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Release 3.62.0 (#1417)
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‎CHANGELOG.md‎

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# Changelog
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# Version 3.62.0 (2024-02-12)
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## Added
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* Support custom metrics for predictions (all applicable annotation classes)
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* `FoundryClient.run_app` Add data_row identifier validation for running foundry app
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* `Client.get_error_status_code` Default to 500 error if a server error is unparseable instead of throwing an exception
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## Updated
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* `DataRowMetadata, DataRowMetadataBatchResponse, _UpsertBatchDataRowMetadata` Make data_row_id and global_key optional in all schema types
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## Fixed
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* `ExportTask.__str__` Fix returned type in ExportTask instance representation
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## Removed
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* `Project.upsert_review_queue`
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## Notebooks
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* Update notebooks to new export methods
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* Add model slice notebook
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* Added support for annotation import with img bytes
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* Update user prompts for huggingface colab
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# Version 3.61.2 (2024-01-29)
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## Added
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* `ModelSlice.get_data_row_identifiers` for Foundry data rows

‎docs/source/conf.py‎

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copyright = '2021, Labelbox'
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author = 'Labelbox'
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release = '3.61.2'
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release = '3.62.0'
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# -- General configuration ---------------------------------------------------
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‎examples/annotation_import/image.ipynb‎

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"source": [
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"import uuid\n",
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"import requests\n",
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"import base64\n",
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"import labelbox as lb\n",
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"import labelbox.types as lb_types"
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],
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" color=(255, 255, 255))\n",
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")\n",
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"\n",
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"# NDJSON using instanceURI, bytes array is not fully supported.\n",
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"# NDJSON using instanceURI, or bytes array.\n",
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"mask_annotation_ndjson = {\n",
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" \"name\": \"mask\",\n",
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" \"classifications\": [],\n",
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" \"mask\": {\"instanceURI\": url,\n",
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" \"colorRGB\": (255, 255, 255)}\n",
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"}\n"
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" \"name\": \"mask\",\n",
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" \"classifications\": [],\n",
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" \"mask\": {\n",
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" \t\"instanceURI\": url,\n",
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" \t\"colorRGB\": (255, 255, 255)\n",
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" }\n",
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"}\n",
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"\n",
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"#Using bytes array.\n",
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"response = requests.get(url)\n",
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"im_bytes = base64.b64encode(response.content).decode('utf-8')\n",
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"\n",
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"mask_annotation_ndjson = {\n",
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" \"name\": \"mask\",\n",
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" \"classifications\": [],\n",
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" \"mask\": {\n",
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" \t\"imBytes\": im_bytes,\n",
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" \"colorRGB\": (255, 255, 255)\n",
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" }\n",
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" }"
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],
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"cell_type": "code",
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"outputs": [],

‎examples/basics/batches.ipynb‎

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"source": [
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"import labelbox as lb\n",
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"import random\n",
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"import uuid"
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"import uuid\n",
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"import json"
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],
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"cell_type": "code",
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"outputs": [],
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{
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"metadata": {},
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"source": [
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"global_keys = [data_row.global_key for data_row in dataset.export_data_rows()]\n",
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"client.enable_experimental = True\n",
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"\n",
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"export_task = dataset.export()\n",
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"export_task.wait_till_done()\n",
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"\n",
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"data_rows = []\n",
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"\n",
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"def json_stream_handler(output: lb.JsonConverterOutput):\n",
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" data_row = json.loads(output.json_str)\n",
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" data_rows.append(data_row)\n",
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"\n",
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"\n",
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"if export_task.has_errors():\n",
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" export_task.get_stream(\n",
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" \n",
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" converter=lb.JsonConverter(),\n",
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" stream_type=lb.StreamType.ERRORS\n",
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" ).start(stream_handler=lambda error: print(error))\n",
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"\n",
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"if export_task.has_result():\n",
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" export_json = export_task.get_stream(\n",
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" converter=lb.JsonConverter(),\n",
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" stream_type=lb.StreamType.RESULT\n",
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" ).start(stream_handler=json_stream_handler)"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\n",
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"print(\"Number of global keys:\", len(global_keys))"
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],
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"cell_type": "code",
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{
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"metadata": {},
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"source": [
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"## Manage batches\n",
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"## Manage Batches\n",
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"Note: You can view your batch data through the **Data Rows** tab."
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"### View batches"
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"### Export Batches"
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"#### Create and Attach Ontology to Project"
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"classification_features = [\n",
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" lb.Classification(\n",
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" class_type=lb.Classification.Type.CHECKLIST,\n",
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" name=\"Quality Issues\",\n",
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" options=[\n",
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" lb.Option(value=\"blurry\", label=\"Blurry\"),\n",
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" lb.Option(value=\"distorted\", label=\"Distorted\")\n",
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" ]\n",
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" )\n",
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"]\n",
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"\n",
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"ontology_builder = lb.OntologyBuilder(\n",
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" tools=[],\n",
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" classifications=classification_features\n",
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")\n",
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"\n",
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"ontology = client.create_ontology(\n",
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" \"Ontology from new features\",\n",
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" ontology_builder.asdict(),\n",
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" media_type=lb.MediaType.Image\n",
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")\n",
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"\n",
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"project.setup_editor(ontology)"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"#### Export from Project"
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],
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"cell_type": "markdown"
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},
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{
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"metadata": {},
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"source": [
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"client.enable_experimental = True\n",
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"\n",
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"export_params = {\n",
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" \"attachments\": True,\n",
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" \"metadata_fields\": True,\n",
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" \"data_row_details\": True,\n",
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" \"project_details\": True,\n",
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" \"performance_details\": True,\n",
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" \"batch_ids\" : [batch.uid] # Include batch ids if you only want to export specific batches, otherwise,\n",
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" #you can export all the data without using this parameter\n",
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"}\n",
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"filters = {}\n",
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"\n",
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"# A task is returned, this provides additional information about the status of your task, such as\n",
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"# any errors encountered\n",
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"export_task = project.export(params=export_params, filters=filters)\n",
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"export_task.wait_till_done()"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"data_rows = []\n",
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"\n",
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"def json_stream_handler(output: lb.JsonConverterOutput):\n",
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" data_row = json.loads(output.json_str)\n",
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" data_rows.append(data_row)\n",
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"\n",
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"\n",
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"if export_task.has_errors():\n",
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" export_task.get_stream(\n",
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" \n",
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" converter=lb.JsonConverter(),\n",
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" stream_type=lb.StreamType.ERRORS\n",
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" ).start(stream_handler=lambda error: print(error))\n",
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"\n",
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"if export_task.has_result():\n",
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" export_json = export_task.get_stream(\n",
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" converter=lb.JsonConverter(),\n",
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" stream_type=lb.StreamType.RESULT\n",
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" ).start(stream_handler=json_stream_handler)"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"## Export the data row iDs\n",
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"data_rows = [dr for dr in batch.export_data_rows()]\n",
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"data_rows = [dr for dr in data_rows]\n",
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"print(\"Data rows in batch: \", data_rows)\n",
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"\n",
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"## List the batches in your project\n",
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"execution_count": null
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}
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]
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}
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}

‎examples/basics/custom_embeddings.ipynb‎

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{
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"metadata": {},
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"source": [
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"API_KEY = \"<ADD YOUR LABELBOX CREDENTIALS>\"\n",
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"API_KEY = \"\"\n",
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"client = lb.Client(API_KEY)\n",
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"\n",
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"# set LABELBOX_API_KEY in bash\n",
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{
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"metadata": {},
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"source": [
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"client.enable_experimental = True\n",
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"\n",
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"# get images from a Labelbox dataset\n",
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"# Our systems start to process data after 1000 embeddings of each type, for this demo make sure your dataset is over 1000 data rows\n",
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"dataset = client.get_dataset(\"<ADD YOUR DATASET ID>\") \n",
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"drs = list(dataset.export_data_rows(timeout_seconds=9999))\n",
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"data_row_ids = [dr.uid for dr in drs]\n",
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"dataset = client.get_dataset(\"<ADD YOUR DATASET ID>\")\n",
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"\n",
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"export_task = dataset.export()\n",
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"export_task.wait_till_done()"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"data_rows = []\n",
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"\n",
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"def json_stream_handler(output: lb.JsonConverterOutput):\n",
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" data_row = json.loads(output.json_str)\n",
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" data_rows.append(data_row)\n",
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"\n",
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"if export_task.has_errors():\n",
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" export_task.get_stream(\n",
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" converter=lb.JsonConverter(),\n",
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" stream_type=lb.StreamType.ERRORS\n",
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" ).start(stream_handler=lambda error: print(error))\n",
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"\n",
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"if export_task.has_result():\n",
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" export_json = export_task.get_stream(\n",
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" converter=lb.JsonConverter(),\n",
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" stream_type=lb.StreamType.RESULT\n",
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" ).start(stream_handler=json_stream_handler)"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"data_row_ids = [dr[\"data_row\"][\"id\"] for dr in data_rows]\n",
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"\n",
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"data_row_ids = data_row_ids[:1000] # keep the first 1000 examples for the sake of this demo"
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],
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"cell_type": "code",

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