "metadata": { + "id": "f0033a40" + }, "source": [ "## ✨ Before you begin, you need to…\n", "\n", @@ -185,7 +198,9 @@ { "cell_type": "markdown", "id": "156ab72c", - "metadata": {}, + "metadata": { + "id": "156ab72c" + }, "source": [ "### ⛏️ Preparing the project environment\n", "\n", @@ -197,67 +212,60 @@ }, { "cell_type": "markdown", - "id": "8a363277-2195-42b4-83e7-3e3921bb3db1", - "metadata": {}, + "id": "fa34fdd8", + "metadata": { + "id": "fa34fdd8" + }, "source": [ - "### ↘️ Install the Vertex AI SDK\n", - "\n", - "Note that you will be prompted to restart the runtime after installation." + "### ↘️ Get the code" ] }, { "cell_type": "code", "execution_count": null, - "id": "fc25399d-7d5d-4050-9442-2ef2c7d83e82", - "metadata": {}, + "id": "c2848f6d", + "metadata": { + "id": "c2848f6d" + }, "outputs": [], "source": [ - "!pip3 install --user --upgrade google-cloud-aiplatform" + "# Get the sample source code.\n", + "\n", + "!git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git\n", + "%cd python-docs-samples/people-and-planet-ai/geospatial-classification" ] }, { "cell_type": "code", "execution_count": null, - "id": "63dffaaf-5321-47e7-9dfa-ff82cd0e6303", + "id": "bb8c6ede", "metadata": {}, "outputs": [], "source": [ - "from google.cloud import aiplatform" - ] - }, - { - "cell_type": "markdown", - "id": "fa34fdd8", - "metadata": {}, - "source": [ - "### ↘️ Get the code" - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "id": "c2848f6d", - "metadata": {}, - "outputs": [], - "source": [ - "# Get the sample source code.\n", - "\n", - "## TODO" + "!pip install -r -requirements.txt" ] }, { "cell_type": "markdown", "id": "980b439f-03bb-427f-ae61-4d6d5f066d15", - "metadata": {}, + "metadata": { + "id": "980b439f-03bb-427f-ae61-4d6d5f066d15" + }, "source": [ "### ✏️ Enter your Cloud project's details. Ensure you provide a regional bucket!" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "246702e7", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "246702e7", + "outputId": "69fef0e1-aacc-483b-d44d-8c6847960fa3" + }, "outputs": [], "source": [ "#@title My Google Cloud resources\n", @@ -282,20 +290,12 @@ "!gcloud config set project {project}" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "b96b7201", - "metadata": {}, - "outputs": [], - "source": [ - "aiplatform.init(project=project, staging_bucket=bucket)" - ] - }, { "cell_type": "markdown", "id": "4ec73dd9", - "metadata": {}, + "metadata": { + "id": "4ec73dd9" + }, "source": [ "### 🗺️ Authenticate to Earth Engine\n", "\n", @@ -306,20 +306,30 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "4cb4cd13", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4cb4cd13", + "outputId": "2db4624a-2584-48fa-a098-ca3492458f9a" + }, "outputs": [], "source": [ "import ee\n", - "ee.Authenticate()\n", - "ee.Initialize()" + "import google.auth\n", + "\n", + "credentials, _ = google.auth.default()\n", + "ee.Initialize(credentials, project=project)" ] }, { "cell_type": "markdown", "id": "980b0418", - "metadata": {}, + "metadata": { + "id": "980b0418" + }, "source": [ "# 1. 🛰️ Get the training data\n", "\n", @@ -334,24 +344,11 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "9b0dd3fd", - "metadata": {}, - "outputs": [], - "source": [ - "# Import libraries\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from pprint import pprint\n", - "from datetime import datetime, timedelta" - ] - }, - { - "cell_type": "code", - "execution_count": 109, + "execution_count": 9, "id": "94f60a82", - "metadata": {}, + "metadata": { + "id": "94f60a82" + }, "outputs": [], "source": [ "# Define constants\n", @@ -360,16 +357,15 @@ "IMAGE_COLLECTION = \"COPERNICUS/S2\"\n", "BANDS = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']\n", "SCALE = 10\n", - "\n", - "TRAIN_VALIDATION_SPLIT = 0.7\n", - "\n", "PATCH_SIZE = 16" ] }, { "cell_type": "markdown", "id": "beba81d1", - "metadata": {}, + "metadata": { + "id": "beba81d1" + }, "source": [ "### 🏷️ Import labels\n", "\n", @@ -378,18 +374,25 @@ }, { "cell_type": "code", - "execution_count": 584, + "execution_count": 10, "id": "42190dcf", - "metadata": {}, + "metadata": { + "id": "42190dcf" + }, "outputs": [], "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", "labels_dataframe = pd.read_csv('labeled_geospatial_data.csv')" ] }, { "cell_type": "markdown", "id": "1cdb7d16-a9c7-4be8-b403-ccb10dcad261", - "metadata": {}, + "metadata": { + "id": "1cdb7d16-a9c7-4be8-b403-ccb10dcad261" + }, "source": [ "Each row in this dataframe represents a power plant at a particular timestamp. \n", "\n", @@ -398,45 +401,222 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "fe94b4d3", - "metadata": {}, - "outputs": [], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "fe94b4d3", + "outputId": "3e32c055-b947-45ca-ccce-1b8dfe22969c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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We will do this by creating two new dataframes with a 70/30 training validation split." ] }, { "cell_type": "code", - "execution_count": null, - "id": "05398bb7", - "metadata": {}, + "execution_count": 12, + "id": "e8c5887c", + "metadata": { + "id": "e8c5887c" + }, "outputs": [], "source": [ - "train_dataframe=labels_dataframe.sample(frac=TRAIN_VALIDATION_SPLIT,random_state=200) #random state is a seed value\n", - "validation_dataframe =labels_dataframe.drop(train_dataframe.index).sample(frac=1.0)" + "TRAIN_VALIDATION_SPLIT = 0.7\n", + "\n", + "train_dataframe = labels_dataframe.sample(frac=TRAIN_VALIDATION_SPLIT,random_state=200) #random state is a seed value\n", + "validation_dataframe = labels_dataframe.drop(train_dataframe.index).sample(frac=1.0)" ] }, { "cell_type": "markdown", "id": "96967303", - "metadata": {}, + "metadata": { + "id": "96967303" + }, "source": [ "### Merge 🏷️ labels + 🛰️ Sentinel image data" ] @@ -444,7 +624,9 @@ { "cell_type": "markdown", "id": "fa236af4", - "metadata": {}, + "metadata": { + "id": "fa236af4" + }, "source": [ "In Earth Engine, an [`ImageCollection`](https://developers.google.com/earth-engine/guides/ic_creating) is a stack or sequence of images. An [`Image`](https://developers.google.com/earth-engine/guides/image_overview) is composed of one or more bands and each band has its own name, data type, scale, mask and projection. The [`Sentinel-2`](https://developers.google.com/earth-engine/guides/ic_creating) dataset is represented as an `ImageCollection`, where each image in the collection is of a specific geographic location at a particular time.\n", "\n", @@ -463,11 +645,15 @@ }, { "cell_type": "code", - "execution_count": 659, + "execution_count": 13, "id": "3a8b42dd", - "metadata": {}, + "metadata": { + "id": "3a8b42dd" + }, "outputs": [], "source": [ + "from datetime import datetime, timedelta\n", + "\n", "def labeled_feature(row):\n", " start = datetime.fromisoformat(row.timestamp)\n", " end = start + timedelta(days=1)\n", @@ -490,9 +676,11 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "52762ae1", - "metadata": {}, + "execution_count": 14, + "id": "23a1ad4e", + "metadata": { + "id": "23a1ad4e" + }, "outputs": [], "source": [ "train_features = [labeled_feature(row) for row in train_dataframe.itertuples()]\n", @@ -502,17 +690,50 @@ { "cell_type": "markdown", "id": "21267b05-38cc-4f14-ab09-197baef26713", - "metadata": {}, + "metadata": { + "id": "21267b05-38cc-4f14-ab09-197baef26713" + }, "source": [ - "To get a better sense of what's going on, let's look at the properties for first `Feature` in the `train_features` list. You can see that it contains a property for the label `is_powered_on`, and 13 additional properies, one for each spectral band." + "To get a better sense of what's going on, let's look at the properties for the first `Feature` in the `train_features` list. You can see that it contains a property for the label `is_powered_on`, and 13 additional properies, one for each spectral band." ] }, { "cell_type": "code", - "execution_count": null, - "id": "7f230357", - "metadata": {}, - "outputs": [], + "execution_count": 15, + "id": "994b9f9a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "994b9f9a", + "outputId": "c7d4390e-4155-4ca8-b4cd-c8513008279b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['system:index',\n", + " 'is_powered_on',\n", + " 'B10',\n", + " 'B11',\n", + " 'B12',\n", + " 'B8A',\n", + " 'B1',\n", + " 'B2',\n", + " 'B3',\n", + " 'B4',\n", + " 'B5',\n", + " 'B6',\n", + " 'B7',\n", + " 'B8',\n", + " 'B9']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ee.FeatureCollection(train_features[0]).propertyNames().getInfo()" ] @@ -520,7 +741,9 @@ { "cell_type": "markdown", "id": "8c79c514-ad63-4b85-8da2-ab8967fe8d5a", - "metadata": {}, + "metadata": { + "id": "8c79c514-ad63-4b85-8da2-ab8967fe8d5a" + }, "source": [ "The data contained in each band property is an array of shape 33x33.\n", "\n", @@ -529,21 +752,27 @@ }, { "cell_type": "code", - "execution_count": 595, + "execution_count": 16, "id": "a70c127e-7c45-4eab-914d-17a0c3a9e42c", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a70c127e-7c45-4eab-914d-17a0c3a9e42c", + "outputId": "1942b9a2-bf3f-4d48-9d3f-167197f11dcf" + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[1803 1803 1803 ... 1797 1797 1797]\n", - " [1803 1803 1803 ... 1797 1797 1797]\n", - " [1803 1803 1803 ... 1797 1797 1797]\n", + "[[1390 1390 1390 ... 1650 1650 1650]\n", + " [1390 1390 1390 ... 1650 1650 1650]\n", + " [1307 1307 1307 ... 1655 1655 1655]\n", " ...\n", - " [1519 1519 1519 ... 1560 1560 1560]\n", - " [1519 1519 1519 ... 1560 1560 1560]\n", - " [1519 1519 1519 ... 1560 1560 1560]]\n", + " [1395 1395 1395 ... 1369 1369 1387]\n", + " [1395 1395 1395 ... 1369 1369 1387]\n", + " [1350 1350 1350 ... 1436 1436 1460]]\n", "shape: (33, 33)\n" ] } @@ -557,7 +786,9 @@ { "cell_type": "markdown", "id": "c02607c8", - "metadata": {}, + "metadata": { + "id": "c02607c8" + }, "source": [ "### 💾 Export data" ] @@ -565,25 +796,29 @@ { "cell_type": "markdown", "id": "10c87eb9", - "metadata": {}, + "metadata": { + "id": "10c87eb9" + }, "source": [ - "Lastly, we'll export the data to a Cloud Storage bucket. We'll export the data as [TFRecords](https://www.tensorflow.org/tutorials/load_data/tfrecord) and only export the bands and label properties.\n", + "Lastly, we'll export the data to a Cloud Storage bucket. We'll export the data as [TFRecords](https://www.tensorflow.org/tutorials/load_data/tfrecord).\n", "\n", "Later when we run the training job, we'll parse these TFRecords and feed them to the model." ] }, { "cell_type": "code", - "execution_count": 602, + "execution_count": 20, "id": "53f2d867", - "metadata": {}, + "metadata": { + "id": "53f2d867" + }, "outputs": [], "source": [ "# Export data\n", "\n", "training_task = ee.batch.Export.table.toCloudStorage(\n", " collection=ee.FeatureCollection(train_features),\n", - " description=\"Training image export: test\",\n", + " description=\"Training image export\",\n", " bucket=bucket,\n", " fileNamePrefix=\"geospatial_training\",\n", " selectors=BANDS + [LABEL],\n", @@ -606,25 +841,37 @@ { "cell_type": "markdown", "id": "dadeab16", - "metadata": {}, + "metadata": { + "id": "dadeab16" + }, "source": [ "This export will take around 10 minutes. You can monitor the progress with the following command:" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "4d111251", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4d111251", + "outputId": "50018f0c-146f-421f-de21-6b3fe91bc236" + }, "outputs": [], "source": [ + "from pprint import pprint\n", + "\n", "pprint(ee.batch.Task.list())" ] }, { "cell_type": "markdown", "id": "02b1b9dd", - "metadata": {}, + "metadata": { + "id": "02b1b9dd" + }, "source": [ "# 2. 👟 Run a custom training job\n", "\n", @@ -637,11 +884,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "a4b99054-b843-485e-9cf0-c9f8ae739d1c", - "metadata": {}, + "metadata": { + "id": "a4b99054-b843-485e-9cf0-c9f8ae739d1c" + }, "outputs": [], "source": [ + "from google.cloud import aiplatform\n", + "\n", + "aiplatform.init(project=project, staging_bucket=bucket)\n", + "\n", "job = aiplatform.CustomTrainingJob(\n", " display_name=\"geospatial_model_training\",\n", " script_path=\"task.py\",\n", @@ -651,16 +904,24 @@ { "cell_type": "markdown", "id": "a4baff79-0347-4452-afaa-9c042472f837", - "metadata": {}, + "metadata": { + "id": "a4baff79-0347-4452-afaa-9c042472f837" + }, "source": [ "The job will take around 10 minutes to run." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "5893ce1d-cdbe-43fa-98cc-221a7a2530d3", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5893ce1d-cdbe-43fa-98cc-221a7a2530d3", + "outputId": "ce5f3677-94d6-4bb0-bec5-b4e5c43add95" + }, "outputs": [], "source": [ "model = job.run(args=[f'--bucket={bucket}'])" @@ -669,7 +930,9 @@ { "cell_type": "markdown", "id": "c75499ed", - "metadata": {}, + "metadata": { + "id": "c75499ed" + }, "source": [ "# 3. 💻Deploy a web service to host the trained model" ] @@ -677,7 +940,9 @@ { "cell_type": "markdown", "id": "86cbab39", - "metadata": {}, + "metadata": { + "id": "86cbab39" + }, "source": [ "Next, we use\n", "[Cloud Run](https://cloud.google.com/run)\n", @@ -687,10 +952,7 @@ "\n", "To run the web service, we configure Cloud Run to launch\n", "[`gunicorn`](https://gunicorn.org)\n", - "on the container image we built.\n", - "\n", - "We also provide environment variables with the Google Cloud project ID, a Cloud Storage path, a Compute Engine region, as well as the container image itself.\n", - "These are the Google Cloud resources we want the web service to use when launching new jobs or creating new resources.\n", + "on a container image we'll build.\n", "\n", "Since calls to this web service could launch potentially expensive jobs in our project, we configure it to _only_ accept authenticated calls." ] @@ -698,7 +960,9 @@ { "cell_type": "markdown", "id": "a0e28bff-0804-4093-acf2-739700b24c5f", - "metadata": {}, + "metadata": { + "id": "a0e28bff-0804-4093-acf2-739700b24c5f" + }, "source": [ "## 🏗️ Build container\n", "\n", @@ -707,9 +971,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "c95ba749-b325-4700-ace9-c1c5af3996aa", - "metadata": {}, + "metadata": { + "id": "c95ba749-b325-4700-ace9-c1c5af3996aa" + }, "outputs": [], "source": [ "# Set the Container Registry path for the sample container image.\n", @@ -718,9 +984,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "56c8774f-b90d-47d8-8453-f01e05594751", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "56c8774f-b90d-47d8-8453-f01e05594751", + "outputId": "09ac9d7a-ddba-462f-8b68-77f743a5c011" + }, "outputs": [], "source": [ "# Build and push the container image.\n", @@ -733,16 +1005,24 @@ { "cell_type": "markdown", "id": "9978056f-4467-4dda-85a5-84655ade9a11", - "metadata": {}, + "metadata": { + "id": "9978056f-4467-4dda-85a5-84655ade9a11" + }, "source": [ "## 🐣 Deploy app" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "13c299f1-e108-46bd-8b12-06af56a88444", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "13c299f1-e108-46bd-8b12-06af56a88444", + "outputId": "4556f3b8-b4ff-483e-88c3-8732b7be3e24" + }, "outputs": [], "source": [ "# Deploy the web service to Cloud Run.\n", @@ -759,7 +1039,9 @@ { "cell_type": "markdown", "id": "5a32d4bf-4345-43f7-9371-aa151ad4f521", - "metadata": {}, + "metadata": { + "id": "5a32d4bf-4345-43f7-9371-aa151ad4f521" + }, "source": [ "Now we need the web service URL to make calls to the REST API we just exposed. We can use `gcloud run services describe` to get the web service URL.\n", "\n", @@ -772,9 +1054,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "53f5e5e3-d5dd-4cb7-bb38-5b2e3b439ecd", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "53f5e5e3-d5dd-4cb7-bb38-5b2e3b439ecd", + "outputId": "fb77c7c2-7c41-4494-a8bd-9d724a29fb15" + }, "outputs": [], "source": [ "import subprocess\n", @@ -802,7 +1090,9 @@ { "cell_type": "markdown", "id": "aa2c4dfe-b55d-4f80-a7eb-9ee86542ce59", - "metadata": {}, + "metadata": { + "id": "aa2c4dfe-b55d-4f80-a7eb-9ee86542ce59" + }, "source": [ "Finally, we can test that everything is working.\n", "\n", @@ -831,19 +1121,15 @@ }, { "cell_type": "code", - "execution_count": 557, - "id": "1395bd18-e6eb-47cf-b0ca-7d7d04cce8f0", - "metadata": {}, - "outputs": [], - "source": [ - "import requests" - ] - }, - { - "cell_type": "code", - "execution_count": 558, + "execution_count": 31, "id": "9025db3b-d409-46cf-9ce2-6f0209dfbea3", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9025db3b-d409-46cf-9ce2-6f0209dfbea3", + "outputId": "da02c2f5-c45c-4448-e91c-4b8e429fb4ff" + }, "outputs": [ { "data": { @@ -851,12 +1137,14 @@ "{'args': 'Hello world!', 'response': 'Your request was successful! 🎉'}" ] }, - "execution_count": 558, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import requests\n", + "\n", "requests.post(\n", " url=f'{service_url}/ping',\n", " headers={'Authorization': f'Bearer {identity_token}'},\n", @@ -867,7 +1155,9 @@ { "cell_type": "markdown", "id": "45d64a22-887a-4469-9ba3-668ec23f8667", - "metadata": {}, + "metadata": { + "id": "45d64a22-887a-4469-9ba3-668ec23f8667" + }, "source": [ "# 4.🔮 Get Predictions" ] @@ -875,7 +1165,9 @@ { "cell_type": "markdown", "id": "462b7bf1-cf07-4a93-9cf3-0f9f31c13fa9", - "metadata": {}, + "metadata": { + "id": "462b7bf1-cf07-4a93-9cf3-0f9f31c13fa9" + }, "source": [ "Now that we know our app is up and running, we can use it to make predictions.\n", "\n", @@ -886,23 +1178,17 @@ }, { "cell_type": "code", - "execution_count": 559, - "id": "a7f6b8c2-cab3-4030-8528-9afe644a800d", - "metadata": {}, - "outputs": [], - "source": [ - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": 561, + "execution_count": 32, "id": "9e477289-398c-4bcb-ab23-aee65ac92388", - "metadata": {}, + "metadata": { + "id": "9e477289-398c-4bcb-ab23-aee65ac92388" + }, "outputs": [], "source": [ "# Extract image data\n", "\n", + "import json\n", + "\n", "def get_prediction_data(lon, lat, start, end):\n", " \"\"\"Extracts Sentinel image as json at specific lat/lon and timestamp.\"\"\"\n", "\n", @@ -923,9 +1209,11 @@ }, { "cell_type": "code", - "execution_count": 562, + "execution_count": 33, "id": "86d53ea1-b09e-4afb-8d04-60b73ec59c7c", - "metadata": {}, + "metadata": { + "id": "86d53ea1-b09e-4afb-8d04-60b73ec59c7c" + }, "outputs": [], "source": [ "prediction_data = get_prediction_data(-84.80529, 39.11613, '2021-10-01', '2021-10-31')" @@ -934,29 +1222,50 @@ { "cell_type": "markdown", "id": "9d93aaa8", - "metadata": {}, + "metadata": { + "id": "9d93aaa8" + }, "source": [ "The prediction service **expects two things** the **input data** for the prediction as well as the Cloud Storage **path** where the model is stored." ] }, { "cell_type": "code", - "execution_count": 563, + "execution_count": 46, "id": "5c01631e-5607-45c8-a42e-354fb38a87e1", - "metadata": {}, - "outputs": [], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5c01631e-5607-45c8-a42e-354fb38a87e1", + "outputId": "a35e2c05-0db9-421b-99d4-99310627c03c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'predictions': [[[[0.020780295133590698]]]]}" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "requests.post(\n", " url=f'{service_url}/predict',\n", " headers={'Authorization': f'Bearer {identity_token}'},\n", " json={'data': prediction_data, 'bucket': bucket},\n", - ").json()" + ").json()['predictions']" ] }, { "cell_type": "markdown", "id": "2222aaf9", - "metadata": {}, + "metadata": { + "id": "2222aaf9" + }, "source": [ "# 4. 🗺️ Visualize predictions" ] @@ -964,32 +1273,38 @@ { "cell_type": "markdown", "id": "e70fd0a5-a1ae-4cac-91cc-342809d9fb3b", - "metadata": {}, + "metadata": { + "id": "e70fd0a5-a1ae-4cac-91cc-342809d9fb3b" + }, "source": [ "Let's visualize the results of a power plant in Spain. First, we get predictions for the four towers at this power plant." ] }, { "cell_type": "code", - "execution_count": null, - "id": "b9835c8d", - "metadata": {}, + "execution_count": 35, + "id": "8f6dc24d", + "metadata": { + "id": "8f6dc24d" + }, "outputs": [], "source": [ "def get_prediction(lon, lat, start, end):\n", - " prediction_data = get_prediction_data(lon, lat, start, end)\n", - " result = requests.post(\n", - " url=f'{service_url}/predict',\n", - " headers={'Authorization': f'Bearer {identity_token}'},\n", - " json={'data': prediction_data, 'bucket': bucket},).json()\n", - " return result['predictions']['predictions'][0][0][0][0]" + " prediction_data = get_prediction_data(lon, lat, start, end)\n", + " result = requests.post(\n", + " url=f'{service_url}/predict',\n", + " headers={'Authorization': f'Bearer {identity_token}'},\n", + " json={'data': prediction_data, 'bucket': bucket},).json()\n", + " return result['predictions']['predictions'][0][0][0][0]" ] }, { "cell_type": "code", - "execution_count": 534, + "execution_count": 36, "id": "9fc56972-47bb-4b37-b447-400cbea5e90a", - "metadata": {}, + "metadata": { + "id": "9fc56972-47bb-4b37-b447-400cbea5e90a" + }, "outputs": [], "source": [ "lons = [-7.86444, -7.86376, -7.85755, -7.85587]\n", @@ -998,9 +1313,11 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "316d4190", - "metadata": {}, + "execution_count": 37, + "id": "5230f05e", + "metadata": { + "id": "5230f05e" + }, "outputs": [], "source": [ "plant_predictions = [get_prediction(lon , lat, '2021-10-01', '2021-10-31') for lon, lat in zip(lons, lats)]" @@ -1009,31 +1326,45 @@ { "cell_type": "markdown", "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd", - "metadata": {}, + "metadata": { + "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd" + }, "source": [ - "Next, we can **plot** these points on a map. **Red** means our model predicts that the towers are **\"off\"**, and **blue** means that it predicts the towers are **\"on\"**." + "Next, we can **plot** these points on a map. **Red** means our model predicts that the towers are **\"on\"**, and **blue** means that it predicts the towers are **\"off\"**." ] }, { "cell_type": "code", - "execution_count": 522, - "id": "98651a74", - "metadata": {}, - "outputs": [], + "execution_count": 42, + "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 759 + }, + "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", + "outputId": "6518bf9a-d1da-4a0d-c671-c1719b1b32ca" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import folium\n", "import folium.plugins as folium_plugins\n", - "import branca.colormap as cm" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", - "metadata": {}, - "outputs": [], - "source": [ - "colormap = cm.LinearColormap(colors=['red','lightblue'], index=[0,1], vmin=0, vmax=1)\n", + "import branca.colormap as cm\n", + "\n", + "colormap = cm.LinearColormap(colors=['lightblue', 'red'], index=[0,1], vmin=0, vmax=1)\n", "map = folium.Map([43.45, -7.87], zoom_start=14, tiles='CartoDB positron')\n", "for loc, p in zip(zip(lats, lons), plant_predictions):\n", " folium.Circle(\n", @@ -1051,7 +1382,9 @@ { "cell_type": "markdown", "id": "efcabd53-e81e-4d72-a453-e2fb6dcaa7ae", - "metadata": {}, + "metadata": { + "id": "efcabd53-e81e-4d72-a453-e2fb6dcaa7ae" + }, "source": [ "# 6. 🧹 Clean Up" ] @@ -1059,7 +1392,9 @@ { "cell_type": "markdown", "id": "b002e48a-035c-4369-a161-aa2d800a70b2", - "metadata": {}, + "metadata": { + "id": "b002e48a-035c-4369-a161-aa2d800a70b2" + }, "source": [ "To **avoid incurring charges** to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources." ] @@ -1067,7 +1402,9 @@ { "cell_type": "markdown", "id": "9fc97850-80dd-4c24-80d0-f93825d96e0e", - "metadata": {}, + "metadata": { + "id": "9fc97850-80dd-4c24-80d0-f93825d96e0e" + }, "source": [ "## Deleting the project\n", "\n", @@ -1095,17 +1432,14 @@ "\n", "1. In the dialog, type the project ID, and then click **Shut down** to delete the project." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7c1c51a-ef84-4645-91ae-b997e1643170", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { + "colab": { + "collapsed_sections": [], + "name": "README_colab.ipynb", + "provenance": [] + }, "environment": { "kernel": "python3", "name": "tf2-gpu.2-6.m82", diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 283c2c80d9ca..5c06309f59c3 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -12,19 +12,19 @@ # See the License for the specific language governing permissions and # limitations under the License. +from datetime import datetime, timedelta import logging import os import platform import subprocess import time -from datetime import datetime, timedelta import uuid +import ee from google.cloud import aiplatform from google.cloud import storage -import ee -import pandas as pd import google.auth +import pandas as pd import pytest import requests diff --git a/people-and-planet-ai/geospatial-classification/img/outputs.png b/people-and-planet-ai/geospatial-classification/img/outputs.png deleted file mode 100644 index b7377b7a976141749677ad9b537637f043a502a7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 172207 zcmeGCgL7q1_Xmu|$;7rbv2AB!nRzAj-rT^}$6POvZ`v{7&b!x5v%;Hb+>!|$V&?-iXr z2)We015PwcJv|>a`GdfSRS=_*O+^egcRl*PAvFV=P)E$oX7lqY)AGY|UkWVWJdOTdKGhU);_EjKPBJ!+ zpDK@jvRauoW{Y&rL*4NWbmI|mRmr5+W|J&3Y%SH~{Mu#*j%oFePo1SJTGPOhpy_%p zein=A5gjuuDN>UsXUKPb=ac6#s~Pl5dv zLrKKtH0pl%V_BySE_j=zF0M*%bGkx9Q*#rj;qm4)esBxQ!UKvjKezeB??8Ou!>As0 zRs%%}_^cAl_upOI89+M_DoBvVhgypKOdLF~;{f4q81!8D#Q;Pmkm7|jZZM7QFFwM= zFyKnWgmjQL2dBmc zW$eZFK%yju=@&K$gDn$nk3%s5T@`T|CAbd$kq1LXDE&j31e82LT^KLVs1&|D5FJele 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a/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py b/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py index a128da606af1..2fe78cccf04d 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py @@ -22,7 +22,7 @@ TEST_CONFIG_OVERRIDE = { # You can opt out from the test for specific Python versions. - # ℹ️ We're opting out of all Python versions as a workaround since there + # ℹ️ We're opting out of all Python versions as a workaround since there # are no tests in this directory. This is a temporary fix. "ignored_versions": ["2.7", "3.6", "3.7", "3.8", "3.9", "3.10"], # Old samples are opted out of enforcing Python type hints From 105d587957c44adc7e2b6a42ac3763f877d623b4 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Tue, 11 Jan 2022 11:41:14 -0600 Subject: [PATCH 15/30] small updates --- .../geospatial-classification/README.md | 18 ++++++++++++++++++ .../geospatial-classification/e2e_test.py | 4 ++-- .../geospatial-classification/requirements.txt | 1 + 3 files changed, 21 insertions(+), 2 deletions(-) create mode 100644 people-and-planet-ai/geospatial-classification/README.md diff --git a/people-and-planet-ai/geospatial-classification/README.md b/people-and-planet-ai/geospatial-classification/README.md new file mode 100644 index 000000000000..f58ac540a299 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/README.md @@ -0,0 +1,18 @@ +# 🗺 Coal Plant Predictions -- _geospatial-classification_ + +[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/geospatial-classification/README.ipynb) + +> [Watch the video in YouTube
![thumbnail](http://img.youtube.com/vi/LnEhSVEJUuY/0.jpg)](https://youtu.be/LnEhSVEJUuY) + +This model uses satellite data to predict if a coal plant is turned on and producing carbon emissions. The satellite data comes from [Google Earth Engine.](https://earthengine.google.com/) + +* **Model**: 1D Fully Convolutional Network in [TensorFlow] +* **Creating datasets**: [Sentinel-2] satellite data from [Earth Engine] +* **Training the model**: [TensorFlow] in [Vertex AI] +* **Getting predictions**: [TensorFlow] in [Cloud Run] + +[Cloud Run]: https://cloud.google.com/run +[Sentinel-2]: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 +[Earth Engine]: https://earthengine.google.com/ +[TensorFlow]: https://www.tensorflow.org/ +[Vertex AI]: https://cloud.google.com/vertex-ai diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 5c06309f59c3..6efc2eb79ea4 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -22,8 +22,8 @@ import ee from google.cloud import aiplatform -from google.cloud import storage import google.auth +from google.cloud import storage import pandas as pd import pytest import requests @@ -74,7 +74,7 @@ PATCH_SIZE = 16 -credentials, project = google.auth.default() +credentials, _ = google.auth.default() ee.Initialize(credentials, project=PROJECT) logging.getLogger().setLevel(logging.INFO) diff --git a/people-and-planet-ai/geospatial-classification/requirements.txt b/people-and-planet-ai/geospatial-classification/requirements.txt index 413399c39a79..dce50a3ef4c9 100644 --- a/people-and-planet-ai/geospatial-classification/requirements.txt +++ b/people-and-planet-ai/geospatial-classification/requirements.txt @@ -6,3 +6,4 @@ pytest==6.2.4 pytest-xdist==2.5.0 google-cloud-aiplatform==1.8.0 pandas==1.3.5 +folium==0.8.3 From e8a26187b61a2e8368377219194ef3a7a99c9f77 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 12 Jan 2022 12:24:45 -0600 Subject: [PATCH 16/30] update credentials --- .../geospatial-classification/README.ipynb | 14 +++++++------- .../geospatial-classification/e2e_test.py | 3 ++- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index ab58cbceed71..36f4e7b8705a 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -36,12 +36,12 @@ "id": "3683a5f8" }, "source": [ - "# 🏭 Power Plant ON/OFF: Predictions\n", + "# 🏭 Coal Plant ON/OFF: Predictions\n", "\n", "* **Time estimate**: 1 hour\n", "* **Cost estimate**: less than $**5**.00 USD\n", "\n", - "This is an **interactive** notebook that contains **all** of the **code** necessary to train an ML model from satellite images for a geospatial classification of whether a power plant is on/off. \n", + "This is an **interactive** notebook that contains **all** of the **code** necessary to train an ML model from satellite images for a geospatial classification of whether a coal plant is on/off. \n", "\n", "This is a first step **introductory example** of how these **satellite images** can be used to detect **carbon pollution** from power plants.\n", "\n", @@ -337,7 +337,7 @@ "\n", "1. The satellite images will be extracted from *Earth Engine*.\n", "\n", - "2. The **labels** are provided in a *CSV file* that indicates whether a *power plant* is turned *on or off* at a **particular timestamp**. \n", + "2. The **labels** are provided in a *CSV file* that indicates whether a *coal plant* is turned *on or off* at a **particular timestamp**. \n", "\n", "For each row in the CSV file, we need to extract the corresponding Sentinel image taken at that specific latitude/longitude and timestamp. We'll **export** this image data, along with the corresponding label (on/off), to Cloud Storage." ] @@ -396,7 +396,7 @@ "source": [ "Each row in this dataframe represents a power plant at a particular timestamp. \n", "\n", - "The \"is_powered_on\" column indicates whether or not the power plant was turned **on (1)** or **off (0)** at that timestamp." + "The \"is_powered_on\" column indicates whether or not the coal plant was turned **on (1)** or **off (0)** at that timestamp." ] }, { @@ -1171,7 +1171,7 @@ "source": [ "Now that we know our app is up and running, we can use it to make predictions.\n", "\n", - "Let's start by making a prediction for a particular power plant. To do this we will need to extract the Sentinel data from Earth Engine and send it in the body of the post requst to the prediction service.\n", + "Let's start by making a prediction for a particular coal plant. To do this we will need to extract the Sentinel data from Earth Engine and send it in the body of the post requst to the prediction service.\n", "\n", "We'll start with a plant located at the coordinates -84.80529, 39.11613, and then extract the satellite data from October 2021." ] @@ -1277,7 +1277,7 @@ "id": "e70fd0a5-a1ae-4cac-91cc-342809d9fb3b" }, "source": [ - "Let's visualize the results of a power plant in Spain. First, we get predictions for the four towers at this power plant." + "Let's visualize the results of a coal plant in Spain. First, we get predictions for the four towers at this power plant." ] }, { @@ -1330,7 +1330,7 @@ "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd" }, "source": [ - "Next, we can **plot** these points on a map. **Red** means our model predicts that the towers are **\"on\"**, and **blue** means that it predicts the towers are **\"off\"**." + "Next, we can **plot** these points on a map. **Blue** means our model predicts that the towers are **\"off\"**, and **red** means our model predicts that the towers are **\"on\"** and producing carbon pollution." ] }, { diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 6efc2eb79ea4..b7ea68cc51ea 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -74,7 +74,8 @@ PATCH_SIZE = 16 -credentials, _ = google.auth.default() +#credentials, _ = google.auth.default() +credentials, _ = google.auth.default(scopes=['https://www.googleapis.com/auth/cloud-platform']) ee.Initialize(credentials, project=PROJECT) logging.getLogger().setLevel(logging.INFO) From 3a9082b95c198b37adea96fcea87d037a5496ac5 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 12 Jan 2022 14:27:48 -0600 Subject: [PATCH 17/30] added staging bucket to deploy --- people-and-planet-ai/geospatial-classification/e2e_test.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index b7ea68cc51ea..7602d8830d11 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -172,7 +172,7 @@ def labeled_feature(row): @pytest.fixture(scope="session") -def container_image(): +def container_image(bucket_name): # https://cloud.google.com/sdk/gcloud/reference/builds/submit container_image = f"gcr.io/{PROJECT}/{NAME}:{UUID}" subprocess.check_call( @@ -182,6 +182,7 @@ def container_image(): "submit", "serving_app", f"--tag={container_image}", + f"--gcs-source-staging-dir=gs://{bucket_name}", "--machine-type=e2-highcpu-8", "--timeout=15m", "--quiet", From 117bbe99ed0aa4951873ec9819d3590d34dfea74 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 12 Jan 2022 16:02:16 -0600 Subject: [PATCH 18/30] update staging bucket --- people-and-planet-ai/geospatial-classification/e2e_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 7602d8830d11..f4d0ce3a87a1 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -182,7 +182,7 @@ def container_image(bucket_name): "submit", "serving_app", f"--tag={container_image}", - f"--gcs-source-staging-dir=gs://{bucket_name}", + f"--gcs-source-staging-dir=gs://{bucket_name}/build", "--machine-type=e2-highcpu-8", "--timeout=15m", "--quiet", From e0cb2edf2cd5f1bd0dbf547fd5d836c3f029e734 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 13 Jan 2022 12:14:31 -0600 Subject: [PATCH 19/30] removed serving app noxfile --- .../serving_app/noxfile_config.py | 46 ------------------- 1 file changed, 46 deletions(-) delete mode 100644 people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py diff --git a/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py b/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py deleted file mode 100644 index 2fe78cccf04d..000000000000 --- a/people-and-planet-ai/geospatial-classification/serving_app/noxfile_config.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright 2022 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Default TEST_CONFIG_OVERRIDE for python repos. - -# You can copy this file into your directory, then it will be imported from -# the noxfile.py. - -# The source of truth: -# https://github.com/GoogleCloudPlatform/python-docs-samples/blob/main/noxfile_config.py - -TEST_CONFIG_OVERRIDE = { - # You can opt out from the test for specific Python versions. - # ℹ️ We're opting out of all Python versions as a workaround since there - # are no tests in this directory. This is a temporary fix. - "ignored_versions": ["2.7", "3.6", "3.7", "3.8", "3.9", "3.10"], - # Old samples are opted out of enforcing Python type hints - # All new samples should feature them - # "enforce_type_hints": True, - # An envvar key for determining the project id to use. Change it - # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a - # build specific Cloud project. You can also use your own string - # to use your own Cloud project. - "gcloud_project_env": "GOOGLE_CLOUD_PROJECT", - # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT', - # If you need to use a specific version of pip, - # change pip_version_override to the string representation - # of the version number, for example, "20.2.4" - "pip_version_override": None, - # A dictionary you want to inject into your test. Don't put any - # secrets here. These values will override predefined values. - "envs": { - "PYTEST_ADDOPTS": "-n=8", # parallelize tests in multiple CPUs - }, -} From afcbf5a449c6af5d90d67a2549cfaabb34bd0416 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 13 Jan 2022 12:37:10 -0600 Subject: [PATCH 20/30] clarify use of timestamp in sample --- .../geospatial-classification/README.ipynb | 16 +++++++++++++--- .../geospatial-classification/e2e_test.py | 3 +-- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index 36f4e7b8705a..a9d1956b4af2 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -39,7 +39,7 @@ "# 🏭 Coal Plant ON/OFF: Predictions\n", "\n", "* **Time estimate**: 1 hour\n", - "* **Cost estimate**: less than $**5**.00 USD\n", + "* **Cost estimate**: less than $**1**.00 USD\n", "\n", "This is an **interactive** notebook that contains **all** of the **code** necessary to train an ML model from satellite images for a geospatial classification of whether a coal plant is on/off. \n", "\n", @@ -163,7 +163,7 @@ "> 🛎️ **To avoid any errors**, wait for each section to finish in their order before clicking the next “run” icon.\n", "\n", "This sample must be connected to a **Google Cloud project**, but nothing else is needed other than your Google Cloud project.\n", - "You can use an existing project and the cost will be less than **$5.00**. Alternatively, you can create a new Cloud project with cloud credits for free." + "You can use an existing project and the cost will be less than **$1.00**. Alternatively, you can create a new Cloud project with cloud credits for free." ] }, { @@ -934,7 +934,7 @@ "id": "c75499ed" }, "source": [ - "# 3. 💻Deploy a web service to host the trained model" + "# 3. 💻 Deploy a web service to host the trained model" ] }, { @@ -1207,6 +1207,16 @@ " return feature.getInfo()[\"features\"][0][\"properties\"]" ] }, + { + "cell_type": "markdown", + "id": "4a718e67", + "metadata": {}, + "source": [ + "When we call the `get_prediction_data` function we need to pass in the start and end dates. \n", + "\n", + "Sentinel-2 takes pictures every 10 days. At training time, we knew the exact date of the Sentinel-2 image, as this was provided in the labels CSV file. However, for user supplied images for prediction we don't know the specific date. To address this, we'll extract data for the entire month of October and then use the `mosaic` function in Earth Engine which will grab the earliest image in that range, stitch together images at the seams, and discard the rest." + ] + }, { "cell_type": "code", "execution_count": 33, diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index f4d0ce3a87a1..ee6843a6f786 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -21,8 +21,8 @@ import uuid import ee -from google.cloud import aiplatform import google.auth +from google.cloud import aiplatform from google.cloud import storage import pandas as pd import pytest @@ -74,7 +74,6 @@ PATCH_SIZE = 16 -#credentials, _ = google.auth.default() credentials, _ = google.auth.default(scopes=['https://www.googleapis.com/auth/cloud-platform']) ee.Initialize(credentials, project=PROJECT) From 74b978834f9c76fd9db27f590e7b69b460c1400d Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 13 Jan 2022 13:23:12 -0600 Subject: [PATCH 21/30] fix permissions error --- people-and-planet-ai/geospatial-classification/e2e_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index ee6843a6f786..33ee4129efce 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -109,7 +109,7 @@ def test_data(bucket_name): training_task = ee.batch.Export.table.toCloudStorage( collection=ee.FeatureCollection(train_features), - description="Training image export: test", + description="Training image export", bucket=bucket_name, fileNamePrefix="geospatial_training", selectors=BANDS + [LABEL], @@ -181,7 +181,7 @@ def container_image(bucket_name): "submit", "serving_app", f"--tag={container_image}", - f"--gcs-source-staging-dir=gs://{bucket_name}/build", + #f"--gcs-source-staging-dir=gs://{bucket_name}/build", "--machine-type=e2-highcpu-8", "--timeout=15m", "--quiet", From 9f4dabdc1e463748a3e71b9a484299c6dec3b6b9 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 13 Jan 2022 13:26:00 -0600 Subject: [PATCH 22/30] Added project to gcloud build --- people-and-planet-ai/geospatial-classification/e2e_test.py | 1 + 1 file changed, 1 insertion(+) diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 33ee4129efce..258faf24ff09 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -181,6 +181,7 @@ def container_image(bucket_name): "submit", "serving_app", f"--tag={container_image}", + f"--project={PROJECT}", #f"--gcs-source-staging-dir=gs://{bucket_name}/build", "--machine-type=e2-highcpu-8", "--timeout=15m", From b0136c1ebea0c45c702224bacb365e2a6cb3ed1b Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 13 Jan 2022 15:38:27 -0600 Subject: [PATCH 23/30] add GPU support --- .../geospatial-classification/README.ipynb | 25 ++++++++++++------- .../geospatial-classification/e2e_test.py | 5 ++-- 2 files changed, 18 insertions(+), 12 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index a9d1956b4af2..48a6e2d3a220 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -126,24 +126,29 @@ "\n", "Here's a quick summary of what you’ll go through:\n", "\n", - "1. **Get the training data**:\n", + "1. **Get the training data** _(~15 minutes to complete, no cost for using Earth Engine)_:\n", " Extract satellite images from [Earth Engine](https://earthengine.google.com/), combine it with the data that was labeled and contains lat/long coordinates from [Climate TRACE](https://climatetrace.org) in a CSV, and export to\n", " [Cloud Storage](https://cloud.google.com/storage).\n", "\n", - "1. **Run a custom training job**:\n", - " Using [Tensorflow Keras](https://keras.io/) on [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) using a [pre-built training container](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers).\n", "\n", - "1. **Deploy a web service to host the trained model**:\n", + "1. **Run a custom training job** _(~15 minutes to complete, [costs ~ $1](https://cloud.google.com/vertex-ai/pricing#custom-trained_models))_:\n", + " Using [Tensorflow](https://www.tensorflow.org/) on [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) using a [pre-built training container](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers).\n", + "\n", + "\n", + "1. **Deploy a web service to host the trained model** _(~7 minutes to complete, [costs a few cents to build the image](https://cloud.google.com/build/pricing), and [deployment cost covered by free tier](https://cloud.google.com/run/pricing))_:\n", " On\n", " [Cloud Run](https://cloud.google.com/run)\n", " and get predictions using the model.\n", "\n", - "1. **Get Predictions**:\n", + "\n", + "1. **Get Predictions** _(a few seconds per prediction, [costs covered by free tier](https://cloud.google.com/run/pricing))_:\n", " Use the web service to get predictions for new data.\n", "\n", - "1. **Visualize predictions**:\n", + "\n", + "1. **Visualize predictions** _(~5 minutes to complete)_ :\n", " Visualize the predictions on a map.\n", "\n", + "\n", "1. (Optional) **Delete the project** to avoid ongoing costs." ] }, @@ -879,7 +884,9 @@ "\n", "The complete training code can be found in the `task.py` file.\n", "\n", - "To run our custom training job on Vertex AI Training, we'll use the [pre-built containers](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers) provided by Vertex AI to run our training script.\n" + "To run our custom training job on Vertex AI Training, we'll use the [pre-built containers](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers) provided by Vertex AI to run our training script.\n", + "\n", + "We'll also make use of a GPU. Our model training will only take a couple of minutes, so using a GPU isn't really necessary. But for demonstration purposes (since adding a GPU is simple!) we will make sure we use a container image that is GPU compatible, and then add the `accelerator_type` and `accelerator_count` parameters to `job.run`. TensorFlow will make use of a single GPU out of the box without any extra code changes." ] }, { @@ -898,7 +905,7 @@ "job = aiplatform.CustomTrainingJob(\n", " display_name=\"geospatial_model_training\",\n", " script_path=\"task.py\",\n", - " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest\")" + " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-gpu.2-7:latest\")" ] }, { @@ -924,7 +931,7 @@ }, "outputs": [], "source": [ - "model = job.run(args=[f'--bucket={bucket}'])" + "model = job.run(accelerator_type='NVIDIA_TESLA_K80', accelerator_count=1, args=[f'--bucket={bucket}'])" ] }, { diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index 258faf24ff09..e31fb7820c68 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -182,7 +182,6 @@ def container_image(bucket_name): "serving_app", f"--tag={container_image}", f"--project={PROJECT}", - #f"--gcs-source-staging-dir=gs://{bucket_name}/build", "--machine-type=e2-highcpu-8", "--timeout=15m", "--quiet", @@ -285,10 +284,10 @@ def train_model(bucket_name): job = aiplatform.CustomTrainingJob( display_name="climate_script_colab", script_path="task.py", - container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-7:latest", + container_uri="us-docker.pkg.dev/vertex-ai/training/tf-gpu.2-7:latest", ) - job.run(args=[f"--bucket={bucket_name}"]) + job.run(accelerator_type='NVIDIA_TESLA_K80', accelerator_count=1, args=[f"--bucket={bucket_name}"]) logging.info(f"train_model resource_name: {job.resource_name}") From c4d6f6e7e5593e6c4d5baa833b055ed23d379b64 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 13 Jan 2022 16:18:24 -0600 Subject: [PATCH 24/30] deploy from source code --- .../geospatial-classification/README.ipynb | 62 +++---------------- 1 file changed, 8 insertions(+), 54 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index 48a6e2d3a220..fbf6003d34e3 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -131,25 +131,25 @@ " [Cloud Storage](https://cloud.google.com/storage).\n", "\n", "\n", - "1. **Run a custom training job** _(~15 minutes to complete, [costs ~ $1](https://cloud.google.com/vertex-ai/pricing#custom-trained_models))_:\n", + "2. **Run a custom training job** _(~15 minutes to complete, [costs ~ $1](https://cloud.google.com/vertex-ai/pricing#custom-trained_models))_:\n", " Using [Tensorflow](https://www.tensorflow.org/) on [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) using a [pre-built training container](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers).\n", "\n", "\n", - "1. **Deploy a web service to host the trained model** _(~7 minutes to complete, [costs a few cents to build the image](https://cloud.google.com/build/pricing), and [deployment cost covered by free tier](https://cloud.google.com/run/pricing))_:\n", + "3. **Deploy a web service to host the trained model** _(~7 minutes to complete, [costs a few cents to build the image](https://cloud.google.com/build/pricing), and [deployment cost covered by free tier](https://cloud.google.com/run/pricing))_:\n", " On\n", " [Cloud Run](https://cloud.google.com/run)\n", " and get predictions using the model.\n", "\n", "\n", - "1. **Get Predictions** _(a few seconds per prediction, [costs covered by free tier](https://cloud.google.com/run/pricing))_:\n", + "4. **Get Predictions** _(a few seconds per prediction, [costs covered by free tier](https://cloud.google.com/run/pricing))_:\n", " Use the web service to get predictions for new data.\n", "\n", "\n", - "1. **Visualize predictions** _(~5 minutes to complete)_ :\n", + "5. **Visualize predictions** _(~5 minutes to complete)_ :\n", " Visualize the predictions on a map.\n", "\n", "\n", - "1. (Optional) **Delete the project** to avoid ongoing costs." + "6. (Optional) **Delete the project** to avoid ongoing costs." ] }, { @@ -957,58 +957,13 @@ "[REST API](https://en.wikipedia.org/wiki/Representational_state_transfer) to\n", "get predictions from our trained model.\n", "\n", - "To run the web service, we configure Cloud Run to launch\n", + "We we'll deploy our service to Cloud Run directly from source code so we don't need to build the container image first. Behind the scenes, this command uses Google Cloud buildpacks and Cloud Build to automatically build a container image from our source code in the `serving_app` directory. To run the web service, we configure Cloud Run to launch\n", "[`gunicorn`](https://gunicorn.org)\n", - "on a container image we'll build.\n", + "on this container image. \n", "\n", "Since calls to this web service could launch potentially expensive jobs in our project, we configure it to _only_ accept authenticated calls." ] }, - { - "cell_type": "markdown", - "id": "a0e28bff-0804-4093-acf2-739700b24c5f", - "metadata": { - "id": "a0e28bff-0804-4093-acf2-739700b24c5f" - }, - "source": [ - "## 🏗️ Build container\n", - "\n", - "We use Cloud Build to build the container image. After the image is built, it is available in Container Registry." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "c95ba749-b325-4700-ace9-c1c5af3996aa", - "metadata": { - "id": "c95ba749-b325-4700-ace9-c1c5af3996aa" - }, - "outputs": [], - "source": [ - "# Set the Container Registry path for the sample container image.\n", - "app_container_image = f\"gcr.io/{project}/geospatial-classification/app\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "56c8774f-b90d-47d8-8453-f01e05594751", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "56c8774f-b90d-47d8-8453-f01e05594751", - "outputId": "09ac9d7a-ddba-462f-8b68-77f743a5c011" - }, - "outputs": [], - "source": [ - "# Build and push the container image.\n", - "# https://cloud.google.com/sdk/gcloud/reference/builds/submit\n", - "!gcloud builds submit serving_app \\\n", - " --tag=\"{app_container_image}\" \\\n", - " --machine-type \"e2-highcpu-8\"" - ] - }, { "cell_type": "markdown", "id": "9978056f-4467-4dda-85a5-84655ade9a11", @@ -1035,11 +990,10 @@ "# Deploy the web service to Cloud Run.\n", "# https://cloud.google.com/sdk/gcloud/reference/run/deploy\n", "!gcloud run deploy \"geospatial-service\" \\\n", - " --image=\"{app_container_image}\" \\\n", + " --source=serving_app \\\n", " --command=\"gunicorn\" \\\n", " --args=\"--threads=8,--timeout=0,main:app\" \\\n", " --region=\"us-central1\" \\\n", - " --memory=\"1G\" \\\n", " --no-allow-unauthenticated" ] }, From bd937e5b64b8948b13a6b3080af93bc048f58390 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Tue, 18 Jan 2022 12:03:29 -0600 Subject: [PATCH 25/30] update READMEs --- people-and-planet-ai/README.md | 17 +++++++++++++++++ .../geospatial-classification/README.ipynb | 2 +- .../geospatial-classification/README.md | 4 +--- 3 files changed, 19 insertions(+), 4 deletions(-) diff --git a/people-and-planet-ai/README.md b/people-and-planet-ai/README.md index d8faf5a579ae..fb77962fda6e 100644 --- a/people-and-planet-ai/README.md +++ b/people-and-planet-ai/README.md @@ -40,3 +40,20 @@ location data. [Dataflow]: https://cloud.google.com/dataflow [Keras]: https://keras.io [Vertex AI]: https://cloud.google.com/vertex-ai + +## 🏭 [Coal Plant Predictions -- _geospatial-classification_](geospatial-classification) + +[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/geospatial-classification/README.ipynb) + +This model uses satellite data to predict if a coal plant is turned on and producing carbon emissions. The satellite data comes from [Google Earth Engine.](https://earthengine.google.com/) + +* **Model**: 1D Fully Convolutional Network in [TensorFlow] +* **Creating datasets**: [Sentinel-2] satellite data from [Earth Engine] +* **Training the model**: [TensorFlow] in [Vertex AI] +* **Getting predictions**: [TensorFlow] in [Cloud Run] + +[Cloud Run]: https://cloud.google.com/run +[Sentinel-2]: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 +[Earth Engine]: https://earthengine.google.com/ +[TensorFlow]: https://www.tensorflow.org/ +[Vertex AI]: https://cloud.google.com/vertex-ai diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index fbf6003d34e3..ff6d3180f1af 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -957,7 +957,7 @@ "[REST API](https://en.wikipedia.org/wiki/Representational_state_transfer) to\n", "get predictions from our trained model.\n", "\n", - "We we'll deploy our service to Cloud Run directly from source code so we don't need to build the container image first. Behind the scenes, this command uses Google Cloud buildpacks and Cloud Build to automatically build a container image from our source code in the `serving_app` directory. To run the web service, we configure Cloud Run to launch\n", + "We we'll deploy our service to Cloud Run [directly from source code](https://cloud.google.com/run/docs/deploying-source-code) so we don't need to build the container image first. Behind the scenes, this command uses Google Cloud buildpacks and Cloud Build to automatically build a container image from our source code in the `serving_app` directory. To run the web service, we configure Cloud Run to launch\n", "[`gunicorn`](https://gunicorn.org)\n", "on this container image. \n", "\n", diff --git a/people-and-planet-ai/geospatial-classification/README.md b/people-and-planet-ai/geospatial-classification/README.md index f58ac540a299..938c74edd5eb 100644 --- a/people-and-planet-ai/geospatial-classification/README.md +++ b/people-and-planet-ai/geospatial-classification/README.md @@ -1,9 +1,7 @@ -# 🗺 Coal Plant Predictions -- _geospatial-classification_ +# 🏭 Coal Plant Predictions -- _geospatial-classification_ [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/geospatial-classification/README.ipynb) -> [Watch the video in YouTube
![thumbnail](http://img.youtube.com/vi/LnEhSVEJUuY/0.jpg)](https://youtu.be/LnEhSVEJUuY) - This model uses satellite data to predict if a coal plant is turned on and producing carbon emissions. The satellite data comes from [Google Earth Engine.](https://earthengine.google.com/) * **Model**: 1D Fully Convolutional Network in [TensorFlow] From 3a166e4d1c78811557f67c7b508187c3a23e649f Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 26 Jan 2022 19:03:57 -0600 Subject: [PATCH 26/30] added constrants file --- .../geospatial-classification/README.ipynb | 26 +++++++++++++++---- .../geospatial-classification/constraints.txt | 2 ++ 2 files changed, 23 insertions(+), 5 deletions(-) create mode 100644 people-and-planet-ai/geospatial-classification/constraints.txt diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index ff6d3180f1af..9f8014188a91 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -236,8 +236,8 @@ "source": [ "# Get the sample source code.\n", "\n", - "!git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git\n", - "%cd python-docs-samples/people-and-planet-ai/geospatial-classification" + "!git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git ~/python-docs-samples\n", + "%cd ~/python-docs-samples/people-and-planet-ai/geospatial-classification" ] }, { @@ -247,7 +247,17 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -r -requirements.txt" + "!pip install -r requirements.txt -c constraints.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b86fba8", + "metadata": {}, + "outputs": [], + "source": [ + "%cd ~/python-docs-samples/people-and-planet-ai/geospatial-classification" ] }, { @@ -994,7 +1004,8 @@ " --command=\"gunicorn\" \\\n", " --args=\"--threads=8,--timeout=0,main:app\" \\\n", " --region=\"us-central1\" \\\n", - " --no-allow-unauthenticated" + " --memory=\"1G\"\n", + " --no-allow-unauthenticated \\" ] }, { @@ -1336,7 +1347,12 @@ "import branca.colormap as cm\n", "\n", "colormap = cm.LinearColormap(colors=['lightblue', 'red'], index=[0,1], vmin=0, vmax=1)\n", - "map = folium.Map([43.45, -7.87], zoom_start=14, tiles='CartoDB positron')\n", + "map = folium.Map(\n", + " location=[43.44, -7.86],\n", + " zoom_start=16,\n", + " tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',\n", + " attr = 'ESRI'\n", + ")\n", "for loc, p in zip(zip(lats, lons), plant_predictions):\n", " folium.Circle(\n", " location=loc,\n", diff --git a/people-and-planet-ai/geospatial-classification/constraints.txt b/people-and-planet-ai/geospatial-classification/constraints.txt new file mode 100644 index 000000000000..e36ed6fc8252 --- /dev/null +++ b/people-and-planet-ai/geospatial-classification/constraints.txt @@ -0,0 +1,2 @@ +google-auth==2.5.0 +google-cloud-bigquery==2.32.0 From 5857fe7eec4b10ea6098e2a387ef8c6db25c2bb0 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 26 Jan 2022 19:58:10 -0600 Subject: [PATCH 27/30] added type hints --- .../geospatial-classification/e2e_test.py | 35 +++++++++++++------ .../noxfile_config.py | 2 +- .../serving_app/main.py | 4 +-- .../serving_app/predict.py | 4 +-- .../geospatial-classification/task.py | 14 ++++---- 5 files changed, 36 insertions(+), 23 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/e2e_test.py b/people-and-planet-ai/geospatial-classification/e2e_test.py index e31fb7820c68..5d5e641df291 100644 --- a/people-and-planet-ai/geospatial-classification/e2e_test.py +++ b/people-and-planet-ai/geospatial-classification/e2e_test.py @@ -18,6 +18,7 @@ import platform import subprocess import time +from typing import NamedTuple import uuid import ee @@ -74,14 +75,16 @@ PATCH_SIZE = 16 -credentials, _ = google.auth.default(scopes=['https://www.googleapis.com/auth/cloud-platform']) +credentials, _ = google.auth.default( + scopes=["https://www.googleapis.com/auth/cloud-platform"] +) ee.Initialize(credentials, project=PROJECT) logging.getLogger().setLevel(logging.INFO) @pytest.fixture(scope="session") -def bucket_name(): +def bucket_name() -> str: storage_client = storage.Client() bucket_name = f"{NAME.replace('/', '-')}-{UUID}" @@ -94,7 +97,7 @@ def bucket_name(): @pytest.fixture(scope="session") -def test_data(bucket_name): +def test_data(bucket_name: str) -> str: labels_dataframe = pd.read_csv("labeled_geospatial_data.csv") train_dataframe = labels_dataframe.sample( frac=TRAIN_VALIDATION_SPLIT, random_state=200 @@ -150,7 +153,7 @@ def test_data(bucket_name): yield training_task.name -def labeled_feature(row): +def labeled_feature(row: NamedTuple) -> ee.FeatureCollection: start = datetime.fromisoformat(row.timestamp) end = start + timedelta(days=1) image = ( @@ -171,7 +174,7 @@ def labeled_feature(row): @pytest.fixture(scope="session") -def container_image(bucket_name): +def container_image(bucket_name: str) -> str: # https://cloud.google.com/sdk/gcloud/reference/builds/submit container_image = f"gcr.io/{PROJECT}/{NAME}:{UUID}" subprocess.check_call( @@ -207,7 +210,7 @@ def container_image(bucket_name): @pytest.fixture(scope="session") -def service_url(bucket_name, container_image): +def service_url(bucket_name: str, container_image: str) -> str: # https://cloud.google.com/sdk/gcloud/reference/run/deploy service_name = f"{NAME.replace('/', '-')}-{UUID}" subprocess.check_call( @@ -267,7 +270,7 @@ def service_url(bucket_name, container_image): @pytest.fixture(scope="session") -def identity_token(): +def identity_token() -> str: yield ( subprocess.run( ["gcloud", "auth", "print-identity-token", f"--project={PROJECT}"], @@ -279,7 +282,7 @@ def identity_token(): @pytest.fixture(scope="session") -def train_model(bucket_name): +def train_model(bucket_name: str) -> str: aiplatform.init(project=PROJECT, staging_bucket=bucket_name) job = aiplatform.CustomTrainingJob( display_name="climate_script_colab", @@ -287,7 +290,11 @@ def train_model(bucket_name): container_uri="us-docker.pkg.dev/vertex-ai/training/tf-gpu.2-7:latest", ) - job.run(accelerator_type='NVIDIA_TESLA_K80', accelerator_count=1, args=[f"--bucket={bucket_name}"]) + job.run( + accelerator_type="NVIDIA_TESLA_K80", + accelerator_count=1, + args=[f"--bucket={bucket_name}"], + ) logging.info(f"train_model resource_name: {job.resource_name}") @@ -306,7 +313,7 @@ def train_model(bucket_name): yield job.resource_name -def get_prediction_data(lon, lat, start, end): +def get_prediction_data(lon: float, lat: float, start: str, end: str) -> dict: """Extracts Sentinel image as json at specific lat/lon and timestamp.""" location = ee.Feature(ee.Geometry.Point([lon, lat])) @@ -324,7 +331,13 @@ def get_prediction_data(lon, lat, start, end): return feature.getInfo()["features"][0]["properties"] -def test_predict(bucket_name, test_data, train_model, service_url, identity_token): +def test_predict( + bucket_name: str, + test_data: str, + train_model: str, + service_url: str, + identity_token: str, +) -> None: # Test point prediction_data = get_prediction_data( diff --git a/people-and-planet-ai/geospatial-classification/noxfile_config.py b/people-and-planet-ai/geospatial-classification/noxfile_config.py index b41e2f3352af..fdc5de57de41 100644 --- a/people-and-planet-ai/geospatial-classification/noxfile_config.py +++ b/people-and-planet-ai/geospatial-classification/noxfile_config.py @@ -26,7 +26,7 @@ "ignored_versions": ["2.7", "3.6", "3.7", "3.8", "3.10"], # Old samples are opted out of enforcing Python type hints # All new samples should feature them - # "enforce_type_hints": True, + "enforce_type_hints": True, # An envvar key for determining the project id to use. Change it # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a # build specific Cloud project. You can also use your own string diff --git a/people-and-planet-ai/geospatial-classification/serving_app/main.py b/people-and-planet-ai/geospatial-classification/serving_app/main.py index 15e938c1d298..3d871180157a 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/main.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/main.py @@ -21,7 +21,7 @@ @app.route("/ping", methods=["POST"]) -def run_root(): +def run_root() -> str: args = flask.request.get_json() or {} return { "response": "Your request was successful! 🎉", @@ -30,7 +30,7 @@ def run_root(): @app.route("/predict", methods=["POST"]) -def run_predict(): +def run_predict() -> dict: import predict try: diff --git a/people-and-planet-ai/geospatial-classification/serving_app/predict.py b/people-and-planet-ai/geospatial-classification/serving_app/predict.py index e74315ec1b12..4fc953c0834b 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/predict.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/predict.py @@ -12,13 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Dict, List import numpy as np import tensorflow as tf -def run(data, model_dir): - +def run(data: Dict[str, Any], model_dir: str) -> dict: model = tf.keras.models.load_model(model_dir) prediction_values = np.array(list(data.values())) transposed = np.transpose(prediction_values, (1, 2, 0)) diff --git a/people-and-planet-ai/geospatial-classification/task.py b/people-and-planet-ai/geospatial-classification/task.py index 842da4974741..ca2095a25386 100644 --- a/people-and-planet-ai/geospatial-classification/task.py +++ b/people-and-planet-ai/geospatial-classification/task.py @@ -50,7 +50,7 @@ BATCH_SIZE = 64 -def get_args(): +def get_args() -> dict: """Parses args.""" parser = argparse.ArgumentParser() @@ -59,13 +59,13 @@ def get_args(): return args -def parse_tfrecord(example_proto, features_dict): +def parse_tfrecord(example_proto: bytes, features_dict: dict) -> dict: """Parses a single tf.train.Example.""" return tf.io.parse_single_example(example_proto, features_dict) -def create_features_dict(): +def create_features_dict() -> dict: """Creates dict of features.""" features_dict = { @@ -77,7 +77,7 @@ def create_features_dict(): return features_dict -def get_feature_and_label_vectors(inputs, features_dict): +def get_feature_and_label_vectors(inputs: dict, features_dict: dict) -> tf.Tensor, int: """Formats data.""" label_value = tf.cast(inputs.pop(LABEL), tf.int32) @@ -87,7 +87,7 @@ def get_feature_and_label_vectors(inputs, features_dict): return features_vec, label_value -def create_datasets(bucket): +def create_datasets(bucket: str) -> tf.data.Dataset, tf.data.Dataset: """Creates training and validation datasets.""" train_data_dir = f"gs://{bucket}/geospatial_training.tfrecord.gz" @@ -111,7 +111,7 @@ def create_datasets(bucket): return training_dataset, validation_dataset -def create_model(training_dataset): +def create_model(training_dataset: tf.data.Dataset) -> tf.keras.Model: """Creates model.""" feature_ds = training_dataset.map(lambda x, y: x) @@ -133,7 +133,7 @@ def create_model(training_dataset): return model -def main(): +def main() -> None: args = get_args() training_dataset, validation_dataset = create_datasets(args.bucket) model = create_model(training_dataset) From b032a5be9182e09badcabce4ed80ea196faf4cc4 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Wed, 26 Jan 2022 20:12:31 -0600 Subject: [PATCH 28/30] fix type hint errors --- .../geospatial-classification/serving_app/predict.py | 4 +--- people-and-planet-ai/geospatial-classification/task.py | 7 +++++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/serving_app/predict.py b/people-and-planet-ai/geospatial-classification/serving_app/predict.py index 4fc953c0834b..1fb70d07d9a2 100644 --- a/people-and-planet-ai/geospatial-classification/serving_app/predict.py +++ b/people-and-planet-ai/geospatial-classification/serving_app/predict.py @@ -12,13 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, List - import numpy as np import tensorflow as tf -def run(data: Dict[str, Any], model_dir: str) -> dict: +def run(data: dict, model_dir: str) -> dict: model = tf.keras.models.load_model(model_dir) prediction_values = np.array(list(data.values())) transposed = np.transpose(prediction_values, (1, 2, 0)) diff --git a/people-and-planet-ai/geospatial-classification/task.py b/people-and-planet-ai/geospatial-classification/task.py index ca2095a25386..2dbc4806cafb 100644 --- a/people-and-planet-ai/geospatial-classification/task.py +++ b/people-and-planet-ai/geospatial-classification/task.py @@ -28,6 +28,7 @@ """ import argparse +from typing import Tuple import tensorflow as tf @@ -77,7 +78,9 @@ def create_features_dict() -> dict: return features_dict -def get_feature_and_label_vectors(inputs: dict, features_dict: dict) -> tf.Tensor, int: +def get_feature_and_label_vectors( + inputs: dict, features_dict: dict +) -> Tuple[tf.Tensor, int]: """Formats data.""" label_value = tf.cast(inputs.pop(LABEL), tf.int32) @@ -87,7 +90,7 @@ def get_feature_and_label_vectors(inputs: dict, features_dict: dict) -> tf.Tenso return features_vec, label_value -def create_datasets(bucket: str) -> tf.data.Dataset, tf.data.Dataset: +def create_datasets(bucket: str) -> Tuple[tf.data.Dataset, tf.data.Dataset]: """Creates training and validation datasets.""" train_data_dir = f"gs://{bucket}/geospatial_training.tfrecord.gz" From b0e795d6294a80f4d1a784cf89b03322424dd096 Mon Sep 17 00:00:00 2001 From: nikitamaia Date: Thu, 27 Jan 2022 09:18:05 -0600 Subject: [PATCH 29/30] final updates to notebook --- .../geospatial-classification/README.ipynb | 137 +++++++++--------- 1 file changed, 70 insertions(+), 67 deletions(-) diff --git a/people-and-planet-ai/geospatial-classification/README.ipynb b/people-and-planet-ai/geospatial-classification/README.ipynb index 9f8014188a91..147668a32769 100644 --- a/people-and-planet-ai/geospatial-classification/README.ipynb +++ b/people-and-planet-ai/geospatial-classification/README.ipynb @@ -39,9 +39,9 @@ "# 🏭 Coal Plant ON/OFF: Predictions\n", "\n", "* **Time estimate**: 1 hour\n", - "* **Cost estimate**: less than $**1**.00 USD\n", + "* **Cost estimate**: Around $**1**.00 USD\n", "\n", - "This is an **interactive** notebook that contains **all** of the **code** necessary to train an ML model from satellite images for a geospatial classification of whether a coal plant is on/off. \n", + "This is an **interactive** notebook that contains **all** of the **code** necessary to train an ML model from satellite images for geospatial classification of whether a coal plant is on/off. \n", "\n", "This is a first step **introductory example** of how these **satellite images** can be used to detect **carbon pollution** from power plants.\n", "\n", @@ -104,9 +104,9 @@ "### Model _(function)_\n", "\n", "**TL;DR**\n", - "*The model will receive a patch of pixels, in the center is the power plant tower. We then take 16 pixels as padding creating a 33x33 patch. The model returns a classification of ON/OFF*\n", + "*The model will receive a patch of pixels, in the center is the power plant tower. We take 16 pixels as padding creating a 33x33 patch. The model returns a classification of ON/OFF*\n", "\n", - "In this example, we have a CSV file of labels. Each row in this file represents a power plant at a specific lat/lon and timestamp. At training time we'll prepare a dataset where each input image is a single pixel that we have a label for. We will then add a padding around that image. These padded pixels will not get predictions, but will help our model to make better predictions for the center point that we have a label for.\n", + "In this example, we have a CSV file of labels. Each row in this file represents a power plant at a specific lat/lon and timestamp. At training time we'll prepare a dataset where each input image is a single pixel that we have a label for. We will then add padding around that image. These padded pixels will not get predictions, but will help our model to make better predictions for the center point that we have a label for.\n", "\n", "For example, with a padding of 16, each 1 pixel input point would become a 33x33 image after the padding is added.\n", "\n", @@ -168,7 +168,7 @@ "> 🛎️ **To avoid any errors**, wait for each section to finish in their order before clicking the next “run” icon.\n", "\n", "This sample must be connected to a **Google Cloud project**, but nothing else is needed other than your Google Cloud project.\n", - "You can use an existing project and the cost will be less than **$1.00**. Alternatively, you can create a new Cloud project with cloud credits for free." + "You can use an existing project and the cost will be around **$1.00**. Alternatively, you can create a new Cloud project with cloud credits for free." ] }, { @@ -188,10 +188,6 @@ " > 💡 If you _don't plan to keep the resources_ that you create via this sample, we recommend creating a new project instead of selecting an existing project.\n", " > After you finish these steps, you can delete the project, removing all the resources associated in bulk.\n", "\n", - "1. [_Click here_](https://console.cloud.google.com/flows/enableapi?apiid=dataflow,aiplatform.googleapis.com)\n", - " to **enable the following APIs** in your Google Cloud project:\n", - " _Dataflow_ and _AI Platform_.\n", - "\n", "1. Make sure that **billing is enabled** for your Google Cloud project,\n", " [_click here_](https://cloud.google.com/billing/docs/how-to/modify-project)\n", " to learn how to confirm that billing is enabled.\n", @@ -242,9 +238,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "bb8c6ede", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "bb8c6ede", + "outputId": "3079b40a-9d74-4441-db83-bf6c7bcf58e2" + }, "outputs": [], "source": [ "!pip install -r requirements.txt -c constraints.txt" @@ -254,7 +257,9 @@ "cell_type": "code", "execution_count": null, "id": "3b86fba8", - "metadata": {}, + "metadata": { + "id": "3b86fba8" + }, "outputs": [], "source": [ "%cd ~/python-docs-samples/people-and-planet-ai/geospatial-classification" @@ -272,14 +277,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "246702e7", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "246702e7", - "outputId": "69fef0e1-aacc-483b-d44d-8c6847960fa3" + "outputId": "e37ace27-5c41-4652-899c-975533ec53b1" }, "outputs": [], "source": [ @@ -324,11 +329,7 @@ "execution_count": 2, "id": "4cb4cd13", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4cb4cd13", - "outputId": "2db4624a-2584-48fa-a098-ca3492458f9a" + "id": "4cb4cd13" }, "outputs": [], "source": [ @@ -359,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "94f60a82", "metadata": { "id": "94f60a82" @@ -389,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "42190dcf", "metadata": { "id": "42190dcf" @@ -416,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "id": "fe94b4d3", "metadata": { "colab": { @@ -424,14 +425,14 @@ "height": 206 }, "id": "fe94b4d3", - "outputId": "3e32c055-b947-45ca-ccce-1b8dfe22969c" + "outputId": "d9c2d364-e4a7-46ad-fa8d-ed2e9d8af3e9" }, "outputs": [ { "data": { "text/html": [ "\n", - "
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\n", - " " + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "77c0f36a", + "metadata": { + "id": "77c0f36a", + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n", + "\n", + "# Licensed to the Apache Software Foundation (ASF) under one\n", + "# or more contributor license agreements. See the NOTICE file\n", + "# distributed with this work for additional information\n", + "# regarding copyright ownership. The ASF licenses this file\n", + "# to you under the Apache License, Version 2.0 (the\n", + "# \"License\"); you may not use this file except in compliance\n", + "# with the License. You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing,\n", + "# software distributed under the License is distributed on an\n", + "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n", + "# KIND, either express or implied. See the License for the\n", + "# specific language governing permissions and limitations\n", + "# under the License." + ] + }, + { + "cell_type": "markdown", + "id": "3683a5f8", + "metadata": { + "id": "3683a5f8" + }, + "source": [ + "# 🏭 Coal Plant ON/OFF: Predictions\n", + "\n", + "* **Time estimate**: 1 hour\n", + "* **Cost estimate**: Around $**1**.00 USD\n", + "\n", + "This is an **interactive** notebook that contains **all** of the **code** necessary to train an ML model from satellite images for geospatial classification of whether a coal plant is on/off. \n", + "\n", + "This is a first step **introductory example** of how these **satellite images** can be used to detect **carbon pollution** from power plants.\n", + "\n", + "--------------------------------------------\n", + "\n", + "\n", + "💚 This is one of many **machine learning how-to samples** inspired from **real climate solutions** aired on the [People and Planet AI 🎥 series](https://www.youtube.com/playlist?list=PLIivdWyY5sqI-llB35Dcb187ZG155Rs_7)." + ] + }, + { + "cell_type": "markdown", + "id": "922968ad", + "metadata": { + "id": "922968ad" + }, + "source": [ + "# 🚏 Overview\n", + "\n", + "This notebook leverages geospatial data from [Google Earth Engine](https://earthengine.google.com/), and labeled data provided by the organization [Climate TRACE](https://www.climatetrace.org/). By combining these two data sources, you'll build and train a model that predicts whether or not a power plant is turned on and producing emissions." + ] + }, + { + "cell_type": "markdown", + "id": "8f15adff", + "metadata": { + "id": "8f15adff" + }, + "source": [ + "### 🛰️ Data _(inputs)_\n", + "\n", + "The data in this example consists of images from a satellite called [Sentinel-2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#description), a wide-swath, **high-resolution**, multi-spectral imaging mission for land monitoring studies.\n", + "\n", + "When working with satellite data, each input image has the **dimensions** `[width, height, bands]`. **Bands** are measurements from specific satellite instruments for different ranges of the **electromagnetic spectrum**. For example, Sentinel-2 contains [🌈 13 spectral bands](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#bands). If you're familiar with image classification problems, you can think of the bands as similar to an image's RGB (red, green, blue) channels. However, when working with satellite data we generally have **more than just 3** channels.\n", + "\n", + "![satellite_inputs](https://github.com/nikitamaia/python-docs-samples/blob/geospatial-sandbox/people-and-planet-ai/geospatial-classification/img/inputs.png?raw=1)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d15be296", + "metadata": { + "id": "d15be296" + }, + "source": [ + "### 🏷️ Labels _(outputs)_\n", + "\n", + "For each patch of pixels (an image of a power plant) that we give to the model, it performs **binary classification**, which indicates whether the power plant is on or off.\n", + "\n", + "In this example, the **output** is a single number between *0 (Off) and 1 (On)*, representing the **probability** of that power plant being ON." + ] + }, + { + "cell_type": "markdown", + "id": "4dce50bc", + "metadata": { + "id": "4dce50bc" + }, + "source": [ + "### Model _(function)_\n", + "\n", + "**TL;DR**\n", + "*The model will receive a patch of pixels, in the center is the power plant tower. We take 16 pixels as padding creating a 33x33 patch. The model returns a classification of ON/OFF*\n", + "\n", + "In this example, we have a CSV file of labels. Each row in this file represents a power plant at a specific lat/lon and timestamp. At training time we'll prepare a dataset where each input image is a single pixel that we have a label for. We will then add padding around that image. These padded pixels will not get predictions, but will help our model to make better predictions for the center point that we have a label for.\n", + "\n", + "For example, with a padding of 16, each 1 pixel input point would become a 33x33 image after the padding is added.\n", + "\n", + "![training](https://github.com/nikitamaia/python-docs-samples/blob/geospatial-sandbox/people-and-planet-ai/geospatial-classification/img/training.png?raw=1)\n", + "\n", + "The model in this sample is trained for image patches where a power plant is located in the center, and the dimensions must be 33x33 pixels where each pixel has a constant number of bands." + ] + }, + { + "cell_type": "markdown", + "id": "3547aec6", + "metadata": { + "id": "3547aec6" + }, + "source": [ + "## 🚴‍♀️ Steps summary\n", + "\n", + "Here's a quick summary of what you’ll go through:\n", + "\n", + "1. **Get the training data** _(~15 minutes to complete, no cost for using Earth Engine)_:\n", + " Extract satellite images from [Earth Engine](https://earthengine.google.com/), combine it with the data that was labeled and contains lat/long coordinates from [Climate TRACE](https://climatetrace.org) in a CSV, and export to\n", + " [Cloud Storage](https://cloud.google.com/storage).\n", + "\n", + "\n", + "2. **Run a custom training job** _(~15 minutes to complete, [costs ~ $1](https://cloud.google.com/vertex-ai/pricing#custom-trained_models))_:\n", + " Using [Tensorflow](https://www.tensorflow.org/) on [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) using a [pre-built training container](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers).\n", + "\n", + "\n", + "3. **Deploy a web service to host the trained model** _(~7 minutes to complete, [costs a few cents to build the image](https://cloud.google.com/build/pricing), and [deployment cost covered by free tier](https://cloud.google.com/run/pricing))_:\n", + " On\n", + " [Cloud Run](https://cloud.google.com/run)\n", + " and get predictions using the model.\n", + "\n", + "\n", + "4. **Get Predictions** _(a few seconds per prediction, [costs covered by free tier](https://cloud.google.com/run/pricing))_:\n", + " Use the web service to get predictions for new data.\n", + "\n", + "\n", + "5. **Visualize predictions** _(~5 minutes to complete)_ :\n", + " Visualize the predictions on a map.\n", + "\n", + "\n", + "6. (Optional) **Delete the project** to avoid ongoing costs." + ] + }, + { + "cell_type": "markdown", + "id": "46b21577", + "metadata": { + "id": "46b21577" + }, + "source": [ + "## 🙈 Using this interactive notebook\n", + "\n", + "Click the **run** icons ▶️ of each section within this notebook. \n", + "\n", + "This notebook code lets you train and deploy an ML model from end-to-end. When you run a code cell, the code runs in the notebook's runtime, so you're not making any changes to your personal computer.\n", + "\n", + "> 🛎️ **To avoid any errors**, wait for each section to finish in their order before clicking the next “run” icon.\n", + "\n", + "This sample must be connected to a **Google Cloud project**, but nothing else is needed other than your Google Cloud project.\n", + "You can use an existing project and the cost will be around **$1.00**. Alternatively, you can create a new Cloud project with cloud credits for free." + ] + }, + { + "cell_type": "markdown", + "id": "f0033a40", + "metadata": { + "id": "f0033a40" + }, + "source": [ + "## ✨ Before you begin, you need to…\n", + "\n", + "1. Decide on creating a new\n", + " [free project](https://cloud.google.com/free/docs/gcp-free-tier)\n", + " _(recommended)_ or using an existing one.\n", + " Then **copy the project ID** and paste it in the `google_cloud_project` field in the \"Entering project details” section below.\n", + "\n", + " > 💡 If you _don't plan to keep the resources_ that you create via this sample, we recommend creating a new project instead of selecting an existing project.\n", + " > After you finish these steps, you can delete the project, removing all the resources associated in bulk.\n", + "\n", + "1. Make sure that **billing is enabled** for your Google Cloud project,\n", + " [_click here_](https://cloud.google.com/billing/docs/how-to/modify-project)\n", + " to learn how to confirm that billing is enabled.\n", + "\n", + "1. Have an **Earth Engine** account (it's FREE) or create a new one.\n", + " To create an account, fill out the [registration form here.](https://signup.earthengine.google.com/#!/). Please note this can take from 0-24 hours...but it's worth it! Come back to this sample after you have this." + ] + }, + { + "cell_type": "markdown", + "id": "156ab72c", + "metadata": { + "id": "156ab72c" + }, + "source": [ + "### ⛏️ Preparing the project environment\n", + "\n", + "Click the run ▶️ icons in order for the cells to download and install the necessary code, libraries, and resources for this solution.\n", + "\n", + "> 💡 You can _optionally_ view the entire\n", + "> [code in GitHub](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai/geospatial-classification)." + ] + }, + { + "cell_type": "markdown", + "id": "fa34fdd8", + "metadata": { + "id": "fa34fdd8" + }, + "source": [ + "### ↘️ Get the code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2848f6d", + "metadata": { + "id": "c2848f6d" + }, + "outputs": [], + "source": [ + "# Get the sample source code.\n", + "\n", + "!git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git ~/python-docs-samples\n", + "%cd ~/python-docs-samples/people-and-planet-ai/geospatial-classification" + ] + }, + { + "cell_type": "markdown", + "source": [ + "You will need to restart the runtime (Runtime -> Restart Runtime) after executing this cell." ], - "text/plain": [ - " timestamp lat lon is_powered_on\n", - "0 2020-07-03 16:32:41.397000+00:00 39.11613 -84.80529 1\n", - "1 2018-06-09 16:25:19.280000+00:00 39.11613 -84.80529 1\n", - "2 2017-11-24 16:36:14.460000+00:00 39.11613 -84.80529 0\n", - "3 2019-11-01 16:32:42.327000+00:00 39.11613 -84.80529 0\n", - "4 2020-05-09 16:32:43.614000+00:00 39.11613 -84.80529 1" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "labels_dataframe.head()" - ] - }, - { - "cell_type": "markdown", - "id": "e58c0029", - "metadata": { - "id": "e58c0029" - }, - "source": [ - "### 🎛️ Create train/validation splits" - ] - }, - { - "cell_type": "markdown", - "id": "1cf438ec", - "metadata": { - "id": "1cf438ec" - }, - "source": [ - "Before we can train an ML model, we need to split this data into training and validation datasets. We will do this by creating two new dataframes with a 70/30 training validation split." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e8c5887c", - "metadata": { - "id": "e8c5887c" - }, - "outputs": [], - "source": [ - "TRAIN_VALIDATION_SPLIT = 0.7\n", - "\n", - "train_dataframe = labels_dataframe.sample(frac=TRAIN_VALIDATION_SPLIT,random_state=200) #random state is a seed value\n", - "validation_dataframe = labels_dataframe.drop(train_dataframe.index).sample(frac=1.0)" - ] - }, - { - "cell_type": "markdown", - "id": "96967303", - "metadata": { - "id": "96967303" - }, - "source": [ - "### Merge 🏷️ labels + 🛰️ Sentinel image data" - ] - }, - { - "cell_type": "markdown", - "id": "fa236af4", - "metadata": { - "id": "fa236af4" - }, - "source": [ - "In Earth Engine, an [`ImageCollection`](https://developers.google.com/earth-engine/guides/ic_creating) is a stack or sequence of images. An [`Image`](https://developers.google.com/earth-engine/guides/image_overview) is composed of one or more bands and each band has its own name, data type, scale, mask and projection. The [`Sentinel-2`](https://developers.google.com/earth-engine/guides/ic_creating) dataset is represented as an `ImageCollection`, where each image in the collection is of a specific geographic location at a particular time.\n", - "\n", - "In the cell below, we write a function to extract the Sentinel image taken at the specific latitude/longitude and timestamp for each row of our dataframe.\n", - "\n", - "We will store all of this information as an Earth Engine [`Feature Collection`](https://developers.google.com/earth-engine/apidocs/ee-featurecollection). In Earth Engine, a [`Feature`](https://developers.google.com/earth-engine/guides/features) is an object with a _geometry property_ storing a [`Geometry`](https://developers.google.com/earth-engine/guides/geometries) object, and a _properties property_ storing a dictionary of other properties. Groups of related `Features` can be combined into a `FeatureCollection` to enable additional operations on the entire set such as filtering, sorting, and rendering. \n", - "\n", - "We first filter the Sentinel-2 `ImageCollection` at the start/end dates for a particular row in our dataframe.\n", - "\n", - "Then, using the [`neighorboodToArray`](https://developers.google.com/earth-engine/api_docs#eeimageneighborhoodtoarray) method we create a `FeatureCollection` that contains the satellite data for each band at the latitude and longitude of interest as well as a 16 pixel padding around that point.\n", - "\n", - "In the image below you can think of the purple box representing the lat/lon where the power plant is located. And around this pixel, we add the padding.\n", - "\n", - "![training](img/training.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3a8b42dd", - "metadata": { - "id": "3a8b42dd" - }, - "outputs": [], - "source": [ - "from datetime import datetime, timedelta\n", - "\n", - "def labeled_feature(row):\n", - " start = datetime.fromisoformat(row.timestamp)\n", - " end = start + timedelta(days=1)\n", - " image = (\n", - " ee.ImageCollection(IMAGE_COLLECTION)\n", - " .filterDate(start.strftime(\"%Y-%m-%d\"), end.strftime(\"%Y-%m-%d\"))\n", - " .select(BANDS)\n", - " .mosaic()\n", - " )\n", - " point = ee.Feature(\n", - " ee.Geometry.Point([row.lon, row.lat]),\n", - " {LABEL: row.is_powered_on},\n", - " )\n", - " return (\n", - " image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE))\n", - " .sampleRegions(ee.FeatureCollection([point]), scale=SCALE)\n", - " .first()\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "23a1ad4e", - "metadata": { - "id": "23a1ad4e" - }, - "outputs": [], - "source": [ - "train_features = [labeled_feature(row) for row in train_dataframe.itertuples()]\n", - "validation_features = [labeled_feature(row) for row in validation_dataframe.itertuples()]" - ] - }, - { - "cell_type": "markdown", - "id": "21267b05-38cc-4f14-ab09-197baef26713", - "metadata": { - "id": "21267b05-38cc-4f14-ab09-197baef26713" - }, - "source": [ - "To get a better sense of what's going on, let's look at the properties for the first `Feature` in the `train_features` list. You can see that it contains a property for the label `is_powered_on`, and 13 additional properies, one for each spectral band." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "994b9f9a", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "994b9f9a", - "outputId": "3dc76f9b-f447-469e-c9ab-26297c0edc6c" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['system:index',\n", - " 'is_powered_on',\n", - " 'B10',\n", - " 'B11',\n", - " 'B12',\n", - " 'B8A',\n", - " 'B1',\n", - " 'B2',\n", - " 'B3',\n", - " 'B4',\n", - " 'B5',\n", - " 'B6',\n", - " 'B7',\n", - " 'B8',\n", - " 'B9']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ee.FeatureCollection(train_features[0]).propertyNames().getInfo()" - ] - }, - { - "cell_type": "markdown", - "id": "8c79c514-ad63-4b85-8da2-ab8967fe8d5a", - "metadata": { - "id": "8c79c514-ad63-4b85-8da2-ab8967fe8d5a" - }, - "source": [ - "The data contained in each band property is an array of shape 33x33.\n", - "\n", - "For example, here is the data for band B1 in the first element in our list expressed as a numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a70c127e-7c45-4eab-914d-17a0c3a9e42c", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "a70c127e-7c45-4eab-914d-17a0c3a9e42c", - "outputId": "04d79e2b-cfe4-4760-a33c-b073bd07c82e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1390 1390 1390 ... 1650 1650 1650]\n", - " [1390 1390 1390 ... 1650 1650 1650]\n", - " [1307 1307 1307 ... 1655 1655 1655]\n", - " ...\n", - " [1395 1395 1395 ... 1369 1369 1387]\n", - " [1395 1395 1395 ... 1369 1369 1387]\n", - " [1350 1350 1350 ... 1436 1436 1460]]\n", - "shape: (33, 33)\n" - ] - } - ], - "source": [ - "example_feature = np.array(train_features[0].get('B1').getInfo())\n", - "print(example_feature)\n", - "print('shape: ' + str(example_feature.shape))" - ] - }, - { - "cell_type": "markdown", - "id": "c02607c8", - "metadata": { - "id": "c02607c8" - }, - "source": [ - "### 💾 Export data" - ] - }, - { - "cell_type": "markdown", - "id": "10c87eb9", - "metadata": { - "id": "10c87eb9" - }, - "source": [ - "Lastly, we'll export the data to a Cloud Storage bucket. We'll export the data as [TFRecords](https://www.tensorflow.org/tutorials/load_data/tfrecord).\n", - "\n", - "Later when we run the training job, we'll parse these TFRecords and feed them to the model." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "53f2d867", - "metadata": { - "id": "53f2d867" - }, - "outputs": [], - "source": [ - "# Export data\n", - "\n", - "training_task = ee.batch.Export.table.toCloudStorage(\n", - " collection=ee.FeatureCollection(train_features),\n", - " description=\"Training image export\",\n", - " bucket=bucket,\n", - " fileNamePrefix=\"geospatial_training\",\n", - " selectors=BANDS + [LABEL],\n", - " fileFormat=\"TFRecord\",\n", - ")\n", - "\n", - "training_task.start()\n", - "\n", - "validation_task = ee.batch.Export.table.toCloudStorage(\n", - " collection=ee.FeatureCollection(validation_features),\n", - " description=\"Validation image export\",\n", - " bucket=bucket,\n", - " fileNamePrefix=\"geospatial_validation\",\n", - " selectors= BANDS + [LABEL],\n", - " fileFormat='TFRecord')\n", - "\n", - "validation_task.start()" - ] - }, - { - "cell_type": "markdown", - "id": "dadeab16", - "metadata": { - "id": "dadeab16" - }, - "source": [ - "This export will take around 10 minutes. You can monitor the progress with the following command:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4d111251", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4d111251", - "outputId": "3be30292-4c39-45e1-eb01-776649ea167e" - }, - "outputs": [], - "source": [ - "from pprint import pprint\n", - "\n", - "pprint(ee.batch.Task.list())" - ] - }, - { - "cell_type": "markdown", - "id": "02b1b9dd", - "metadata": { - "id": "02b1b9dd" - }, - "source": [ - "# 2. 👟 Run a custom training job\n", - "\n", - "Once the export jobs have finished, we're **ready to use** that data to train a model on Vertex AI Training.\n", - "\n", - "The complete training code can be found in the `task.py` file.\n", - "\n", - "To run our custom training job on Vertex AI Training, we'll use the [pre-built containers](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers) provided by Vertex AI to run our training script.\n", - "\n", - "We'll also make use of a GPU. Our model training will only take a couple of minutes, so using a GPU isn't really necessary. But for demonstration purposes (since adding a GPU is simple!) we will make sure we use a container image that is GPU compatible, and then add the `accelerator_type` and `accelerator_count` parameters to `job.run`. TensorFlow will make use of a single GPU out of the box without any extra code changes." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a4b99054-b843-485e-9cf0-c9f8ae739d1c", - "metadata": { - "id": "a4b99054-b843-485e-9cf0-c9f8ae739d1c" - }, - "outputs": [], - "source": [ - "from google.cloud import aiplatform\n", - "\n", - "aiplatform.init(project=project, staging_bucket=bucket)\n", - "\n", - "job = aiplatform.CustomTrainingJob(\n", - " display_name=\"geospatial_model_training\",\n", - " script_path=\"task.py\",\n", - " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-gpu.2-7:latest\")" - ] - }, - { - "cell_type": "markdown", - "id": "a4baff79-0347-4452-afaa-9c042472f837", - "metadata": { - "id": "a4baff79-0347-4452-afaa-9c042472f837" - }, - "source": [ - "The job will take around 10 minutes to run." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5893ce1d-cdbe-43fa-98cc-221a7a2530d3", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5893ce1d-cdbe-43fa-98cc-221a7a2530d3", - "outputId": "ae9e192e-95ad-4f3e-9a4f-05d92c1ff53e" - }, - "outputs": [], - "source": [ - "model = job.run(accelerator_type='NVIDIA_TESLA_K80', accelerator_count=1, args=[f'--bucket={bucket}'])" - ] - }, - { - "cell_type": "markdown", - "id": "c75499ed", - "metadata": { - "id": "c75499ed" - }, - "source": [ - "# 3. 💻 Deploy a web service to host the trained model" - ] - }, - { - "cell_type": "markdown", - "id": "86cbab39", - "metadata": { - "id": "86cbab39" - }, - "source": [ - "Next, we use\n", - "[Cloud Run](https://cloud.google.com/run)\n", - "to deploy a web service that exposes a\n", - "[REST API](https://en.wikipedia.org/wiki/Representational_state_transfer) to\n", - "get predictions from our trained model.\n", - "\n", - "We'll deploy our service to Cloud Run [directly from source code](https://cloud.google.com/run/docs/deploying-source-code) so we don't need to build the container image first. Behind the scenes, this command uses Google Cloud buildpacks and Cloud Build to automatically build a container image from our source code in the `serving_app` directory. To run the web service, we configure Cloud Run to launch\n", - "[`gunicorn`](https://gunicorn.org)\n", - "on this container image. \n", - "\n", - "Since calls to this web service could launch potentially expensive jobs in our project, we configure it to _only_ accept authenticated calls." - ] - }, - { - "cell_type": "markdown", - "id": "9978056f-4467-4dda-85a5-84655ade9a11", - "metadata": { - "id": "9978056f-4467-4dda-85a5-84655ade9a11" - }, - "source": [ - "## 🐣 Deploy app" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "13c299f1-e108-46bd-8b12-06af56a88444", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "13c299f1-e108-46bd-8b12-06af56a88444", - "outputId": "ed394adf-ffaf-4dd8-fc3f-529d88b49666" - }, - "outputs": [], - "source": [ - "# Deploy the web service to Cloud Run.\n", - "# https://cloud.google.com/sdk/gcloud/reference/run/deploy\n", - "!gcloud run deploy \"geospatial-service\" \\\n", - " --source=serving_app \\\n", - " --command=\"gunicorn\" \\\n", - " --args=\"--threads=8,--timeout=0,main:app\" \\\n", - " --region=\"us-central1\" \\\n", - " --memory=\"1G\" \\\n", - " --no-allow-unauthenticated \\" - ] - }, - { - "cell_type": "markdown", - "id": "5a32d4bf-4345-43f7-9371-aa151ad4f521", - "metadata": { - "id": "5a32d4bf-4345-43f7-9371-aa151ad4f521" - }, - "source": [ - "Now we need the web service URL to make calls to the REST API we just exposed. We can use `gcloud run services describe` to get the web service URL.\n", - "\n", - "Since we only accept authorized calls in our web service, we also need to authenticate each call.\n", - "`gcloud` is already authenticated, so we can use `gcloud auth print-identity-token` to get quick access.\n", - "\n", - "> ℹ️ For more information on how to do authenticated calls in Cloud Run, see the\n", - "> [Authentication overview](https://cloud.google.com/run/docs/authenticating/overview) page." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "53f5e5e3-d5dd-4cb7-bb38-5b2e3b439ecd", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "53f5e5e3-d5dd-4cb7-bb38-5b2e3b439ecd", - "outputId": "823137dd-d561-4ad2-8edf-96f69f246d2d" - }, - "outputs": [], - "source": [ - "import subprocess\n", - "\n", - "# Get the web service URL.\n", - "# https://cloud.google.com/sdk/gcloud/reference/run/services/describe\n", - "service_url = subprocess.run(\n", - " [ 'gcloud', 'run', 'services', 'describe', 'geospatial-service',\n", - " f'--region={region}',\n", - " f'--format=get(status.url)',\n", - " ],\n", - " capture_output=True,\n", - ").stdout.decode('utf-8').strip()\n", - "print(f\"service_url: {service_url}\")\n", - "\n", - "# Get an identity token for authorized calls to our web service.\n", - "# https://cloud.google.com/sdk/gcloud/reference/auth/print-identity-token\n", - "identity_token = subprocess.run(\n", - " ['gcloud', 'auth', 'print-identity-token'],\n", - " capture_output=True,\n", - ").stdout.decode('utf-8').strip()\n", - "print(f\"identity_token: {identity_token}\")" - ] - }, - { - "cell_type": "markdown", - "id": "aa2c4dfe-b55d-4f80-a7eb-9ee86542ce59", - "metadata": { - "id": "aa2c4dfe-b55d-4f80-a7eb-9ee86542ce59" - }, - "source": [ - "Finally, we can test that everything is working.\n", - "\n", - "We included a `ping` method in our web service just to *make sure everything is working* as expected.\n", - "It simply returns back the arguments we passed to the call, as well as a response saying that the call was successful.\n", - "\n", - "> 🛎️ This is a convenient way to make sure the web service is reachable, the authentication is working as expected, and the request arguments are passed correctly.\n", - "\n", - "We can use Python's\n", - "[`requests`](https://docs.python-requests.org)\n", - "library.\n", - "The web service was built to always accept [JSON](https://www.w3schools.com/whatis/whatis_json.asp)-encoded requests, and returns JSON-encoded responses.\n", - "\n", - "For a **request to be successful**, it **must**:\n", - "\n", - "* Be an [`HTTP POST`](https://developer.mozilla.org/en-US/docs/Web/HTTP/Methods/POST) request\n", - "* Contain the following **headers**:\n", - " * `Authorization: Bearer IDENTITY_TOKEN`\n", - " * `Content-Type: application/json`\n", - "* The data must be **valid JSON**, if *no arguments* are needed we can pass `{}` as an **empty object**.\n", - "\n", - "For **ease of use**, `requests.post` has a\n", - "[`json` parameter](https://docs.python-requests.org/en/master/user/quickstart/#more-complicated-post-requests)\n", - "that **automatically attaches the header** `Content-Type: application/json` and encodes our data into a JSON string." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "9025db3b-d409-46cf-9ce2-6f0209dfbea3", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "metadata": { + "id": "0-SFNGO63asJ" + }, + "id": "0-SFNGO63asJ" }, - "id": "9025db3b-d409-46cf-9ce2-6f0209dfbea3", - "outputId": "977aa6d1-59f6-4dbf-80c7-c9725d43dddc" - }, - "outputs": [ { - "data": { - "text/plain": [ - "{'args': 'Hello world!', 'response': 'Your request was successful! 🎉'}" + "cell_type": "code", + "execution_count": null, + "id": "bb8c6ede", + "metadata": { + "id": "bb8c6ede" + }, + "outputs": [], + "source": [ + "!pip install -r requirements.txt -c constraints.txt" ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import requests\n", - "\n", - "requests.post(\n", - " url=f'{service_url}/ping',\n", - " headers={'Authorization': f'Bearer {identity_token}'},\n", - " json={'x': 42, 'message': 'Hello world!'},\n", - ").json()" - ] - }, - { - "cell_type": "markdown", - "id": "45d64a22-887a-4469-9ba3-668ec23f8667", - "metadata": { - "id": "45d64a22-887a-4469-9ba3-668ec23f8667" - }, - "source": [ - "# 4.🔮 Get Predictions" - ] - }, - { - "cell_type": "markdown", - "id": "462b7bf1-cf07-4a93-9cf3-0f9f31c13fa9", - "metadata": { - "id": "462b7bf1-cf07-4a93-9cf3-0f9f31c13fa9" - }, - "source": [ - "Now that we know our app is up and running, we can use it to make predictions.\n", - "\n", - "Let's start by making a prediction for a particular coal plant. To do this we will need to extract the Sentinel data from Earth Engine and send it in the body of the post requst to the prediction service.\n", - "\n", - "We'll start with a plant located at the coordinates -84.80529, 39.11613, and then extract the satellite data from October 2021." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "9e477289-398c-4bcb-ab23-aee65ac92388", - "metadata": { - "id": "9e477289-398c-4bcb-ab23-aee65ac92388" - }, - "outputs": [], - "source": [ - "# Extract image data\n", - "\n", - "import json\n", - "\n", - "def get_prediction_data(lon, lat, start, end):\n", - " \"\"\"Extracts Sentinel image as json at specific lat/lon and timestamp.\"\"\"\n", - "\n", - " location = ee.Feature(ee.Geometry.Point([lon, lat]))\n", - " image = (\n", - " ee.ImageCollection(IMAGE_COLLECTION)\n", - " .filterDate(start, end)\n", - " .select(BANDS)\n", - " .mosaic()\n", - " )\n", - "\n", - " feature = image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE)).sampleRegions(\n", - " collection=ee.FeatureCollection([location]), scale=SCALE\n", - " )\n", - "\n", - " return feature.getInfo()[\"features\"][0][\"properties\"]" - ] - }, - { - "cell_type": "markdown", - "id": "4a718e67", - "metadata": { - "id": "4a718e67" - }, - "source": [ - "When we call the `get_prediction_data` function we need to pass in the start and end dates. \n", - "\n", - "Sentinel-2 takes pictures every 10 days. At training time, we knew the exact date of the Sentinel-2 image, as this was provided in the labels CSV file. However, for user supplied images for prediction we don't know the specific date the image was taken. To address this, we'll extract data for the entire month of October and then use the `mosaic` function in Earth Engine which will grab the earliest image in that range, stitch together images at the seams, and discard the rest." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "86d53ea1-b09e-4afb-8d04-60b73ec59c7c", - "metadata": { - "id": "86d53ea1-b09e-4afb-8d04-60b73ec59c7c" - }, - "outputs": [], - "source": [ - "prediction_data = get_prediction_data(-84.80529, 39.11613, '2021-10-01', '2021-10-31')" - ] - }, - { - "cell_type": "markdown", - "id": "9d93aaa8", - "metadata": { - "id": "9d93aaa8" - }, - "source": [ - "The prediction service **expects two things** the **input data** for the prediction as well as the Cloud Storage **path** where the model is stored." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5c01631e-5607-45c8-a42e-354fb38a87e1", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "5c01631e-5607-45c8-a42e-354fb38a87e1", - "outputId": "92fd906e-af86-4e1c-f60c-35e84a9831fe" - }, - "outputs": [ { - "data": { - "text/plain": [ - "{'predictions': [[[[1.0]]]]}" + "cell_type": "markdown", + "id": "980b439f-03bb-427f-ae61-4d6d5f066d15", + "metadata": { + "id": "980b439f-03bb-427f-ae61-4d6d5f066d15" + }, + "source": [ + "### ✏️ Enter your Cloud project's details. Ensure you provide a regional bucket!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "246702e7", + "metadata": { + "id": "246702e7", + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title My Google Cloud resources\n", + "project = '' #@param {type:\"string\"}\n", + "bucket = '' #@param {type:\"string\"}\n", + "region = 'us-central1' #@param {type:\"string\"}\n", + "\n", + "# Validate the inputs.\n", + "if not project:\n", + " raise ValueError(f\"Please provide a value for 'project'\")\n", + "if not bucket:\n", + " raise ValueError(f\"Please provide a value for 'bucket'\")\n", + "if not region:\n", + " raise ValueError(f\"Please provide a value for 'region'\")\n", + "\n", + "# Authenticate\n", + "from google.colab import auth\n", + "\n", + "auth.authenticate_user()\n", + "print('Authenticated')\n", + "\n", + "!gcloud config set project {project}\n", + "\n", + "%cd ~/python-docs-samples/people-and-planet-ai/geospatial-classification" + ] + }, + { + "cell_type": "markdown", + "id": "4ec73dd9", + "metadata": { + "id": "4ec73dd9" + }, + "source": [ + "### 🗺️ Authenticate to Earth Engine\n", + "\n", + "In order to use the Earth Engine API, you'll **need** to have an **Earth Engine account**.\n", + "\n", + "To create an account, fill out the [registration form here.](https://signup.earthengine.google.com/#!/)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cb4cd13", + "metadata": { + "id": "4cb4cd13" + }, + "outputs": [], + "source": [ + "import ee\n", + "import google.auth\n", + "\n", + "credentials, _ = google.auth.default()\n", + "ee.Initialize(credentials, project=project)" + ] + }, + { + "cell_type": "markdown", + "id": "980b0418", + "metadata": { + "id": "980b0418" + }, + "source": [ + "# 1. 🛰️ Get the training data\n", + "\n", + "The training data in this sample comes from **two places**: \n", + "\n", + "1. The satellite images will be extracted from *Earth Engine*.\n", + "\n", + "2. The **labels** are provided in a *CSV file* that indicates whether a *coal plant* is turned *on or off* at a **particular timestamp**. \n", + "\n", + "For each row in the CSV file, we need to extract the corresponding Sentinel image taken at that specific latitude/longitude and timestamp. We'll **export** this image data, along with the corresponding label (on/off), to Cloud Storage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94f60a82", + "metadata": { + "id": "94f60a82" + }, + "outputs": [], + "source": [ + "# Define constants\n", + "\n", + "LABEL = 'is_powered_on'\n", + "IMAGE_COLLECTION = \"COPERNICUS/S2\"\n", + "BANDS = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']\n", + "SCALE = 10\n", + "PATCH_SIZE = 16" + ] + }, + { + "cell_type": "markdown", + "id": "beba81d1", + "metadata": { + "id": "beba81d1" + }, + "source": [ + "### 🏷️ Import labels\n", + "\n", + "First, we **import** the **CSV** file that contains the labels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42190dcf", + "metadata": { + "id": "42190dcf" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "labels_dataframe = pd.read_csv('labeled_geospatial_data.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "1cdb7d16-a9c7-4be8-b403-ccb10dcad261", + "metadata": { + "id": "1cdb7d16-a9c7-4be8-b403-ccb10dcad261" + }, + "source": [ + "Each row in this dataframe represents a power plant at a particular timestamp. \n", + "\n", + "The \"is_powered_on\" column indicates whether or not the coal plant was turned **on (1)** or **off (0)** at that timestamp." ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "requests.post(\n", - " url=f'{service_url}/predict',\n", - " headers={'Authorization': f'Bearer {identity_token}'},\n", - " json={'data': prediction_data, 'bucket': bucket},\n", - ").json()['predictions']" - ] - }, - { - "cell_type": "markdown", - "id": "2222aaf9", - "metadata": { - "id": "2222aaf9" - }, - "source": [ - "# 4. 🗺️ Visualize predictions" - ] - }, - { - "cell_type": "markdown", - "id": "e70fd0a5-a1ae-4cac-91cc-342809d9fb3b", - "metadata": { - "id": "e70fd0a5-a1ae-4cac-91cc-342809d9fb3b" - }, - "source": [ - "Let's visualize the results of a coal plant in Spain. First, we get predictions for the four towers at this power plant." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "8f6dc24d", - "metadata": { - "id": "8f6dc24d" - }, - "outputs": [], - "source": [ - "def get_prediction(lon, lat, start, end):\n", - " prediction_data = get_prediction_data(lon, lat, start, end)\n", - " result = requests.post(\n", - " url=f'{service_url}/predict',\n", - " headers={'Authorization': f'Bearer {identity_token}'},\n", - " json={'data': prediction_data, 'bucket': bucket},).json()\n", - " return result['predictions']['predictions'][0][0][0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "9fc56972-47bb-4b37-b447-400cbea5e90a", - "metadata": { - "id": "9fc56972-47bb-4b37-b447-400cbea5e90a" - }, - "outputs": [], - "source": [ - "lons = [-7.86444, -7.86376, -7.85755, -7.85587]\n", - "lats = [43.43717, 43.43827, 43.44075, 43.44114]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5230f05e", - "metadata": { - "id": "5230f05e" - }, - "outputs": [], - "source": [ - "plant_predictions = [get_prediction(lon , lat, '2021-10-01', '2021-10-31') for lon, lat in zip(lons, lats)]" - ] - }, - { - "cell_type": "markdown", - "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd", - "metadata": { - "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd" - }, - "source": [ - "Next, we can **plot** these points on a map. **Blue** means our model predicts that the towers are **\"off\"**, and **red** means our model predicts that the towers are **\"on\"** and producing carbon pollution." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 771 }, - "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", - "outputId": "76e05dd2-3bae-433f-9f34-4df229f7620e" - }, - "outputs": [ { - "data": { - "text/html": [ - "
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We will do this by creating two new dataframes with a 70/30 training validation split." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8c5887c", + "metadata": { + "id": "e8c5887c" + }, + "outputs": [], + "source": [ + "TRAIN_VALIDATION_SPLIT = 0.7\n", + "\n", + "train_dataframe = labels_dataframe.sample(frac=TRAIN_VALIDATION_SPLIT,random_state=200) #random state is a seed value\n", + "validation_dataframe = labels_dataframe.drop(train_dataframe.index).sample(frac=1.0)" + ] + }, + { + "cell_type": "markdown", + "id": "96967303", + "metadata": { + "id": "96967303" + }, + "source": [ + "### Merge 🏷️ labels + 🛰️ Sentinel image data" + ] + }, + { + "cell_type": "markdown", + "id": "fa236af4", + "metadata": { + "id": "fa236af4" + }, + "source": [ + "In Earth Engine, an [`ImageCollection`](https://developers.google.com/earth-engine/guides/ic_creating) is a stack or sequence of images. An [`Image`](https://developers.google.com/earth-engine/guides/image_overview) is composed of one or more bands and each band has its own name, data type, scale, mask and projection. The [`Sentinel-2`](https://developers.google.com/earth-engine/guides/ic_creating) dataset is represented as an `ImageCollection`, where each image in the collection is of a specific geographic location at a particular time.\n", + "\n", + "In the cell below, we write a function to extract the Sentinel image taken at the specific latitude/longitude and timestamp for each row of our dataframe.\n", + "\n", + "We will store all of this information as an Earth Engine [`Feature Collection`](https://developers.google.com/earth-engine/apidocs/ee-featurecollection). In Earth Engine, a [`Feature`](https://developers.google.com/earth-engine/guides/features) is an object with a _geometry property_ storing a [`Geometry`](https://developers.google.com/earth-engine/guides/geometries) object, and a _properties property_ storing a dictionary of other properties. Groups of related `Features` can be combined into a `FeatureCollection` to enable additional operations on the entire set such as filtering, sorting, and rendering. \n", + "\n", + "We first filter the Sentinel-2 `ImageCollection` at the start/end dates for a particular row in our dataframe.\n", + "\n", + "Then, using the [`neighorboodToArray`](https://developers.google.com/earth-engine/api_docs#eeimageneighborhoodtoarray) method we create a `FeatureCollection` that contains the satellite data for each band at the latitude and longitude of interest as well as a 16 pixel padding around that point.\n", + "\n", + "In the image below you can think of the purple box representing the lat/lon where the power plant is located. And around this pixel, we add the padding.\n", + "\n", + "![training](https://github.com/nikitamaia/python-docs-samples/blob/geospatial-sandbox/people-and-planet-ai/geospatial-classification/img/training.png?raw=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a8b42dd", + "metadata": { + "id": "3a8b42dd" + }, + "outputs": [], + "source": [ + "from datetime import datetime, timedelta\n", + "\n", + "def labeled_feature(row):\n", + " start = datetime.fromisoformat(row.timestamp)\n", + " end = start + timedelta(days=1)\n", + " image = (\n", + " ee.ImageCollection(IMAGE_COLLECTION)\n", + " .filterDate(start.strftime(\"%Y-%m-%d\"), end.strftime(\"%Y-%m-%d\"))\n", + " .select(BANDS)\n", + " .mosaic()\n", + " )\n", + " point = ee.Feature(\n", + " ee.Geometry.Point([row.lon, row.lat]),\n", + " {LABEL: row.is_powered_on},\n", + " )\n", + " return (\n", + " image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE))\n", + " .sampleRegions(ee.FeatureCollection([point]), scale=SCALE)\n", + " .first()\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23a1ad4e", + "metadata": { + "id": "23a1ad4e" + }, + "outputs": [], + "source": [ + "train_features = [labeled_feature(row) for row in train_dataframe.itertuples()]\n", + "validation_features = [labeled_feature(row) for row in validation_dataframe.itertuples()]" + ] + }, + { + "cell_type": "markdown", + "id": "21267b05-38cc-4f14-ab09-197baef26713", + "metadata": { + "id": "21267b05-38cc-4f14-ab09-197baef26713" + }, + "source": [ + "To get a better sense of what's going on, let's look at the properties for the first `Feature` in the `train_features` list. You can see that it contains a property for the label `is_powered_on`, and 13 additional properies, one for each spectral band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "994b9f9a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "994b9f9a", + "outputId": "3dc76f9b-f447-469e-c9ab-26297c0edc6c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['system:index',\n", + " 'is_powered_on',\n", + " 'B10',\n", + " 'B11',\n", + " 'B12',\n", + " 'B8A',\n", + " 'B1',\n", + " 'B2',\n", + " 'B3',\n", + " 'B4',\n", + " 'B5',\n", + " 'B6',\n", + " 'B7',\n", + " 'B8',\n", + " 'B9']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ee.FeatureCollection(train_features[0]).propertyNames().getInfo()" + ] + }, + { + "cell_type": "markdown", + "id": "8c79c514-ad63-4b85-8da2-ab8967fe8d5a", + "metadata": { + "id": "8c79c514-ad63-4b85-8da2-ab8967fe8d5a" + }, + "source": [ + "The data contained in each band property is an array of shape 33x33.\n", + "\n", + "For example, here is the data for band B1 in the first element in our list expressed as a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a70c127e-7c45-4eab-914d-17a0c3a9e42c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a70c127e-7c45-4eab-914d-17a0c3a9e42c", + "outputId": "04d79e2b-cfe4-4760-a33c-b073bd07c82e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1390 1390 1390 ... 1650 1650 1650]\n", + " [1390 1390 1390 ... 1650 1650 1650]\n", + " [1307 1307 1307 ... 1655 1655 1655]\n", + " ...\n", + " [1395 1395 1395 ... 1369 1369 1387]\n", + " [1395 1395 1395 ... 1369 1369 1387]\n", + " [1350 1350 1350 ... 1436 1436 1460]]\n", + "shape: (33, 33)\n" + ] + } + ], + "source": [ + "example_feature = np.array(train_features[0].get('B1').getInfo())\n", + "print(example_feature)\n", + "print('shape: ' + str(example_feature.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "c02607c8", + "metadata": { + "id": "c02607c8" + }, + "source": [ + "### 💾 Export data" + ] + }, + { + "cell_type": "markdown", + "id": "10c87eb9", + "metadata": { + "id": "10c87eb9" + }, + "source": [ + "Lastly, we'll export the data to a Cloud Storage bucket. We'll export the data as [TFRecords](https://www.tensorflow.org/tutorials/load_data/tfrecord).\n", + "\n", + "Later when we run the training job, we'll parse these TFRecords and feed them to the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53f2d867", + "metadata": { + "id": "53f2d867" + }, + "outputs": [], + "source": [ + "# Export data\n", + "\n", + "training_task = ee.batch.Export.table.toCloudStorage(\n", + " collection=ee.FeatureCollection(train_features),\n", + " description=\"Training image export\",\n", + " bucket=bucket,\n", + " fileNamePrefix=\"geospatial_training\",\n", + " selectors=BANDS + [LABEL],\n", + " fileFormat=\"TFRecord\",\n", + ")\n", + "\n", + "training_task.start()\n", + "\n", + "validation_task = ee.batch.Export.table.toCloudStorage(\n", + " collection=ee.FeatureCollection(validation_features),\n", + " description=\"Validation image export\",\n", + " bucket=bucket,\n", + " fileNamePrefix=\"geospatial_validation\",\n", + " selectors= BANDS + [LABEL],\n", + " fileFormat='TFRecord')\n", + "\n", + "validation_task.start()" + ] + }, + { + "cell_type": "markdown", + "id": "dadeab16", + "metadata": { + "id": "dadeab16" + }, + "source": [ + "This export will take around 10 minutes. You can monitor the progress with the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d111251", + "metadata": { + "id": "4d111251" + }, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "\n", + "pprint(ee.batch.Task.list())" + ] + }, + { + "cell_type": "markdown", + "id": "02b1b9dd", + "metadata": { + "id": "02b1b9dd" + }, + "source": [ + "# 2. 👟 Run a custom training job\n", + "\n", + "Once the export jobs have finished, we're **ready to use** that data to train a model on Vertex AI Training.\n", + "\n", + "The complete training code can be found in the `task.py` file.\n", + "\n", + "To run our custom training job on Vertex AI Training, we'll use the [pre-built containers](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers) provided by Vertex AI to run our training script.\n", + "\n", + "We'll also make use of a GPU. Our model training will only take a couple of minutes, so using a GPU isn't really necessary. But for demonstration purposes (since adding a GPU is simple!) we will make sure we use a container image that is GPU compatible, and then add the `accelerator_type` and `accelerator_count` parameters to `job.run`. TensorFlow will make use of a single GPU out of the box without any extra code changes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4b99054-b843-485e-9cf0-c9f8ae739d1c", + "metadata": { + "id": "a4b99054-b843-485e-9cf0-c9f8ae739d1c" + }, + "outputs": [], + "source": [ + "from google.cloud import aiplatform\n", + "\n", + "aiplatform.init(project=project, staging_bucket=bucket)\n", + "\n", + "job = aiplatform.CustomTrainingJob(\n", + " display_name=\"geospatial_model_training\",\n", + " script_path=\"task.py\",\n", + " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-gpu.2-7:latest\")" + ] + }, + { + "cell_type": "markdown", + "id": "a4baff79-0347-4452-afaa-9c042472f837", + "metadata": { + "id": "a4baff79-0347-4452-afaa-9c042472f837" + }, + "source": [ + "The job will take around 10 minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5893ce1d-cdbe-43fa-98cc-221a7a2530d3", + "metadata": { + "id": "5893ce1d-cdbe-43fa-98cc-221a7a2530d3" + }, + "outputs": [], + "source": [ + "model = job.run(accelerator_type='NVIDIA_TESLA_K80', accelerator_count=1, args=[f'--bucket={bucket}'])" + ] + }, + { + "cell_type": "markdown", + "id": "c75499ed", + "metadata": { + "id": "c75499ed" + }, + "source": [ + "# 3. 💻 Deploy a web service to host the trained model" + ] + }, + { + "cell_type": "markdown", + "id": "86cbab39", + "metadata": { + "id": "86cbab39" + }, + "source": [ + "Next, we use\n", + "[Cloud Run](https://cloud.google.com/run)\n", + "to deploy a web service that exposes a\n", + "[REST API](https://en.wikipedia.org/wiki/Representational_state_transfer) to\n", + "get predictions from our trained model.\n", + "\n", + "We'll deploy our service to Cloud Run [directly from source code](https://cloud.google.com/run/docs/deploying-source-code) so we don't need to build the container image first. Behind the scenes, this command uses Google Cloud buildpacks and Cloud Build to automatically build a container image from our source code in the `serving_app` directory. To run the web service, we configure Cloud Run to launch\n", + "[`gunicorn`](https://gunicorn.org)\n", + "on this container image. \n", + "\n", + "Since calls to this web service could launch potentially expensive jobs in our project, we configure it to _only_ accept authenticated calls." + ] + }, + { + "cell_type": "markdown", + "id": "9978056f-4467-4dda-85a5-84655ade9a11", + "metadata": { + "id": "9978056f-4467-4dda-85a5-84655ade9a11" + }, + "source": [ + "## 🐣 Deploy app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13c299f1-e108-46bd-8b12-06af56a88444", + "metadata": { + "id": "13c299f1-e108-46bd-8b12-06af56a88444" + }, + "outputs": [], + "source": [ + "# Deploy the web service to Cloud Run.\n", + "# https://cloud.google.com/sdk/gcloud/reference/run/deploy\n", + "!gcloud run deploy \"geospatial-service\" \\\n", + " --source=serving_app \\\n", + " --command=\"gunicorn\" \\\n", + " --args=\"--threads=8,--timeout=0,main:app\" \\\n", + " --region=\"us-central1\" \\\n", + " --memory=\"1G\" \\\n", + " --no-allow-unauthenticated \\" + ] + }, + { + "cell_type": "markdown", + "id": "5a32d4bf-4345-43f7-9371-aa151ad4f521", + "metadata": { + "id": "5a32d4bf-4345-43f7-9371-aa151ad4f521" + }, + "source": [ + "Now we need the web service URL to make calls to the REST API we just exposed. We can use `gcloud run services describe` to get the web service URL.\n", + "\n", + "Since we only accept authorized calls in our web service, we also need to authenticate each call.\n", + "`gcloud` is already authenticated, so we can use `gcloud auth print-identity-token` to get quick access.\n", + "\n", + "> ℹ️ For more information on how to do authenticated calls in Cloud Run, see the\n", + "> [Authentication overview](https://cloud.google.com/run/docs/authenticating/overview) page." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53f5e5e3-d5dd-4cb7-bb38-5b2e3b439ecd", + "metadata": { + "id": "53f5e5e3-d5dd-4cb7-bb38-5b2e3b439ecd" + }, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "# Get the web service URL.\n", + "# https://cloud.google.com/sdk/gcloud/reference/run/services/describe\n", + "service_url = subprocess.run(\n", + " [ 'gcloud', 'run', 'services', 'describe', 'geospatial-service',\n", + " f'--region={region}',\n", + " f'--format=get(status.url)',\n", + " ],\n", + " capture_output=True,\n", + ").stdout.decode('utf-8').strip()\n", + "print(f\"service_url: {service_url}\")\n", + "\n", + "# Get an identity token for authorized calls to our web service.\n", + "# https://cloud.google.com/sdk/gcloud/reference/auth/print-identity-token\n", + "identity_token = subprocess.run(\n", + " ['gcloud', 'auth', 'print-identity-token'],\n", + " capture_output=True,\n", + ").stdout.decode('utf-8').strip()\n", + "print(f\"identity_token: {identity_token}\")" + ] + }, + { + "cell_type": "markdown", + "id": "aa2c4dfe-b55d-4f80-a7eb-9ee86542ce59", + "metadata": { + "id": "aa2c4dfe-b55d-4f80-a7eb-9ee86542ce59" + }, + "source": [ + "Finally, we can test that everything is working.\n", + "\n", + "We included a `ping` method in our web service just to *make sure everything is working* as expected.\n", + "It simply returns back the arguments we passed to the call, as well as a response saying that the call was successful.\n", + "\n", + "> 🛎️ This is a convenient way to make sure the web service is reachable, the authentication is working as expected, and the request arguments are passed correctly.\n", + "\n", + "We can use Python's\n", + "[`requests`](https://docs.python-requests.org)\n", + "library.\n", + "The web service was built to always accept [JSON](https://www.w3schools.com/whatis/whatis_json.asp)-encoded requests, and returns JSON-encoded responses.\n", + "\n", + "For a **request to be successful**, it **must**:\n", + "\n", + "* Be an [`HTTP POST`](https://developer.mozilla.org/en-US/docs/Web/HTTP/Methods/POST) request\n", + "* Contain the following **headers**:\n", + " * `Authorization: Bearer IDENTITY_TOKEN`\n", + " * `Content-Type: application/json`\n", + "* The data must be **valid JSON**, if *no arguments* are needed we can pass `{}` as an **empty object**.\n", + "\n", + "For **ease of use**, `requests.post` has a\n", + "[`json` parameter](https://docs.python-requests.org/en/master/user/quickstart/#more-complicated-post-requests)\n", + "that **automatically attaches the header** `Content-Type: application/json` and encodes our data into a JSON string." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9025db3b-d409-46cf-9ce2-6f0209dfbea3", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9025db3b-d409-46cf-9ce2-6f0209dfbea3", + "outputId": "977aa6d1-59f6-4dbf-80c7-c9725d43dddc" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'args': 'Hello world!', 'response': 'Your request was successful! 🎉'}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "\n", + "requests.post(\n", + " url=f'{service_url}/ping',\n", + " headers={'Authorization': f'Bearer {identity_token}'},\n", + " json={'x': 42, 'message': 'Hello world!'},\n", + ").json()" + ] + }, + { + "cell_type": "markdown", + "id": "45d64a22-887a-4469-9ba3-668ec23f8667", + "metadata": { + "id": "45d64a22-887a-4469-9ba3-668ec23f8667" + }, + "source": [ + "# 4.🔮 Get Predictions" + ] + }, + { + "cell_type": "markdown", + "id": "462b7bf1-cf07-4a93-9cf3-0f9f31c13fa9", + "metadata": { + "id": "462b7bf1-cf07-4a93-9cf3-0f9f31c13fa9" + }, + "source": [ + "Now that we know our app is up and running, we can use it to make predictions.\n", + "\n", + "Let's start by making a prediction for a particular coal plant. To do this we will need to extract the Sentinel data from Earth Engine and send it in the body of the post requst to the prediction service.\n", + "\n", + "We'll start with a plant located at the coordinates -84.80529, 39.11613, and then extract the satellite data from October 2021." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e477289-398c-4bcb-ab23-aee65ac92388", + "metadata": { + "id": "9e477289-398c-4bcb-ab23-aee65ac92388" + }, + "outputs": [], + "source": [ + "# Extract image data\n", + "\n", + "import json\n", + "\n", + "def get_prediction_data(lon, lat, start, end):\n", + " \"\"\"Extracts Sentinel image as json at specific lat/lon and timestamp.\"\"\"\n", + "\n", + " location = ee.Feature(ee.Geometry.Point([lon, lat]))\n", + " image = (\n", + " ee.ImageCollection(IMAGE_COLLECTION)\n", + " .filterDate(start, end)\n", + " .select(BANDS)\n", + " .mosaic()\n", + " )\n", + "\n", + " feature = image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE)).sampleRegions(\n", + " collection=ee.FeatureCollection([location]), scale=SCALE\n", + " )\n", + "\n", + " return feature.getInfo()[\"features\"][0][\"properties\"]" + ] + }, + { + "cell_type": "markdown", + "id": "4a718e67", + "metadata": { + "id": "4a718e67" + }, + "source": [ + "When we call the `get_prediction_data` function we need to pass in the start and end dates. \n", + "\n", + "Sentinel-2 takes pictures every 10 days. At training time, we knew the exact date of the Sentinel-2 image, as this was provided in the labels CSV file. However, for user supplied images for prediction we don't know the specific date the image was taken. To address this, we'll extract data for the entire month of October and then use the `mosaic` function in Earth Engine which will grab the earliest image in that range, stitch together images at the seams, and discard the rest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86d53ea1-b09e-4afb-8d04-60b73ec59c7c", + "metadata": { + "id": "86d53ea1-b09e-4afb-8d04-60b73ec59c7c" + }, + "outputs": [], + "source": [ + "prediction_data = get_prediction_data(-84.80529, 39.11613, '2021-10-01', '2021-10-31')" + ] + }, + { + "cell_type": "markdown", + "id": "9d93aaa8", + "metadata": { + "id": "9d93aaa8" + }, + "source": [ + "The prediction service **expects two things** the **input data** for the prediction as well as the Cloud Storage **path** where the model is stored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c01631e-5607-45c8-a42e-354fb38a87e1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5c01631e-5607-45c8-a42e-354fb38a87e1", + "outputId": "92fd906e-af86-4e1c-f60c-35e84a9831fe" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'predictions': [[[[1.0]]]]}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "requests.post(\n", + " url=f'{service_url}/predict',\n", + " headers={'Authorization': f'Bearer {identity_token}'},\n", + " json={'data': prediction_data, 'bucket': bucket},\n", + ").json()['predictions']" + ] + }, + { + "cell_type": "markdown", + "id": "2222aaf9", + "metadata": { + "id": "2222aaf9" + }, + "source": [ + "# 4. 🗺️ Visualize predictions" + ] + }, + { + "cell_type": "markdown", + "id": "e70fd0a5-a1ae-4cac-91cc-342809d9fb3b", + "metadata": { + "id": "e70fd0a5-a1ae-4cac-91cc-342809d9fb3b" + }, + "source": [ + "Let's visualize the results of a coal plant in Spain. First, we get predictions for the four towers at this power plant." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f6dc24d", + "metadata": { + "id": "8f6dc24d" + }, + "outputs": [], + "source": [ + "def get_prediction(lon, lat, start, end):\n", + " prediction_data = get_prediction_data(lon, lat, start, end)\n", + " result = requests.post(\n", + " url=f'{service_url}/predict',\n", + " headers={'Authorization': f'Bearer {identity_token}'},\n", + " json={'data': prediction_data, 'bucket': bucket},).json()\n", + " return result['predictions']['predictions'][0][0][0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fc56972-47bb-4b37-b447-400cbea5e90a", + "metadata": { + "id": "9fc56972-47bb-4b37-b447-400cbea5e90a" + }, + "outputs": [], + "source": [ + "lons = [-7.86444, -7.86376, -7.85755, -7.85587]\n", + "lats = [43.43717, 43.43827, 43.44075, 43.44114]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5230f05e", + "metadata": { + "id": "5230f05e" + }, + "outputs": [], + "source": [ + "plant_predictions = [get_prediction(lon , lat, '2021-10-01', '2021-10-31') for lon, lat in zip(lons, lats)]" + ] + }, + { + "cell_type": "markdown", + "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd", + "metadata": { + "id": "be593a27-71b1-4ea1-8c2e-100a0a82ccdd" + }, + "source": [ + "Next, we can **plot** these points on a map. **Blue** means our model predicts that the towers are **\"off\"**, and **red** means our model predicts that the towers are **\"on\"** and producing carbon pollution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 771 + }, + "id": "15ba038b-49c9-40bf-8d87-af7d253d270d", + "outputId": "76e05dd2-3bae-433f-9f34-4df229f7620e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import folium\n", + "import folium.plugins as folium_plugins\n", + "import branca.colormap as cm\n", + "\n", + "colormap = cm.LinearColormap(colors=['lightblue', 'red'], index=[0,1], vmin=0, vmax=1)\n", + "map = folium.Map(\n", + " location=[43.44, -7.86],\n", + " zoom_start=16,\n", + " tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',\n", + " attr = 'ESRI'\n", + ")\n", + "for loc, p in zip(zip(lats, lons), plant_predictions):\n", + " folium.Circle(\n", + " location=loc,\n", + " radius=20,\n", + " fill=True,\n", + " color=colormap(p),\n", + " ).add_to(map)\n", + "\n", + "map.add_child(colormap)\n", + "\n", + "display(map)" + ] + }, + { + "cell_type": "markdown", + "id": "efcabd53-e81e-4d72-a453-e2fb6dcaa7ae", + "metadata": { + "id": "efcabd53-e81e-4d72-a453-e2fb6dcaa7ae" + }, + "source": [ + "# 6. 🧹 Clean Up" + ] + }, + { + "cell_type": "markdown", + "id": "b002e48a-035c-4369-a161-aa2d800a70b2", + "metadata": { + "id": "b002e48a-035c-4369-a161-aa2d800a70b2" + }, + "source": [ + "To **avoid incurring charges** to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources." + ] + }, + { + "cell_type": "markdown", + "id": "9fc97850-80dd-4c24-80d0-f93825d96e0e", + "metadata": { + "id": "9fc97850-80dd-4c24-80d0-f93825d96e0e" + }, + "source": [ + "## Deleting the project\n", + "\n", + "The **easiest** way to **eliminate billing** is to delete the project that you created for the tutorial.\n", + "\n", + "To delete the project:\n", + "\n", + "> ⚠️ Deleting a project has the following effects:\n", + ">\n", + "> * **Everything in the project is deleted.** If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.\n", + ">\n", + "> * **Custom project IDs are lost.** When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.\n", + ">\n", + "> If you plan to explore multiple tutorials and quickstarts, **reusing** projects can help you avoid exceeding project **quota limits**.\n", + "\n", + "1. In the Cloud Console, go to the **Manage resources** page.\n", + "\n", + " \n", + "\n", + "1. In the project list, select the project that you want to delete, and then click **Delete**.\n", + "\n", + "1. In the dialog, type the project ID, and then click **Shut down** to delete the project." ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "import folium\n", - "import folium.plugins as folium_plugins\n", - "import branca.colormap as cm\n", - "\n", - "colormap = cm.LinearColormap(colors=['lightblue', 'red'], index=[0,1], vmin=0, vmax=1)\n", - "map = folium.Map(\n", - " location=[43.44, -7.86],\n", - " zoom_start=16,\n", - " tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',\n", - " attr = 'ESRI'\n", - ")\n", - "for loc, p in zip(zip(lats, lons), plant_predictions):\n", - " folium.Circle(\n", - " location=loc,\n", - " radius=20,\n", - " fill=True,\n", - " color=colormap(p),\n", - " ).add_to(map)\n", - "\n", - "map.add_child(colormap)\n", - "\n", - "display(map)" - ] - }, - { - "cell_type": "markdown", - "id": "efcabd53-e81e-4d72-a453-e2fb6dcaa7ae", - "metadata": { - "id": "efcabd53-e81e-4d72-a453-e2fb6dcaa7ae" - }, - "source": [ - "# 6. 🧹 Clean Up" - ] - }, - { - "cell_type": "markdown", - "id": "b002e48a-035c-4369-a161-aa2d800a70b2", - "metadata": { - "id": "b002e48a-035c-4369-a161-aa2d800a70b2" - }, - "source": [ - "To **avoid incurring charges** to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources." - ] - }, - { - "cell_type": "markdown", - "id": "9fc97850-80dd-4c24-80d0-f93825d96e0e", - "metadata": { - "id": "9fc97850-80dd-4c24-80d0-f93825d96e0e" - }, - "source": [ - "## Deleting the project\n", - "\n", - "The **easiest** way to **eliminate billing** is to delete the project that you created for the tutorial.\n", - "\n", - "To delete the project:\n", - "\n", - "> ⚠️ Deleting a project has the following effects:\n", - ">\n", - "> * **Everything in the project is deleted.** If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.\n", - ">\n", - "> * **Custom project IDs are lost.** When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.\n", - ">\n", - "> If you plan to explore multiple tutorials and quickstarts, **reusing** projects can help you avoid exceeding project **quota limits**.\n", - "\n", - "1. In the Cloud Console, go to the **Manage resources** page.\n", - "\n", - " \n", - "\n", - "1. In the project list, select the project that you want to delete, and then click **Delete**.\n", - "\n", - "1. In the dialog, type the project ID, and then click **Shut down** to delete the project." - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "README.ipynb", - "provenance": [] - }, - "environment": { - "kernel": "python3", - "name": "tf2-gpu.2-6.m82", - "type": "gcloud", - "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m82" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "README.ipynb", + "provenance": [], + "toc_visible": true + }, + "environment": { + "kernel": "python3", + "name": "tf2-gpu.2-6.m82", + "type": "gcloud", + "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m82" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file