Changelog

All notable changes to the Nucleus Python Client will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.17.13 - 2026-03-06

Fixed

Removed the deprecated pkg_resources package and replaced it with importlib-metadata
Resolved ~79 errors/warnings in sphinx auto doc build errors

0.17.12 - 2026-02-23

Added

Dataset.deduplicate() method to deduplicate images using perceptual hashing. Accepts optional reference_ids to deduplicate specific items, or deduplicates the entire dataset when only threshold is provided. Required threshold parameter (0-64) controls similarity matching (lower = stricter, 0 = exact matches only).
Dataset.deduplicate_by_ids() method for deduplication using internal dataset_item_ids directly, avoiding the reference ID to item ID mapping for improved efficiency.
DeduplicationResult and DeduplicationStats dataclasses for structured deduplication results.

Example usage:

dataset = client.get_dataset("ds_...")

# Deduplicate entire dataset
result = dataset.deduplicate(threshold=10)

# Deduplicate specific items by reference IDs
result = dataset.deduplicate(threshold=10, reference_ids=["ref_1", "ref_2", "ref_3"])

# Deduplicate by internal item IDs (more efficient if you have them)
result = dataset.deduplicate_by_ids(threshold=10, dataset_item_ids=["item_1", "item_2"])

# Access results
print(f"Threshold: {result.stats.threshold}")
print(f"Original: {result.stats.original_count}, Unique: {result.stats.deduplicated_count}")
print(result.unique_reference_ids)

0.17.11 - 2025-11-03

Added

Support passing a limited access key via NucleusClient(limited_access_key=...). When provided, the client sends the x-limited-access-key header on all requests (sync and async).
Allow using the SDK without a standard API key when a limited_access_key is supplied. In this mode, Basic Auth is omitted and only the limited access header is used.

Example usage:

client = nucleus.NucleusClient(limited_access_key="<LIMITED_ACCESS_KEY>")
#...

Changed

Connection accepts extra_headers and only includes Basic Auth when api_key is provided. This enables header-only auth with limited access keys.
Header propagation applies across all request paths, including Validate endpoints and concurrent async helpers.
Tests updated to be tolerant of limited-access-only runs.
NoAPIKey error messaging updated to account for limited_access_key support.

0.17.10 - 2025-03-19

Added

Adding page size variable to items_and_annotation_generator() to reduce timeout errors for customers with large datasets

0.17.9 - 2025-03-11

Added

Adding export_class_labels methods to datasets and slices to extract unique class labels of the annotations in the dataset/slice.

0.17.8 - 2025-01-02

Added

Adding only_most_recent_tasks parameter for dataset.scene_and_annotation_generator() and dataset.items_and_annotation_generator() to accommodate for multiple sets of ground truth caused by relabeled tasks. Also returns the task_id in the annotation results.

0.17.7 - 2024-11-05

Added

Adding slice_id parameter for dataset.scene_and_annotation_generator().

Example usage:

dataset = client.get_dataset("ds_...")
for scene in dataset.scene_and_annotation_generator(slice_id="slc_..."):
  #...

0.17.6 - 2024-07-03

Added

Method for downloading all annotations grouped by scene and track_reference_id.

Example usage:

dataset = client.get_dataset("ds_...")
for scene in dataset.scene_and_annotation_generator():
  #...

0.17.5 - 2024-04-15

Added

Method for uploading lidar semantic segmentation predictions, via dataset.upload_lidar_semseg_predictions

Example usage:

dataset = client.get_dataset("ds_...")
model = client.get_model("prj_...")
pointcloud_ref_id = 'pc_ref_1'
predictions_s3 = "s3://temp/predictions.json"

dataset.upload_lidar_semseg_predictions(model, pointcloud_ref_id, predictions_s3)

For the expected format of the s3 predictions, refer to the documentation here

0.17.4 - 2024-03-25

Modified

In Model.run, added the model_run_name parameter. This allows the creation of multiple model runs for datasets.

[0.17.3] - 2024-02-29

Added

Added the environment variable S3_ENDPOINT to accomodate for nonstandard S3 Endpoint URLs when asking for presigned URLs

0.17.2 - 2024-02-28

Modified

In Dataset.create_slice, the reference_ids parameter is now optional. If left unspecified, it will create an empty slice

0.17.1 - 2024-02-22

Added

Environment variable NUCLEUS_SKIP_SSL_VERIFY to skip SSL verification on requests

0.17.0 - 2024-02-06

Added

Added dataset.add_items_from_dir
Added pytest-xdist for test parallelization

Fixes

Fix test test_models.test_remove_invalid_tag_from_model

0.16.18 - 2024-02-06

Added

Add the ability to add and remove trained_slice_id to a model

0.16.17 - 2024-01-29

Fixes

Update documentation

0.16.16 - 2024-01-25

Fixes

Minor fixes to docstring

0.16.15 - 2024-01-11

Fixes

Fix lidar concurrent lidar pointcloud to also return intensity in case it exists in the response.

0.16.14 - 2024-01-03

Fixes

Open up Pydantic version requirements as was fixed in 0.16.11

0.16.13 - 2023-12-13

Added

Added trained_slice_id parameter to dataset.upload_predictions() to specify the slice ID used to train the model.

Fixes

Fix offset generation for image chips in dataset.items_and_annotation_chip_generator()

0.16.12 - 2023-11-29

Added

Added tag support for slices.

Example:

>>> slc = client.get_slice('slc_id')
>>> tags = slc.tags
>>> slc.add_tags(['new_tag_1', 'new_tag_2'])

0.16.11 - 2023-11-22

Added

Added num_processes parameter to dataset.items_and_annotation_chip_generator() to specify parallel processing.
Method to allow for concurrent task fetches for pointcloud data

Example:

>>> task_ids = ['task_1', 'task_2']
>>> resp = client.download_pointcloud_tasks(task_ids=task_ids, frame_num=1)
>>> resp
{
  'task_1': [Point3D(x=5, y=10.7, z=-2.3), ...],
  'task_2': [Point3D(x=1.3 y=11.1, z=1.5), ...],
}

Fixes

Support environments using pydantic>=2

0.16.10 - 2023-11-22

Allow creating a dataset by crawling all images in a directory, recursively. Also supports privacy mode datasets.

Example structure:

~/Documents/
    data/
        2022/
            - img01.png
            - img02.png
        2023/
            - img01.png
            - img02.png

Default Example:

data_dir = "~/Documents/data"
client.create_dataset_from_dir(data_dir)
# this will create a dataset named "data" and will contain 4 images, with the ref IDs:
# ["2022/img01.png", "2022/img02.png", "2023/img01.png", "2023/img02.png"]

Example Privacy Mode:

This requires that a proxy (or file server) is setup and can serve files relative to the data_dir

data_dir = "~/Documents/data"
client.create_dataset_from_dir(
    data_dir,
    dataset_name='my-dataset',
    use_privacy_mode=True,
    privacy_mode_proxy="http://localhost:5000/assets/"
)

This would create a dataset my-dataset, and when opened in Nucleus, the images would be requested to the path: <privacy_mode_proxy>/<img ref id>, for example: http://localhost:5000/assets/2022/img01.png

0.16.9 - 2023-11-17

Fixes

Minor fixes to video scene upload on privacy mode

0.16.8 - 2023-11-16

Added

Dataset Item width and height

Allow passing width and height to DatasetItem
This is required when using privacy mode

Dataset Item Fetch

Added dataset.items_and_annotation_chip_generator() functionality to generate chips of images in s3 or locally.
Added query parameter for dataset.items_and_annotation_generator() to filter dataset items.

Removed

upload_to_scale is no longer a property in DatasetItem, users should instead specify use_privacy_mode on the dataset during creation

0.16.7 - 2023-11-03

Added

Allow direct embedding vector upload together with dataset items. DatasetItem now has an additional parameter called embedding_info which can be used to directly upload embeddings when a dataset is uploaded.
Added dataset.embedding_indexes property, which exposes information about every embedding index which belongs to the dataset.

0.16.6 - 2023-11-01

Added

Allow datasets to be created in "privacy mode". For example, client.create_dataset('name', use_privacy_mode=True).
Privacy Mode lets customers use Nucleus without sensitive raw data ever leaving their servers.
When set to True, you can submit URLs to Nucleus that link to raw data assets like images or point clouds, instead of transferring that data to Scale. Access control is then completely in the hands of users: URLs may optionally be protected behind your corporate VPN or an IP whitelist. When you load a Nucleus web page, your browser will directly fetch the raw data from your servers without it ever being accessible to Scale.

0.16.5 - 2023-10-30

Added

Added a description to the slice info.

Changed

Made skeleton key optional on KeypointsAnnotation.

0.16.4 - 2023-10-23

Added

Added a query_objects method on the Dataset class.
Example

>>> ds = client.get_dataset('ds_id')
>>> objects = ds.query_objects('annotations.metadata.distance_to_device > 150', ObjectQueryType.GROUND_TRUTH_ONLY)
[CuboidAnnotation(label="", dimensions={}, ...), ...]

Added EvaluationMatch class to represent IOU Matches, False Positives and False Negatives retrieved through the query_objects method

0.16.3 - 2023-10-10

Added

Added a query_scenes method on the Dataset class.
Example

>>> ds = client.get_dataset('ds_id')
>>> scenes = ds.query_scenes('scene.metadata.foo = "baz"')
[Scene(reference_id="", metadata={}, ...), ...]

0.16.2 - 2023-10-03

Fixed

Raise error on all error states for AsyncJob.sleep_until_complete(). Before it only handled the deprecated "Errored"

0.16.1 - 2023-09-18

Added

Added asynchronous parameter for slice.export_embeddings() and dataset.export_embeddings() to allow embeddings to be exported asynchronously.

Changed

Changed slice.export_embeddings() and dataset.export_embeddings() to be asynchronous by deafult.

0.16.0 - 2023-09-18

Removed

Support for Python 3.6 - it is end of life for more than a year

Fixed

Development environment for Python 3.11

0.15.11 - 2023-09-15

Added

Added slice.export_raw_json() functionality to support raw export of object slices (annotations, predictions, item and scene level data). Currently does not support image slices.

0.15.10 - 2023-07-20

Added

Fix slice.export_predictions(args) and slice.export_predictions_generator(args) methods to return Predictions instead of Annotations

0.15.9 - 2023-06-26

Added

Support for Scale Launch client v1.0.0 and higher for the Nucleus + Launch integration

0.15.7 - 2023-06-09

Added

Allow for downloading pointcloud data for a give task and frame number, example:

import nucleus
import numpy as np
client = nucleus.NucleusClient(API_KEY)
pts = client.download_pointcloud_task(task_id, frame_num=1)
np_pts = np.array([pt.to_list() for pt in pts])

0.15.6 - 2023-06-03

Changed

Document new restrictions to slice create/append.
Dataset.create_slice and Slice.append methods cannot exceed 10,000 items per request.

0.15.5 - 2023-05-8

Fixed

Give default annotation_id to KeypointAnnotations when not specified

0.15.4 - 2023-03-21

Changed

Added create_slice_by_ids to create slices from dataset item, scene, and object IDs

0.15.3 - 2023-03-02

Changed

Allow denormalized scores in EvaluationResults

0.15.2 - 2023-02-10

Changed

Fix client.create_launch_model_from_dir(args) method

0.15.1 - 2023-01-16

Changed

Better filter tuning of client.list_jobs(args) method

Added

Dataset method to filter jobs, and statistics on running jobs Example:

>>> client = nucleus.NucleusClient(API_KEY)
>>> ds = client.get_dataset(ds_id)
>>> ds.jobs(show_completed=True, stats_only=True)
{'autotagInference': {'Cancelled': 1, 'Completed': 11},
 'modelRunCommit': {'Completed': 7, 'Errored_Server': 1, 'Running': 1},
 'sliceQuery': {'Completed': 40, 'Running': 2}}

Detailed Example

>>> from nucleus.job import CustomerJobTypes
>>> client = nucleus.NucleusClient(API_KEY)
>>> ds = client.get_dataset(ds_id)
>>> from_date = "2022-12-20"; to_date = "2023-01-15"
>>> job_types = [CustomerJobTypes.MODEL_INFERENCE_RUN, CustomerJobTypes.UPLOAD_DATASET_ITEMS]
>>> ds.jobs(
  from_date=from_date,
  to_date=to_date,
  show_completed=True,
  job_types=job_types,
  limit=150
)
# ... returns list of AsyncJob objects

0.15.0 - 2022-12-19

Changed

dataset.slices now returns a list of Slice objects instead of a list of IDs

Added

Retrieve a slice from a dataset by its name, or all slices of a particular type from a dataset. Where type is one of ["dataset_item", "object", "scene"].

dataset.get_slices(name, slice_type): List[Slice]

from nucleus.slice import SliceType
dataset.get_slices(name="My Slice")
dataset.get_slices(slice_type=SliceType.DATASET_ITEM)

0.14.30 - 2022-11-29

Added

Support for uploading track-level metrics to external evaluation functions using track_ref_ids

0.14.29 - 2022-11-22

Added

Support for Tracks, enabling ground truth annotations and model predictions to be grouped across dataset items and scenes
Helpers to update track metadata, as well as to create and delete tracks at the dataset level

0.14.28 - 2022-11-17

Added

Support for appending to slice with scene reference IDs
Better error handling when appending to a slice with non-existent reference IDs

0.14.27 - 2022-11-04

Added

Support for scene-level external evaluation functions
Support for uploading custom scene-level metrics

0.14.26 - 2022-11-01

Added

Support for fetching scene from a DatasetItem.reference_id Example:

dataset = client.get_dataset("<dataset_id>")
assert dataset.is_scene  # only works on scene datasets
some_item = dataset.iloc(0)
dataset.get_scene_from_item_ref_id(some_item['item'].reference_id)

0.14.25 - 2022-10-20

Updated

Items of a slice can be retrieved by Slice property .item
The type of items returned from .items is based on the slice type:
- slice.type == 'dataset_item' => list of DatasetItem objects
- slice.type == 'object' => list of Annotation/Prediction objects
- slice.type == 'scene' => list of Scene objects

0.14.24 - 2022-10-19

Fixed

Late imports for seldomly used heavy libraries. Sped up CLI invocation and autocomplation. If you had shell completions installed before we recommend removeing them from your .(bash|zsh)rc file and reinstalling with nu install-completions

0.14.23 - 2022-10-17

Added

Support for building slices via Nucleus' Smart Sample

0.14.22 - 2022-10-14

Added

Trigger for calculating Validate metrics for a model. This allows underperforming slice discovery and more model analysis

0.14.21 - 2022-09-28

Added

Support for context_attachment metadata values. See upload metadata for more information.

0.14.20 - 2022-09-23

Fixed

Local uploads are correctly batched and prevents flooding the network with requests

0.14.19 - 2022-08-26

Added

Support for Coordinate metadata values. See upload metadata for more information.

0.14.18 - 2022-08-16

Added

Metadata and confidence support for scene categories

0.14.17 - 2022-08-15

Fixed

Fix AsyncJob status payload keys causing test failures
Fix AsyncJob export test
Fix page_size for {Dataset,Slice}.items_and_annotatation_generator()
Change to simple dependency install step to fix CircleCI caching failures

0.14.16 - 2022-08-12

Added

Scene categorization support

0.14.15 - 2022-08-11

Removed

Removed s3fs, fsspec dependencies for simpler installation in various environments

0.14.14 - 2022-08-11

Added

client.slices to list all of users slices independent of dataset
Added optional parameter asynchronous: bool to Dataset.update_item_metadata and Dataset.update_scene_metadata, allowing the update to run as a background job when set to True

Fixed

Validate unit test listing and evaluation history listing. Now uses new bulk fetch endpoints for faster listing.

0.14.13 - 2022-08-10

Fixed

Fix payload parsing for scene export

0.14.12 - 2022-08-05

Added

Added auto-paginated Slice.export_predictions_generator

Fixed

Change {Dataset,Slice}.items_and_annotation_generator to work with improved paginate endpoint

0.14.11 - 2022-07-20

Fixed

Various docstring and typing updates

0.14.10 - 2022-07-20

Added

Dataset.items_and_annotation_generator()

Fixed

Slice.items_and_annotation_generator() bug

0.14.9 - 2022-07-14

Fixed

NoneType errors in Validate

0.14.8 - 2022-07-14

Fixed

Segmentation metrics filtering. Prior version artificially boosted performance when filtering was applied.

0.14.7 - 2022-07-07

Added

Support running structured queries and retrieving item results via API

0.14.6 - 2022-07-07

Fixed

Dataset.delete_annotations now defaults reference_ids to an empty list and keep_history to true

0.14.5 - 2022-07-05

Fixed

Averaging of rich semantic segmentation taxonomies not taking into account missing classes

0.14.4 - 2022-06-21

Fixed

Regression that caused Validate filter statements to not work

0.14.3 - 2022-06-21

Fixed

CLI installation without GEOS errored out. Now handled by importer.

0.14.2 - 2022-06-21

Fixed

Better error reporting when everything is filtered out by a filter statement in a Validate evaluation function

0.14.1 - 2022-06-20

Fixed

Adapt Segmentation metrics to better support instance segmentation
Change Segmentation/Polygon metrics to use new segmentation metrics

0.14.0 - 2022-06-16

Added

Allow creation/deletion of model tags on new and existing models, eg:

# on model creation
model = client.create_model(name="foo_model", reference_id="foo-model-ref", tags=["some tag"])

# on existing models
existing_model = client.models[0]
existing_model.add_tags(['tag a', 'tag b'])

# remove tag
existing_model.remove_tags(['tag a'])

0.13.5 - 2022-06-15

Fixed

Guard against invalid skeleton indexes in KeypointsAnnotation

0.13.4 - 2022-06-09

Fixed

Guard against extras imports

0.13.3 - 2022-06-09

Fixed

Make installation of scale-launch optional (again!).

0.13.2 - 2022-06-08

Fixed

Open up requirements for easier installation in more environments. Add more optional installs under metrics

0.13.1 - 2022-06-08

Fixed

Make installation of scale-launch optional

0.13.0 - 2022-06-08

Added

Segmentation functions to Validate API

0.12.4 - 2022-06-02

Fixed

Poetry dependency list

0.12.3 - 2022-06-02

Added

New methods to export associated Scale task info at either the item or scene level.
Dataset.export_scale_task_info
Slice.export_scale_task_info

0.12.2 - 2022-06-02

Added

Allow users to upload external evaluation results calculated on the client side.

0.12.1 - 2022-06-02

Added

Suppress warning statement when un-implemented standard configs found

0.12.0 - 2022-05-27

Added

Allow users to create external evaluation functions for Scenario Tests in Validate.

0.11.2 - 2022-05-20

Changed

Restored backward compatibility of video constructor by adding back deprecated attachment_type argument

0.11.1 - 2022-05-19

Added

Exporting model predictions from a slice

0.11.0 - 2022-05-13

Added

Segmentation prediction masks can now be evaluated against polygon annotation with new Validate functions
New function SegmentationToPolyIOU, configurable through client.validate.eval_functions.segmentation_to_poly_iou
New function SegmentationToPolyRecall, configurable through client.validate.eval_functions.segmentation_to_poly_recall
New function SegmentationToPolyPrecision, configurable through client.validate.eval_functions.segmentation_to_poly_precision
New function SegmentationToPolyMAP, configurable through client.validate.eval_functions.segmentation_to_poly_map
New function SegmentationToPolyAveragePrecision, configurable through client.validate.eval_functions.segmentation_to_poly_ap

0.10.8 - 2022-05-10

Fixed

Add checks for duplicate (reference_id, annotation_id) when uploading Annotations or Predictions

0.10.7 - 2022-05-09

Fixed

Add checks for duplicate reference IDs

0.10.6 - 2022-05-06

Added

Video privacy mode

Changed

Removed attachment_type argument in video upload API

0.10.5 - 2022-05-04

Fixed

Invalid polygons are dropped from PolygonMetric iou matching

0.10.4) - 2022-05-02

Added

Additional check added for KeypointsAnnotation names validation
MP4 video upload

0.10.3 - 2022-04-22

Fixed

Polygon and bounding box matching uses Shapely again providing faster evaluations
Evaluation function passing fixed for Polygon and Boundingbox configurations

0.10.1 - 2022-04-21

Added

Added check for payload size

0.10.0) - 2022-04-21

Added

KeypointsAnnotation added
KeypointsPrediction added

0.9.0 - 2022-04-07

Added

Validate metrics support metadata and field filtering on input annotation and predictions
3D/Cuboid metrics: Recall, Precision, 3D IOU and birds eye 2D IOU```
Shapely can be used for metric development if the optional scale-nucleus[shapely] is installed
Full support for passing parameters to evaluation configurations

0.8.4 - 2022-04-06

Changing camera_params of dataset items can now be done through the dataset method update_items_metadata

0.8.3 - 2022-03-29

Added

new Validate functionality to intialize scenario tests without a threshold, and to set test thresholds based on a baseline model.

0.8.2 - 2022-03-18

Added

a fix to the CameraModels enumeration to fix export of camera calibrations for 3D scenes

0.8.1 - 2022-03-18

Added

slice.items_generator() and dataset.items_generator() to allow for export of dataset items at any scale.

0.8.0 - 2022-03-16

Added

mask_url can now be a local file for segmentation annotations or predictions, meaning local upload is now supported for segmentations
Camera params for sensor fusion ingest now support additional camera params to accommodate fisheye camera, etc.
More detailed parameters to control for upload in case of timeouts (see dataset.upload_predictions, dataset.append, and dataset.upload_predictions)

Fixed

Artificially low concurrency for local uploads (all local uploads should be faster now)
Client no longer uses the deprecated (and now removed) segmentation-specific server endpoints
Fixed a bug where retries for local uploads were not working properly: should improve local upload robustness

Removed

client.predict, client.annotate, which have been marked as deprecated for several months.

0.7.0 - 2022-03-09

Added

LineAnnotation added
LinePrediction added

0.6.7 - 2021-03-08

Added

get_autotag_refinement_metrics
Get model using model_run_id
Video API change to require image_location instead of video_frame_location in DatasetItems

0.6.6 - 2021-02-18

Added

Video upload support

0.6.5 - 2021-02-16

Fixed

Dataset.update_autotag docstring formatting
BoxPrediction dataclass parameter typing
validate.scenario_test_evaluation typo

0.6.4 - 2021-02-16

Fixes

Categorization metrics are patched to run properly on Validate evaluation service

0.6.3 - 2021-02-15

Added

Add categorization f1 score to metrics

0.6.1 - 2021-02-08

Added

Adapt scipy and click dependencies to allow Google COLAB usage without update

0.6.0 - 2021-02-07

Added

Nucleus CLI interface nu. Installation instructions are in the README.md.

0.5.4 - 2022-01-28

Added

Add NucleusClient.get_job to retrieve AsyncJobs by job ID

0.5.3 - 2022-01-25

Added

Add average precision to polygon metrics
Add mean average precision to polygon metrics

0.5.2 - 2022-01-20

Added

Add Dataset.delete_scene

Fixed

Removed Shapely dependency

0.5.1 - 2022-01-11

Fixed

Updated dependencies for full Python 3.6 compatibility

0.5.0 - 2022-01-10

Added

nucleus.metrics module for computing metrics between Nucleus Annotation and Prediction objects.

0.4.5 - 2022-01-07

Added

Dataset.scenes property that fetches the Scale-generated ID, reference ID, type, and metadata of all scenes in the Dataset.

0.4.4 - 2022-01-04

Added

Slice.export_raw_items() method that fetches accessible (signed) URLs for all items in the Slice.

0.4.3 - 2022-01-03

Added

Improved error messages for categorization

Changed

Category taxonomies are now updatable

0.4.2 - 2021-12-16

Added

Slice.name property that fetches the Slice's user-defined name.
- The Slice's items are no longer fetched unnecessarily; this used to cause considerable latency.
Slice.items property that fetches all items contained in the Slice.

Changed

Slice.info() now only retrieves the Slice's name, slice_id, and dataset_id.
- The Slice's items are no longer fetched unnecessarily; this used to cause considerable latency.
- This method issues a warning to use Slice.items when attempting to items.

### Deprecated

NucleusClient.slice_info(..) is deprecated in favor of Slice.info().

0.4.1 - 2021-12-13

Changed

Datasets in Nucleus now fall under two categories: scene or item.
- Scene Datasets can only have scenes uploaded to them.
- Item Datasets can only have items uploaded to them.
NucleusClient.create_dataset now requires a boolean parameter is_scene to immutably set whether the Dataset is a scene or item Dataset.

0.4.0 - 2021-08-12

Added

NucleusClient.modelci client extension that houses all features related to Model CI, a continuous integration and testing framework for evaluation machine learning models.
NucleusClient.modelci.UnitTest- class to represent a Model CI unit test.
NucleusClient.modelci.UnitTestEvaluation- class to represent an evaluation result of a Model CI unit test.
NucleusClient.modelci.UnitTestItemEvaluation- class to represent an evaluation result of an individual dataset item within a Model CI unit test.
NucleusClient.modelci.eval_functions- Collection class housing a library of standard evaluation functions used in computer vision.

0.3.0 - 2021-11-23

Added

NucleusClient.datasets property that lists Datasets in a human friendlier manner than NucleusClient.list_datasets()
NucleusClient.models property, this is preferred over the deprecated list_models
NucleusClient.jobs property. NucleusClient.list_jobs is still the preferred method to use if you filter jobs on access.
Deprecated method access now produces a deprecation warning in the logs.

Deprecated

Model runs have been deprecated and will be removed in the near future. Use a Model directly instead. The following functions have all been deprecated as a part of that.
- NucleusClient.get_model_run(..)
- NucleusClient.delete_model_run(..)
- NucleusClient.create_model_run(..)
- NucleusClient.commit_model_run(..)
- NucleusClient.model_run_info(..)
- NucleusClient.predictions_ref_id(..)
- NucleusClient.predictions_iloc(..)
- NucleusClient.predictions_loc(..)
- Dataset.create_model_run(..)
- Dataset.model_runs(..)
NucleusClient.list_datasets is deprecated in favor of NucleusClient.datasets. The latter allows for direct usage of Dataset objects.
NucleusClient.list_models is deprecated in favor of NucleusClient.models.
NucleusClient.get_dataset_items is deprecated in favor of Dataset.items to make the object model more consistent.
NucleusClient.delete_dataset_item is deprecated in favor of Dataset.delete_item to make the object model more consistent.
NucleusClient.populate_dataset is deprecated in favor of Dataset.append to make the object model more consistent.
NucleusClient.ingest_tasks is deprecated in favor of Dataset.ingest_tasks to make the object model more consistent.
NucleusClient.add_model is deprecated in favor of NucleusClient.create_model for consistent terminology.
NucleusClient.dataset_info is deprecated in favor of Dataset.info to make the object model more consistent.
NucleusClient.delete_annotations is deprecated in favor of Dataset.delete_annotations to make the object model more consistent.
NucleusClient.predict is deprecated in favor of Dataset.upload_predictions to make the object model more consistent.
NucleusClient.dataitem_ref_id is deprecated in favor of Dataset.refloc to make the object model more consistent.
NucleusClient.dataitem_iloc is deprecated in favor of Dataset.iloc to make the object model more consistent.
NucleusClient.dataitem_loc is deprecated in favor of Dataset.loc to make the object model more consistent.
NucleusClient.create_slice is deprecated in favor of Dataset.create_slice to make the object model more consistent.
NucleusClient.create_custom_index is deprecated in favor of Dataset.create_custom_index to make the object model more consistent.
NucleusClient.delete_custom_index is deprecated in favor of Dataset.delete_custom_index to make the object model more consistent.
NucleusClient.set_continuous_indexing is deprecated in favor of Dataset.set_continuous_indexing to make the object model more consistent.
NucleusClient.create_image_index is deprecated in favor of Dataset.create_image_index to make the object model more consistent.
NucleusClient.create_object_index is deprecated in favor of Dataset.create_object_index to make the object model more consistent.
Dataset.append_scenes is deprecated in favor of Dataset.append for a simpler interface.

0.0.1 - 0.2.1

Refer to GitHub release notes for older releases.

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CHANGELOG.md

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Changelog

0.17.13 - 2026-03-06

Fixed

0.17.12 - 2026-02-23

Added

0.17.11 - 2025-11-03

Added

Changed

0.17.10 - 2025-03-19

Added

0.17.9 - 2025-03-11

Added

0.17.8 - 2025-01-02

Added

0.17.7 - 2024-11-05

Added

0.17.6 - 2024-07-03

Added

0.17.5 - 2024-04-15

Added

0.17.4 - 2024-03-25

Modified

[0.17.3] - 2024-02-29

Added

0.17.2 - 2024-02-28

Modified

0.17.1 - 2024-02-22

Added

0.17.0 - 2024-02-06

Added

Fixes

0.16.18 - 2024-02-06

Added

0.16.17 - 2024-01-29

Fixes

0.16.16 - 2024-01-25

Fixes

0.16.15 - 2024-01-11

Fixes

0.16.14 - 2024-01-03

Fixes

0.16.13 - 2023-12-13

Added

Fixes

0.16.12 - 2023-11-29

Added

0.16.11 - 2023-11-22

Added

Fixes

0.16.10 - 2023-11-22

Example structure:

Default Example:

Example Privacy Mode:

0.16.9 - 2023-11-17

Fixes

0.16.8 - 2023-11-16

Added

Dataset Item width and height

Dataset Item Fetch

Removed

0.16.7 - 2023-11-03

Added

0.16.6 - 2023-11-01

Added

0.16.5 - 2023-10-30

Added

Changed

0.16.4 - 2023-10-23

Added

0.16.3 - 2023-10-10

Added

0.16.2 - 2023-10-03

Fixed

0.16.1 - 2023-09-18