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Anirudh Apparaju
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Merge remote-tracking branch 'upstream/develop' into develop-2023-07
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‎.github/workflows/python-package.yml‎

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DA_GCP_LABELBOX_API_KEY: ${{ secrets[matrix.da-test-key] }}
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run: |
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tox -e py -- -n 10 -svv --reruns 5 --reruns-delay 10
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tox -e py -- -n 10 -svv --reruns 5 --reruns-delay 8

‎.pre-commit-config.yaml‎

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repos:
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- repo: https://github.com/google/yapf
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rev: v0.31.0
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rev: v0.40.1
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hooks:
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- id: yapf
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name: "yapf"
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args: [-i, --style, google]
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- repo: https://github.com/datarootsio/databooks
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rev: 1.0.1
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hooks:
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- id: databooks-meta
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args: [examples, --overwrite]
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- repo: https://github.com/datarootsio/databooks
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rev: 1.0.1
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hooks:
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- id: databooks-meta
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args: [examples, --overwrite]

‎.readthedocs.yml‎

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sphinx:
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configuration: docs/source/conf.py
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# Build all formats (epub, pdf, htmlzip)
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formats:
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- pdf
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# Not building additional formats, as PDF build is failing
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# formats:
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# - pdf
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# Optionally set the version of Python and requirements required to build your docs
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python:

‎CHANGELOG.md‎

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# Changelog
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# Version 3.49.1 (2023-06-29)
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## Fixed
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* Removed numpy version lock that caused Python version >3.8 to download incompatible numpy version
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# Version 3.49.0 (2023-06-27)
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## Changed
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* Improved batch creation logic when more than 1000 global keys provided
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## Notebooks
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* Added example on how to access mark in export v2
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* Removed NDJSON library from `examples/basics/custom_embeddings.ipynb`
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* Removed `queue_mode` property from `create_project()` method call.
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# Version 3.48.0 (2023-06-13)
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## Added
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* Support for ISO format to exports V2 date filters
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* Support to specify confidence for all free-text annotations
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## Changed
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* Removed backports library and replaced it with python dateutil package to parse iso strings
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## Notebooks
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* Added predictions to model run example
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* Added notebook to run yolov8 and sam on video and upload to LB
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* Updated google colab notebooks to reflect raster segmentation tool being released on 6/13
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* Updated radio NDJSON annotations format to support confidence
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* Added confidence to all free-text annotations (ndjson)
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* Fixed issues with cv2 library rooting from the Geospatial notebook used a png map with a signed URL with an expired token
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# Version 3.47.1 (2023-05-24)
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## Fixed
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* Loading of the ndjson parser when optional [data] libraries (geojson etc.) are not installed
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# Version 3.47.0 (2023-05-23)
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## Added
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* Support for interpolated frames to export v2
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## Changed
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* Removed ndjson library and replaced it with a custom ndjson parser
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## Notebooks
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* Removed confidence scores in annotations - video notebook
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* Removed raster seg masks from video prediction
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* Added export v2 example
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* Added SAM and Labelbox connector notebook
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# Version 3.46.0 (2023-05-03)
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## Added
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* Global key support to DataRow Metadata `bulk_upsert()` function
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## Notebooks
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* Removed dataset based projects from project setup notebook
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* Updated all links to annotation import and prediction notebooks in examples README
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# Version 3.45.0 (2023-04-27)
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## Changed
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* Reduce threshold for async batch creation to 1000 data rows
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## Notebooks
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* Added subclassifications to ontology notebook
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* Added conversational and pdf predictions notebooks
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# Version 3.44.0 (2023-04-26)
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## Added
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* `predictions` param for optionally exporting predictions in model run export v2
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* Limits on `model_run_ids` and `project_ids` on catalog export v2 params
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* `WORKFLOW_ACTION` webhook topic
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* Added `data_row_ids` filter for dataset and project export v2
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## Fixed
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* ISO timestamp parsing for datetime metadata
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* Docstring typo for `client.delete_feature_schema_from_ontology()`
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## Notebooks
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* Removed mention of embeddings metadata fields
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* Fixed broken colab link on `examples/extras/classification-confusion-matrix.ipynb`
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* Added free text classification example to video annotation import notebook
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* Updated prediction_upload notebooks with Annotation Type examples
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# Version 3.43.0 (2023-04-05)
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## Added
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* Nested object classifications to `VideoObjectAnnotation`
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* Relationship Annotation Types
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* Added `project_ids` and `model_run_ids` to params in all export_v2 functions
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## Fixed
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* VideoMaskAnnotation annotation import
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## Notebooks
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* Added DICOM annotation import notebook
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* Added audio annotation import notebook
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* Added HTML annotation import notebook
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* Added relationship examples to annotation import notebooks
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* Added global video classification example
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* Added nested classification examples
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* Added video mask example
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* Added global key and LPOs to queue management notebook
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# Version 3.42.0 (2023-03-22)
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## Added
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* Message based classifications with annotation types for conversations
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* Video and raster segmentation annotation types
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* Global key support to `ConversationEntity`, `DocumentEntity` and `DicomSegments`
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* DICOM polyline annotation type
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* Confidence attribute to classification annotations
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## Changed
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* Increased metadata string size limit to 4096 chars
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* Removed `deletedDataRowGlobalKey` from `get_data_row_ids_for_global_keys()`
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## Fixed
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* Annotation data type coercion by Pydantic
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* Error message when end point coordinates are smaller than start point coordinates
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* Some typos in error messages
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## Notebooks
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* Refactored video notebook to include annotation types
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* Replaced data row ids with global keys in notebooks
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* Replaced `create_data_row` with `create_data_rows` in notebooks
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# Version 3.41.0 (2023-03-15)
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## Added
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* New data classes for creating labels: `AudioData`, `ConversationData`, `DicomData`, `DocumentData`, `HTMLData`
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* New `DocumentEntity` annotation type class
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* New parameter `last_activity_end` to `Project.export_labels()`
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## Notebooks
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* Updated `annotation_import/pdf.ipynb` with example use of `DocumentEntity` class
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# Version 3.40.1 (2023-03-10)
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* Updated `annotation_import/pdf.ipynb` with more examples
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* Added `integrations/huggingface/huggingface.ipynb`
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* Fixed broken links for detectron notebooks in README
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* Added Dataset QueueMode during project creation in `integrations/detectron2/coco_object.ipynb`
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* Added Dataset QueueMode during project creation in `integrations/detectron2/coco_object.ipynb`
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* Removed metadata and updated ontology in `annotation_import/text.ipynb`
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* Removed confidence scores in `annotation_import/image.ipynb`
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* Updated custom embedding tutorial links in `basics/data_row_metadata.ipynb`
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# Version 3.37.0 (2023-02-08)
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## Added
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* New `last_activity_start` param to `project.export_labels()` for filtering which labels are exported. See docstring for more on how this works.
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* New `last_activity_start` param to `project.export_labels()` for filtering which labels are exported. See docstring for more on how this works.
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## Changed
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* Rename `Classification.instructions` to `Classification.name`

‎CONTRIB.md‎

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# Labelbox Python SDK Contribution Guide
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## Contribution Guidelines
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Thank you for expressing your interest in contributing to the Labelbox SDK.
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To ensure that your contribution aligns with our guidelines, please carefully
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review the following considerations before proceeding:
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* For feature requests, we recommend consulting with Labelbox support or
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creating a [Github Issue](https://github.com/Labelbox/labelbox-python/issues) on our repository.
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* We can only accept general solutions that address common issues rather than solutions
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designed for specific use cases. Acceptable contributions include simple bug fixes and
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improvements to functions within the schema/ package.
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* Please ensure that any new libraries are compliant with the Apache license that governs the Labelbox SDK.
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* Ensure that you update any relevant docstrings and comments within your code
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## Repository Organization
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The SDK source (excluding tests and support tools) is organized into the
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following packages/modules:
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* `data/` package contains code that maps annotations (labels or pre-labels) to
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Python objects, as well as serialization and deserialization tools for converting
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between NDJson and Annotation Types.
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* `orm/` package contains code that supports the general mapping of Labelbox
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data to Python objects. This includes base classes, attribute (field and
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* Approved PRs are merged to the `develop` branch.
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* The `develop` branch is merged to `master` on each release.
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## Commits
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## Formatting
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Before making a commit, to automatically adhere to our formatting standards,
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install and activate [pre-commit](https://pre-commit.com/)
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```shell
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pip install pre-commit
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pre-commit install
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```
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After the above, running `git commit ...` will attempt to fix formatting,
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and make necessary changes to files. You will then need to stage those files again.
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You may also manually format your code by running the following:
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```shell
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yapf tests labelbox -i --verbose --recursive --parallel --style "google"
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```
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After the above, running `git commit ...` will attempt to fix formatting. If
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there was formatted needed, you will need to re-add and re-commit before pushing.
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## Testing
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Currently, the SDK functionality is tested using integration tests. These tests
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communicate with a Labelbox server (by default the staging server) and are in
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that sense not self-contained. Besides, that they are organized like unit test
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and are based on the `pytest` library.
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Currently, the SDK functionality is tested using unit and integration tests.
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The integration tests communicate with a Labelbox server (by default the staging server)
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and are in that sense not self-contained.
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Please consult "Testing" section in the README for more details on how to test.
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To execute tests you will need to provide an API key for the server you're using
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Additionally, to execute tests you will need to provide an API key for the server you're using
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for testing (staging by default) in the `LABELBOX_TEST_API_KEY` environment
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variable. For more info see [Labelbox API key docs](https://labelbox.helpdocs.io/docs/api/getting-started).
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To pass tests, code must be formatted. If pre-commit was not installed,
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you will need to use the following command:
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yapf tests labelbox -i --verbose --recursive --parallel --style "google"
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```
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## Release Steps
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Each release should follow the following steps:
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2. Make sure the `CHANGELOG.md` contains appropriate info
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3. Commit these changes and tag the commit in Git as `vX.Y`
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4. Merge `develop` to `master` (fast-forward only).
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5. Create a GitHub release.
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6. This will kick off a Github Actions workflow that will:
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- Build the library in the [standard way](https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archives)
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- Upload the distribution archives in the [standard way](https://packaging.python.org/tutorials/packaging-projects/#uploading-the-distribution-archives)
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- with credentials for the `labelbox` PyPI user.
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## Running Jupyter Notebooks
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We have plenty of good samples in the _examples_ directory and using them for testing can help us increase our productivity. One way to use jupyter notebooks is to run the jupyter server locally (another way is to use a VSC plugin, not documented here). It works really fast.
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3. Open the file (this should be ipython_config.py and it is usually located in ~/.ipython/profile_default) and add the following line of code:
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```
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'import sys; sys.path.insert(0, "<labelbox-python root folder>")'
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]
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```
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Please consult the Labelbox team for releasing your contributions
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## Running Jupyter Notebooks
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We have plenty of good samples in the _examples_ directory and using them for testing can help us increase our productivity. One way to use jupyter notebooks is to run the jupyter server locally (another way is to use a VSC plugin, not documented here). It works really fast.
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Make sure your notebook will use your source code:
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1. `ipython profile create`
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2. `ipython locate` - will show where the config file is. This is the config file used by the jupyter server, since it runs via ipython
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3. Open the file (this should be ipython_config.py and it is usually located in ~/.ipython/profile_default) and add the following line of code:
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```
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c.InteractiveShellApp.exec_lines = [
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'import sys; sys.path.insert(0, "<labelbox-python root folder>")'
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]
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```
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4. Go to the root of your project and run `jupyter notebook` to start the server
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‎Dockerfile‎

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libfontconfig1 \
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libgl1-mesa-glx \
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libgeos-dev
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libgeos-dev \
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gcc
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COPY requirements.txt /usr/src/

‎Makefile‎

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build:
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build-image:
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docker build -t local/labelbox-python:test .
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test-local: build-image
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test-local: build
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@# if PATH_TO_TEST we asuume you know what you are doing
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@# if PATH_TO_TEST we assume you know what you are doing
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@if [ -z ${PATH_TO_TEST} ]; then \
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./scripts/ensure_local_setup.sh; \
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docker run -it -v ${PWD}:/usr/src -w /usr/src \
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docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \
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-e LABELBOX_TEST_ENVIRON="local" \
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-e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \
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-e LABELBOX_TEST_API_KEY_LOCAL=${LABELBOX_TEST_API_KEY_LOCAL} \
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local/labelbox-python:test pytest $(PATH_TO_TEST)
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test-staging: build
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docker run -it -v ${PWD}:/usr/src -w /usr/src \
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test-staging: build-image
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docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \
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-e LABELBOX_TEST_ENVIRON="staging" \
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-e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \
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-e LABELBOX_TEST_API_KEY_STAGING=${LABELBOX_TEST_API_KEY_STAGING} \
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local/labelbox-python:test pytest -n 10 $(PATH_TO_TEST)
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local/labelbox-python:test pytest $(PATH_TO_TEST)
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test-staging-eu: build-image
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docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \
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-e LABELBOX_TEST_ENVIRON="staging-eu" \
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-e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \
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-e LABELBOX_TEST_API_KEY_STAGING_EU=${LABELBOX_TEST_API_KEY_STAGING_EU} \
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local/labelbox-python:test pytest $(PATH_TO_TEST)
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test-prod: build
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docker run -it -v ${PWD}:/usr/src -w /usr/src \
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test-prod: build-image
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docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \
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-e LABELBOX_TEST_ENVIRON="prod" \
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-e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \
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-e LABELBOX_TEST_API_KEY_PROD=${LABELBOX_TEST_API_KEY_PROD} \
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local/labelbox-python:test pytest $(PATH_TO_TEST)
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test-onprem: build
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docker run -it -v ${PWD}:/usr/src -w /usr/src \
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test-onprem: build-image
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docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \
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-e LABELBOX_TEST_ENVIRON="onprem" \
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-e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \
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-e LABELBOX_TEST_API_KEY_ONPREM=${LABELBOX_TEST_API_KEY_ONPREM} \
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-e LABELBOX_TEST_ONPREM_HOSTNAME=${LABELBOX_TEST_ONPREM_HOSTNAME} \
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local/labelbox-python:test pytest $(PATH_TO_TEST)
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test-custom: build
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docker run -it -v ${PWD}:/usr/src -w /usr/src \
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test-custom: build-image
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docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \
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-e LABELBOX_TEST_ENVIRON="custom" \
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-e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \
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-e LABELBOX_TEST_API_KEY_CUSTOM=${LABELBOX_TEST_API_KEY_CUSTOM} \

‎docs/source/conf.py‎

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copyright = '2021, Labelbox'
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author = 'Labelbox'
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release = '3.40.1'
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release = '3.49.1'
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# -- General configuration ---------------------------------------------------
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