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Matt Sokoloff
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basics
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‎examples/basics/Ontology.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "exposed-bullet",
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"metadata": {},
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"source": [
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"# Ontology\n",
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"\n",
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"* An ontology defines the types of vector tools and classifications available for a project\n",
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"* Currently the only way to create an ontology object is by setting up a project with one.\n",
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"* Project setup is going to be explained in another section. \n",
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"* This notebook will cover client side tools for working with ontologies\n",
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"-----\n",
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"Set the following variable to run this notebook"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "treated-convenience",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Make sure this project has been fully setup\n",
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"PROJECT_ID = \"ckm4xyfncfgja0760vpfdxoro\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "prescription-harbor",
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"metadata": {},
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"outputs": [
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{
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"ename": "ImportError",
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"evalue": "cannot import name 'OntologyBuilder' from 'labelbox.schema.ontology' (/Users/matthewsokoloff/Library/Python/3.8/lib/python/site-packages/labelbox/schema/ontology.py)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-2-128ad64f9bcd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mlabelbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0montology\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOntologyBuilder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mClassification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlabelbox\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mImportError\u001b[0m: cannot import name 'OntologyBuilder' from 'labelbox.schema.ontology' (/Users/matthewsokoloff/Library/Python/3.8/lib/python/site-packages/labelbox/schema/ontology.py)"
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]
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}
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],
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"source": [
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"from labelbox.schema.ontology import OntologyBuilder, Tool, Classification\n",
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"from labelbox import project"
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]
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},
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{
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"cell_type": "markdown",
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"id": "sophisticated-sunglasses",
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"metadata": {},
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"source": [
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"#### Create"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "recent-composer",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'2.4.10'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pip3 install "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "published-maintenance",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎examples/basics/README.MD‎

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* Basic tutorial to show usage of all functionality.
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* To see how each of the pieces works together look at the example workflows under the examples directory
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* Order to go through this:
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1. Basics
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2. Datarows
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- other concepts: Metadata
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3. Datasets
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- Other concepts: Tasks
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4. Ontology
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5. Labeling Front End
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6. Projects

‎examples/basics/basics.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "complimentary-passing",
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"metadata": {},
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"source": [
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"# Basics"
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]
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},
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{
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"cell_type": "markdown",
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"id": "smaller-syndication",
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"metadata": {},
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"source": [
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"#### Quick install instructions\n",
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"The quick version is basically just\n",
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"1. `!pip install labelbox`\n",
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"2. `export LABELBOX_API_KEY=\"<your_api_key>\"`\n",
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"* Get this from the UI under (Account -> API -> Create API Key)\n",
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"\n",
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"\n",
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"This only works for cloud deployments.\n",
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"* For more details : https://docs.labelbox.com/python-sdk/en/index-en#labelbox-python-sdk"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "everyday-street",
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"metadata": {},
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"outputs": [],
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"source": [
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"from labelbox import Client\n",
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"from labelbox import Project, Dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "committed-matthew",
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"metadata": {},
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"source": [
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"### Main takeaways:\n",
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"* All interactions with labelbox happen through the client\n",
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"* all attributes that are labelbox.orm.Fields can be accessed via object.field_name\n",
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"* all attributes that are labelbox.orm.Relationships can be accessed via object.relationship()\n",
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"----\n",
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"* To use on your own data you need to plug in the following:\n",
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"1. Project and Dataset ids (go to the web ui and you can find these in the url)\n",
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" * (https://app.labelbox.com/projects/<project_id>\n",
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" * https://app.labelbox.com/dataset/<dataset_id>\n",
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"2. A project name and a dataset name\n",
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" * Select any project names from here: https://app.labelbox.com/projects\n",
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" * Select any dataset names from here: https://app.labelbox.com/data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "instructional-reply",
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"metadata": {},
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"outputs": [],
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"source": [
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"PROJECT_ID = \"ckk4q1viuc0w20704eh69u28h\"\n",
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"DATASET_ID = \"ckk4q1vjznyhu087203wlghfr\"\n",
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"PROJECT_NAME = \"Sample Project\"\n",
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"DATASET_NAME = \"Example Jellyfish Dataset\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "thick-gasoline",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Client is used for all DB interactions.\n",
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"#This is usually the starting point for all usage.\n",
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"client = Client()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "victorian-consumer",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Client can be used to fetch by id:\n",
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"project = client.get_project(PROJECT_ID)\n",
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"dataset = client.get_dataset(DATASET_ID)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "industrial-onion",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Project ID: ckk4q1viuc0w20704eh69u28h>"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"project"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "superb-revolution",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "cubic-joint",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<labelbox.pagination.PaginatedCollection at 0x10caa6160>"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#Or you can fetch all based on a condition\n",
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"projects = client.get_projects(where = Project.name == PROJECT_NAME)\n",
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"datasets = client.get_datasets(where = Dataset.name == DATASET_NAME)\n",
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"projects"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "adapted-parish",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Project ID: ckk4q1viuc0w20704eh69u28h>"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#The above two queries return PaginatedCollections because the filter parameters aren't guarenteed to be unique.\n",
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"#This object is an iterable containing the query results\n",
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"next(projects)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "future-bargain",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<Dataset ID: cklv1qzlv1oqn0y9ne7b9gtpb>]"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# If you are interested in the relationship between objects then \n",
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"#You can only filter on attributes of either a dataset or a project.\n",
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"#If you want all datasets that belongs to a particular project then you can do that with the following query.\n",
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"sample_project_datasets = project.datasets()\n",
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"list(sample_project_datasets)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bacterial-yield",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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