Project

class labelbox.schema.project.Project(client, field_values)[source]

Bases: DbObject, Updateable, Deletable

A Project is a container that includes a labeling frontend, an ontology, datasets and labels.

name

Type:: str

description

Type:: str

updated_at

Type:: datetime

created_at

Type:: datetime

setup_complete

Type:: datetime

last_activity_time

Type:: datetime

auto_audit_number_of_labels

Type:: int

auto_audit_percentage

Type:: float

is_benchmark_enabled

Type:: bool

is_consensus_enabled

Type:: bool

created_by

ToOne relationship to User

Type:: Relationship

organization

ToOne relationship to Organization

Type:: Relationship

labeling_frontend

ToOne relationship to LabelingFrontend

Type:: Relationship

labeling_frontend_options

ToMany relationship to LabelingFrontendOptions

Type:: Relationship

labeling_parameter_overrides

ToMany relationship to LabelingParameterOverride

Type:: Relationship

webhooks

ToMany relationship to Webhook

Type:: Relationship

benchmarks

ToMany relationship to Benchmark

Type:: Relationship

ontology

ToOne relationship to Ontology

Type:: Relationship

task_queues[source]

ToMany relationship to TaskQueue

Type:: Relationship

add_model_config(model_config_id: str) → str[source]

Adds a model config to this project.

Parameters:: model_config_id (str) – ID of a model config to add to this project.
Returns:: str, ID of the project model config association. This is needed for updating and deleting associations.

batches() → PaginatedCollection[source]

Fetch all batches that belong to this project

Returns:: A PaginatedCollection of `Batch`es

clone() → Project[source]

Clones the current project.

Returns:: The cloned project.
Return type:: Project

connect_ontology(ontology) → None[source]

Connects the ontology to the project. If an editor is not setup, it will be connected as well.

Note: For live chat model evaluation projects, the editor setup is skipped because it is automatically setup when the project is created.

Parameters:: ontology (Ontology) – The ontology to attach to the project

create_batch(name: str, data_rows: List[str | DataRow] | None = None, priority: int = 5, consensus_settings: Dict[str, Any] | None = None, global_keys: List[str] | None = None)[source]

Creates a new batch for a project. One of global_keys or data_rows must be provided, but not both. A: maximum of 100,000 data rows can be added to a batch.

Parameters:

name – a name for the batch, must be unique within a project
data_rows – Either a list of DataRows or Data Row ids.
global_keys – global keys for data rows to add to the batch.
priority – An optional priority for the Data Rows in the Batch. 1 highest -> 5 lowest
consensus_settings – An optional dictionary with consensus settings: {‘number_of_labels’: 3, ‘coverage_percentage’: 0.1}

Returns: the created batch

Raises:: lbox.exceptions.ValueError if a project is not batch mode, if the project is auto data generation, if the batch exceeds 100k data rows –

create_batches(name_prefix: str, data_rows: List[str | DataRow] | None = None, global_keys: List[str] | None = None, priority: int = 5, consensus_settings: Dict[str, Any] | None = None) → CreateBatchesTask[source]

Creates batches for a project from a list of data rows. One of global_keys or data_rows must be provided, but not both. When more than 100k data rows are specified and thus multiple batches are needed, the specific batch that each data row will be placed in is undefined.

Batches will be created with the specified name prefix and a unique suffix. The suffix will be a 4-digit number starting at 0000. For example, if the name prefix is “batch” and 3 batches are created, the names will be “batch0000”, “batch0001”, and “batch0002”. This method will throw an error if a batch with the same name already exists.

Parameters:

name_prefix – a prefix for the batch names, must be unique within a project
data_rows – Either a list of DataRows or Data Row ids.
global_keys – global keys for data rows to add to the batch.
priority – An optional priority for the Data Rows in the Batch. 1 highest -> 5 lowest
consensus_settings – An optional dictionary with consensus settings: {‘number_of_labels’: 3, ‘coverage_percentage’: 0.1}

Returns: a task for the created batches

create_batches_from_dataset(name_prefix: str, dataset_id: str, priority: int = 5, consensus_settings: Dict[str, Any] | None = None) → CreateBatchesTask[source]

Creates batches for a project from a dataset, selecting only the data rows that are not already added to the project. When the dataset contains more than 100k data rows and multiple batches are needed, the specific batch that each data row will be placed in is undefined. Note that data rows may not be immediately available for a project after being added to a dataset; use the _wait_until_data_rows_are_processed method to ensure that data rows are available before creating batches.

Batches will be created with the specified name prefix and a unique suffix. The suffix will be a 4-digit number starting at 0000. For example, if the name prefix is “batch” and 3 batches are created, the names will be “batch0000”, “batch0001”, and “batch0002”. This method will throw an error if a batch with the same name already exists.

Parameters:

name_prefix – a prefix for the batch names, must be unique within a project
dataset_id – the id of the dataset to create batches from
priority – An optional priority for the Data Rows in the Batch. 1 highest -> 5 lowest
consensus_settings – An optional dictionary with consensus settings: {‘number_of_labels’: 3, ‘coverage_percentage’: 0.1}

Returns: a task for the created batches

delete_project_model_config(project_model_config_id: str) → bool[source]

Deletes the association between a model config and this project.

Parameters:: project_model_config_id (str) – ID of a project model config association to delete for this project.
Returns:: bool, indicates if the operation was a success.

enable_model_assisted_labeling(toggle: bool = True) → bool[source]

Turns model assisted labeling either on or off based on input

Parameters:: toggle (bool) – True or False boolean
Returns:: True if toggled on or False if toggled off

export(task_name: str | None = None, filters: ProjectExportFilters | None = None, params: ProjectExportParams | None = None) → ExportTask[source]

Creates a project export task with the given params and returns the task.

>>>     task = project.export(
>>>         filters={
>>>             "last_activity_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"],
>>>             "label_created_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"],
>>>             "data_row_ids": [DATA_ROW_ID_1, DATA_ROW_ID_2, ...] # or global_keys: [DATA_ROW_GLOBAL_KEY_1, DATA_ROW_GLOBAL_KEY_2, ...]
>>>             "batch_ids": [BATCH_ID_1, BATCH_ID_2, ...]
>>>         },
>>>         params={
>>>             "performance_details": False,
>>>             "label_details": True
>>>         })
>>>     task.wait_till_done()
>>>     task.result

export_issues(status=None) → str[source]

Calls the server-side Issues exporting that returns the URL to that payload.

Parameters:: status (string) – valid values: Open, Resolved
Returns:: URL of the data file with this Project’s issues.

export_v2(task_name: str | None = None, filters: ProjectExportFilters | None = None, params: ProjectExportParams | None = None) → Task | ExportTask[source]

Creates a project export task with the given params and returns the task.

For more information visit: https://docs.labelbox.com/docs/exports-v2#export-from-a-project-python-sdk

>>>     task = project.export_v2(
>>>         filters={
>>>             "last_activity_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"],
>>>             "label_created_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"],
>>>             "data_row_ids": [DATA_ROW_ID_1, DATA_ROW_ID_2, ...] # or global_keys: [DATA_ROW_GLOBAL_KEY_1, DATA_ROW_GLOBAL_KEY_2, ...]
>>>             "batch_ids": [BATCH_ID_1, BATCH_ID_2, ...]
>>>         },
>>>         params={
>>>             "performance_details": False,
>>>             "label_details": True
>>>         })
>>>     task.wait_till_done()
>>>     task.result

extend_reservations(queue_type) → int[source]

Extends all the current reservations for the current user on the given queue type. :param queue_type: Either “LabelingQueue” or “ReviewQueue” :type queue_type: str

Returns:: int, the number of reservations that were extended.

get_label_count() → int[source]: Returns: the total number of labels in this project.

get_label_imports() → PaginatedCollection[source]

Returns label import objects associated with the project.

Returns:: PaginatedCollection

get_labeling_service() → LabelingService[source]

Get the labeling service for this project.

Will automatically create a labeling service if one does not exist.

Returns:: The labeling service for this project.
Return type:: LabelingService

get_labeling_service_dashboard() → LabelingServiceDashboard[source]

Get the labeling service for this project.

Returns:: The labeling service for this project.
Return type:: LabelingServiceDashboard

Attributes of the dashboard include:

id (str): The project id. name (str): The project name. created_at, updated_at (datetime): The creation and last update times of the labeling service. None if labeling service is not requested. created_by_id (str): The user id of the creator of the labeling service. None if labeling service is not requested. status (LabelingServiceStatus): The status of the labeling service. Returns LabelingServiceStatus.Missing if labeling service is not requested. data_rows_count (int): The number of data rows in the project. 0 if labeling service is not requested. data_rows_in_review_count (int): The number of data rows in review queue. 0 if labeling service is not requested. data_rows_in_rework_count (int): The number of data rows in rework. 0 if labeling service is not requested. data_rows_done_count (int): The number of data rows in done queue. 0 if labeling service is not requested. tags (List[str]): Project tags. tasks_completed (int): The number of tasks completed, the same as data_rows_done_count. 0 if labeling service is not requested. tasks_remaining (int): The number of tasks remaining, the same as data_rows_count - data_rows_done_count. 0 if labeling service is not requested. service_type (str): Descriptive type for labeling service.

NOTE can call dict() to get all attributes as dictionary.

get_labeling_service_status() → LabelingServiceStatus[source]

Get the labeling service status for this project.

Raises:: ResourceNotFoundError if the project does not have a labeling service. –
Returns:: The labeling service status for this project.
Return type:: LabelingServiceStatus

get_mal_prediction_imports() → PaginatedCollection[source]

Returns mal prediction import objects which are used in model-assisted labeling associated with the project.

Returns:: PaginatedCollection

get_overview(details=False) → ProjectOverview | ProjectOverviewDetailed[source]

Return the overview of a project.

This method returns the number of data rows per task queue and issues of a project, which is equivalent to the Overview tab of a project.

Parameters:

details (bool, optional) – Whether to include detailed queue information for review and rework queues. Defaults to False.

Returns:

An object representing the project overview.: If details is False, returns a ProjectOverview object. If details is True, returns a ProjectOverviewDetailed object.

Return type:

Union[ProjectOverview, ProjectOverviewDetailed]

Raises:

Exception – If there is an error executing the query.

get_resource_tags() → List[ResourceTag][source]: Returns tags for a project

is_chat_evaluation() → bool[source]

Returns:: True if this project is a live chat evaluation project, False otherwise

is_prompt_response() → bool[source]

Returns:: True if this project is a prompt response project, False otherwise

labeler_performance() → PaginatedCollection[source]

Returns the labeler performances for this Project.

Returns:: A PaginatedCollection of LabelerPerformance objects.

labels(datasets=None, order_by=None) → PaginatedCollection[source]

Custom relationship expansion method to support limited filtering.

Parameters:

datasets (iterable of Dataset) – Optional collection of Datasets whose Labels are sought. If not provided, all Labels in this Project are returned.
order_by (None or (Field, Field.Order)) – Ordering clause.

members() → PaginatedCollection[source]

Fetch all current members for this project

Returns:: A PaginatedCollection of `ProjectMember`s

move_data_rows_to_task_queue(data_row_ids: UniqueIds | GlobalKeys, task_queue_id: str)[source]

Moves data rows to the specified task queue.

Parameters:

data_row_ids – a list of data row ids to be moved. This should be a DataRowIdentifiers object DataRowIdentifier objects are lists of ids or global keys. A DataIdentifier object can be a UniqueIds or GlobalKeys class.
task_queue_id – the task queue id to be moved to, or None to specify the “Done” queue

Returns:

None if successful, or a raised error on failure

review_metrics(net_score) → int[source]

Returns this Project’s review metrics.

Parameters:: net_score (None or Review.NetScore) – Indicates desired metric.
Returns:: int, aggregation count of reviews for given net_score.

set_labeling_parameter_overrides(data: List[Tuple[UniqueId | GlobalKey, int]]) → bool[source]

Adds labeling parameter overrides to this project.

See information on priority here:

https://docs.labelbox.com/en/configure-editor/queue-system#reservation-system

>>> project.set_labeling_parameter_overrides([
>>>     (data_row_gk1, 2), (data_row_gk2, 1)])

Parameters:

data (iterable) –

An iterable of tuples. Each tuple must contain (DataRowIdentifier, priority<int>) for the new override. DataRowIdentifier is an object representing a data row id or a global key. A DataIdentifier object can be a UniqueIds or GlobalKeys class.

Priority:

Data will be labeled in priority order.
- A lower number priority is labeled first.
- All signed 32-bit integers are accepted, from -2147483648 to 2147483647.
Priority is not the queue position.
- The position is determined by the relative priority.
- E.g. [(data_row_1, 5,1), (data_row_2, 2,1), (data_row_3, 10,1)]
  will be assigned in the following order: [data_row_2, data_row_1, data_row_3]
The priority only effects items in the queue.
- Assigning a priority will not automatically add the item back into the queue.

Returns:

bool, indicates if the operation was a success.

set_project_model_setup_complete() → bool[source]

Sets the model setup is complete for this project. Once the project is marked as “setup complete”, a user can not add / modify delete existing project model configs.

Returns:: bool, indicates if the model setup is complete.

NOTE: This method should only be used for live model evaluation projects.: It will throw exception for all other types of projects. User Project is_chat_evaluation() method to check if the project is a live model evaluation project.

task_queues() → List[TaskQueue][source]

Fetch all task queues that belong to this project

Returns:: A List of `TaskQueue`s

update(**kwargs)[source]

Updates this project with the specified attributes

Parameters:: kwargs – a dictionary containing attributes to be upserted

Note that the queue_mode cannot be changed after a project has been created.

Additionally, the quality setting cannot be changed after a project has been created. The quality mode for a project is inferred through the following attributes:

Benchmark:: auto_audit_number_of_labels = 1 and auto_audit_percentage = 1.0
Consensus:: auto_audit_number_of_labels > 1 or auto_audit_percentage <= 1.0

Attempting to switch between benchmark and consensus modes is an invalid operation and will result in an error.

update_data_row_labeling_priority(data_rows: UniqueIds | GlobalKeys, priority: int) → bool[source]

Updates labeling parameter overrides to this project in bulk. This method allows up to 1 million data rows to be updated at once.

See information on priority here:: https://docs.labelbox.com/en/configure-editor/queue-system#reservation-system

Parameters:

data_rows – data row identifiers object to update priorities. DataRowIdentifier objects are lists of ids or global keys. A DataIdentifier object can be a UniqueIds or GlobalKeys class.
priority (int) – Priority for the new override. See above for more information.

Returns:

bool, indicates if the operation was a success.

update_project_resource_tags(resource_tag_ids: List[str]) → List[ResourceTag][source]

Creates project resource tags

Parameters:: resource_tag_ids –
Returns:: a list of ResourceTag ids that was created.

upsert_instructions(instructions_file: str) → None[source]

Uploads instructions to the UI. Running more than once will replace the instructions

Parameters:

instructions_file (str) – Path to a local file. * Must be a pdf or html file

Raises:

ValueError –

project must be setup * instructions file must have a “.pdf” or “.html” extension

class labelbox.schema.project.ProjectMember(client, field_values)[source]: Bases: DbObject