Result Types

Workspace

Workspace details for a specific workspace given by a provided secret key.

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Applicable Functions

client.get_info()

Attributes

Workspace(
    id="ws_1c8aab980f174b0296c7e35e88665b13",
    name="Raighne's Workspace",
    owner="user_6323fea23e292439f31c58cd",
    tier="Developer",
    create_date=1701927649302
)

Name	Type	Description
`id`	`str`	Unique ID of the current workspace.
`name`	`str`	Name of the current workspace. You can modify this on Nexus directly by clicking on the Settings button on the top right of the Workspace Dashboard.
`owner`	`str`	Unique user ID of the workspace owner. This is tied to the email used to sign up for the Nexus account.
`tier`	`str`	Current workspace tier that determines access to advanced features and increased resource quotas. To enjoy these benefits, check out how to Upgrade Your Plan.
`create_date`	`int`	UNIX timestamp of workspace creation date.

Project

Project results for a specific project given by the project key.

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Applicable Functions

Attributes

Project(
    id='proj_9004a21df7b040ace4674c4879603fe8',
    name='keypoints',
    workspace_id='ws_1c8aab980f174b0296c7e35e88665b13',
    type='ObjectDetection',
    create_date=1701927649302,
    localization='MULTI',
    tags=['cat faces'],
    groups=['main', 'cats'],
    statistic=Statistic(
        tags_count=[TagsCountItem(name='cat faces', count=0)],
        total_assets=28,
        annotated_assets=0,
        total_annotations=0
    )
)

Attribute	Type	Description
`id`	`str`	Project ID.
`name`	`str`	Project name.
`workspace_id`	`str`	Workspace ID of the project.
`type`	`str`	Project task type, one of tails for a specific workspace given by a provided [s
`create_date`	`int`	UNIX timestamp of project creation date.
`localization`	`str`	Region(s) for data localization.
`tags`	`list[str]`	List of tag names.
`groups`	`list[str]`	List of assets groups.
`statistic`	`dict`	Contains project statistics for the following categories: `tags_count`: List of tag counts `asset_total`: Total number of assets in the project. `asset_annotated`: Total number of annotated assets in the project. `annotation_total`: Total number of annotations in the project.

Statistic

Project-level statistics on tags, assets, and annotations.

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Applicable Functions

project.get_info()

Attributes

Statistic(
    tags_count=[TagsCountItem(name="cat faces", count=0)],
    total_assets=28,
    annotated_assets=0,
    total_annotations=0
)

Name	Type	Description
`tags_count`	`list[TagCountItem]`	List of `TagCountItem` objects representing tag counts for each tag in the project.
`total_assets`	`int`	Total number of assets in the project.
`annotated_assets`	`int`	Total number of annotated assets in the project.
`total_annotations`	`int`	Total number of annotations in the project.

User

User metadata.

Applicable Functions

project.list_users()

ProjectUser(
    id='user_6323fea23e292439f31c58cd',
    access_type='Owner',
    email='[email protected]',
    nickname='raighne',
    picture='https://s.gravatar.com/avatar/avatars%2Fra.png'
)

Attribute	Type	Description
`id`	`str`	User ID.
`access_type`	`str`	The access type of the current project, one of workspace given by a provided [secret .
`email`	`str`	User email.
`nickname`	`str`	User nickname.
`picture`	`str`	User profile picture.

AssetResults

Data results for a specific asset.

Applicable Functions

Asset(
    id='asset_8208740a-2d9c-46e8-abb9-5777371bdcd3',
    filename='boat180.png',
    project='proj_cd067221d5a6e4007ccbb4afb5966535',
    status='None',
    create_date=1701927649302,
    url='',
    metadata=AssetMetadata(
        file_size=186497,
        mime_type='image/png',
        height=243,
        width=400,
        groups=['main'],
        custom_metadata={'captureAt': '2021-03-10T09:00:00Z'}
    ),
    statistic=AssetAnnotationsStatistic(
        tags_count=[],
        total_annotations=0
    )
)

Attribute	Type	Description
`id`	`str`	Asset ID.
`filename`	`str`	File name of the asset.
`project`	`str`	Project ID in which the asset is contained.
`status`	`str`	The status of the asset, enum: notated`,` Review`,` Completed`,` Tofix`,` None`] \| \|
`create_date`	`int`	UNIX timestamp of when the asset was uploaded.
`url`	`str`	URL to the raw asset file.
`metadata`	`dict`	Asset metadata .
`statistic`	`dict`	Asset annotation statistics .

AssetMetadata

Metadata for a specific asset.

Applicable Functions

AssetMetadata(
    file_size=186497,
    mime_type='image/png',
    height=243,
    width=400,
    groups=['main'],
    custom_metadata={'captureAt': '2021-03-10T09:00:00Z'}
)

Attribute	Type	Description
`file_size`	`int`	Size of the asset in bytes.
`mime_type`	`str`	Media type and format of the asset.
`height`	`int`	Pixel height of the asset.
`width`	`int`	Pixel width of the asset.
`groups`	`List[str]`	The groups of the asset.
`custom_metadata`	`dict`	The custom metadata of the asset.

AssetStatistics

Data statistics for a specific asset.

Applicable Functions

AssetAnnotationsStatistic(
    tags_count= [
        TagsCountItem(name="tagName1", count=1)
    ],
    total_annotations= 2
)

Attribute	Type	Description
`tags_count`	`list[dict]`	List of tag counts.
`total_annotations`	`int`	Total number of annotations in the asset.

GroupStatistics

Asset group statistics.

Applicable Functions

project.assets.list_groups()

[
    AssetGroup(
        group='1', 
        statistic=AssetGroupStatistic(
            total_assets=1, 
            annotated_assets=0, 
            reviewed_assets=0, 
            to_fixed_assets=0, 
            completed_assets=0
        )
    )
]

Attribute	Type	Description
`group`	`str`	Name of the asset group.
`statistic`	`dict`	Contains asset counts of the following categories: `total_assets`: Total number of assets in the asset group `annotated_assets`: Total number of annotated assets in the asset group. `reviewed_assets`: Total number of reviewed assets in the asset group. `to_fixed_assets`: Total number of assets in which annotations need to be fixed in the asset group. `completed_assets`: Total number of assets that have completed the annotation pipeline in the asset group.

Attribute

Type

Description

group

str

Name of the asset group.

statistic

dict

Contains asset counts of the following categories:

total_assets: Total number of assets in the asset group
annotated_assets: Total number of annotated assets in the asset group.
reviewed_assets: Total number of reviewed assets in the asset group.
to_fixed_assets: Total number of assets in which annotations need to be fixed in the asset group.
completed_assets: Total number of assets that have completed the annotation pipeline in the asset group.

TagCountItem

Total count of instances of a tag.

Applicable Functions

project.get_info()

Attributes

TagCountItem(
  name="tagName1",
  count=1
)

Name	Type	Description
`name`	`str`	Tag name.
`count`	`int`	Total count of instances of the tag.

AnnotationMetadata

Metadata for a specific annotation.

Applicable Functions

Annotation(
    id='annot_a9ff9b21-c0e2-49ff-8a69-773aaf00a6f8',
    project_id='proj_cd067221d5a6e4007ccbb4afb5966535',
    asset_id='asset_f4dcb429-0332-4dd6-a1b4-fee794031ba6',
    tag='boat',
    bound_type='Rectangle',
    create_date=1701927649302,
    bound=[
        [0.2772511848341232, 0.34635416666666663],
        [0.2772511848341232, 0.46875],
        [0.54739336492891, 0.46875],
        [0.54739336492891, 0.34635416666666663]
    ]
)

Attribute	Type	Description
`id`	`str`	Unique ID of the annotation.
`project_id`	`str`	ID of the project containing the annotation.
`asset_id`	`str`	ID of the asset containing the annotation.
`tag`	`str`	Tag name of the annotation.
`bound_type`	`str`	Bound type of the annotation, one of specific workspace give.
`bound`	`list[list[float]]`	Bound vertices with the following format: `[[x1, y1], [x2, y2], ... , [xn, yn]]`

WorkflowMetadata

Metadata for a training workflow.

Applicable Functions

Workflow(
    id='flow_64e812a7e47592ef374cbbc2',
    project_id='proj_cd067221d5a6e4007ccbb4afb5966535',
    title='Yolov8 Workflow',
    create_date=1701927649302,
    update_date=1701927649302
)

Attribute	Type	Description
`id`	`str`	ID of the workflow.
`title`	`str`	Name of the workflow.
`project_id`	`str`	Project ID containing the workflow.
`update_date`	`int`	Last updated UNIX timestamp of the workflow.

TrainingInsight

Insight metadata for a training run.

Applicable Functions

project.list_insights()

ProjectInsight(
    flow_title='Test workflow', 
    run_id='run_4a5d406d-464d-470c-bd7d-e92456621ad3', 
    dataset=InsightDataset(
        data_type='Rectangle', 
        num_classes=1, 
        average_annotations=5.19, 
        total_assets=500, 
        settings=DatasetSettings(
            split_ratio=0.3, 
            shuffle=True, 
            seed=0, 
            using_sliding_window=False
        )
    ), 
    model=InsightModel(
        name='fasterrcnn-inceptionv2-1024x1024', 
        batch_size=2, 
        training_steps=5000, 
        max_detection_per_class=100, 
        solver='momentum', 
        learning_rate=0.04, 
        momentum=0.9
    ), 
    checkpoint=RunCheckpoint(
        strategy='STRAT_ALWAYS_SAVE_LATEST', 
        evaluation_interval=250, 
        metric=None
    ), 
    artifact=InsightArtifact(
        id='artifact_65ae274540259e2a07533532', 
        is_training=False, 
        step=5000, 
        metric=ArtifactMetric(
            total_loss=0.32356, 
            classification_loss=0.012036, 
            localization_loss=0.010706, 
            regularization_loss=0.0
        )
    ),
    create_date=1705912133684
)

Attribute	Type	Description
`flow_title`	`str`	Name of the workflow.
`run_id`	`str`	ID of the training run.
`dataset`	`dict`	Contains loss metrics for the following categories:
`step`	`int`	Total number of training steps.
`create_date`	`int`	UNIX timestamp of the training creation date.
`metric`	`dict`	Contains loss metrics for the following categories: `total_loss` `classification_loss` `localization_loss` `regularization_loss`
`statistic`	`dict`	Contains dataset statistics for the following categories: `average_annotations`: Average number of annotations per asset.
`optimizer`	`str`	Name of the optimizer used in the training.
`learning_rate`	`float`	Value of the learning rate used in the training.
`momentum`	`float`	Value of the momentum used in the training.
`epochs`	`int`	Total number of training epochs.
`batch_size`	`int`	Value of the batch size used in the training.
`model_name`	`str`	Name of the specific model architecture used in the training.
`max_detections_per_class`	`int`	Value to cap the maximum number of detections per class for the model.
`data_type`	`str`	Annotation data type.
`num_classes`	`int`	Total number of unique classes.
`split_ratio`	`float`	Train-test split ratio.
`shuffle`	`bool`	Whether the dataset was shuffled.
`seed`	`int`	Initialization seed for the training.
`checkpoint_every_n`	`int`	Epoch interval to generate checkpoints.
`metric_target`	`str`	Metric used to determine best checkpoint saved.

TrainingMetadata

Metadata for training runs.

Bases

dict

Applicable Functions

{
    "id": "run_63eb212ff0f856bf95085095",
    "object": "run",
    "project_id": "proj_cd067221d5a6e4007ccbb4afb5966535",
    "flow_id": "flow_63bbd3bf8a78eb906f417396",
    "status": {
        "conditions": [
            {
                "condition": "TrainingStarted",
                "last_updated": 1676353954729,
                "status": "finished"
            },
            {
                "condition": "TrainingFinished",
                "last_updated": 1676356061724,
                "status": "finished"
            }
        ],
        "last_updated": 1676356061724
    },
    "execution": {
        "accelerator": {
            "name": "GPU_T4",
            "count": 2
        },
        "checkpoint": {
            "strategy": "STRAT_LOWEST_VALIDATION_LOSS",
            "evaluation_interval": 250,
            "metric": "Loss/total_loss"
        }
    },
    "features": {
        "matrix": true,
        "preview": true
    },
    "create_date": 1676353954729,
    "last_modified_date": 1676356061724,
    "logs": [
        "log_63eb212ff0f856bf95085095"
    ]
}

Attribute	Type	Description
`id`	`str`	Training run ID.
`object`	`str`	Type of object.
`project_id`	`str`	ID of the project containing the training run.
`flow_id`	`str`	ID of the workflow used for the training run.
`status`	`dict`	Status of completion of the different stages in the training run, contains the following categories: `conditions`: List of dictionaries that describe the training conditions (`TrainingStarted`, `TrainingFinished`), last updated UNIX timestamp of the operation, and the status of completion. `last_updated`: UNIX timestamp of when the training statuses were last updated.
`execution`	`dict`	Contains training configuration parameters for the following categories: `accelerator`: Dictionary containing the type and number of GPUs used. `checkpoint`: Dictionary containing the checkpoint strategy, evaluation interval, and metric used to save the best checkpoint.
`features`	`dict`	Contains the activation status of certain advanced visualization features such as Evaluation Preview and Confusion Matrix.
`create_date`	`int`	UNIX timestamp of the training creation date.
`last_modified_date`	`int`	UNIX timestamp of the last modified date of the training.
`logs`	`list[str]`	List of training log IDs that can be used to view training logs via `datature.Run.log()`

LogMetadata

Metadata for training logs.

Bases

dict

Applicable Functions

datature.Run.log()

{
    "id": "log_63eb212ff0f856bf95085095",
    "object": "log",
    "event": [
        {
            "ev": "memoryUsage",
            "pl": {},
            "t": 1675669392000
        }
    ]
}

Attribute	Type	Description
`id`	`str`	Log ID.
`object`	`str`	Type of object.
`event`	`list[dict]`	List of training event logs containing the following categories: `ev`: Type of event tracked. `pl`: Detailed description of the logs. `t`: UNIX timestamp of the event.

Attribute

Type

Description

id

str

Log ID.

object

str

Type of object.

event

list[dict]

List of training event logs containing the following categories:

ev: Type of event tracked.
pl: Detailed description of the logs.
t: UNIX timestamp of the event.

ArtifactMetadata

Metadata for artifacts.

Bases

dict

Applicable Functions

{
    "id": "artifact_63bd140e67b42dc9f431ffe2",
    "object": "artifact",
    "is_training": false,
    "step": 3000,
    "flow_title": "Blood Cell Detector",
    "run_id": "run_63bd08d8cdf700575fa4dd01",
    "files": [
        {
            "name": "ckpt-13.data-00000-of-00001",
            "md5": "5a96886e53f98daae379787ee0f22bda"
        }
    ],
    "project_id": "proj_cd067221d5a6e4007ccbb4afb5966535",
    "artifact_name": "ckpt-13",
    "create_date": 1673335822851,
    "metric": {
        "total_loss": 0.548,
        "classification_loss": 0.511,
        "localization_loss": 0.006,
        "regularization_loss": 0.03
    },
    "is_deployed": false,
    "exports": ["onnx", "tflite"],
    "model_type": "efficientdet-d1-640x640",
    "exportable_formats": ["tensorflow", "tflite", "onnx", "pytorch"]
}

Attribute	Type	Description
`id`	`str`	Artifact ID.
`object`	`str`	Type of object.
`is_training`	`bool`	Whether the training is still running.
`step`	`int`	Total number of training steps.
`flow_title`	`str`	Title of the workflow.
`run_id`	`str`	ID of the training run of the current artifact.
`files`	`list[dict]`	List of artifact checkpoint files containing the following categories: `name`: Name of the checkpoint file. `md5`: MD5 hash value of the checkpoint file.
`project_id`	`str`	ID of the project containing the current artifact.
`artifact_name`	`str`	Checkpoint name of the artifact.
`create_date`	`int`	UNIX timestamp of the artifact creation date.
`metric`	`dict`	Dictionary containing the following metrics: `total_loss` `classification_loss` `localization_loss` `regularization_loss`
`is_deployed`	`bool`	Whether the current artifact has an active deployment.
`exports`	`list[str]`	List of model formats that the artifact has been exported in.
`model_type`	`str`	Model architecture name.
`exportable_formats`	`list[str]`	List of all exportable model formats for the artifact.

ExportedMetadata

Metadata of exported models.

Bases

dict

Applicable Functions

datature.Artifact.list_exported()

{
    "id": "model_d15aba68872b045e27ac3db06a401da3",
    "object": "model",
    "status": "Finished",
    "format": "tensorflow",
    "create_date": 1673336054173,
    "download": {
        "method": "GET",
        "expiry": 1673339505871,
        "url": "https://storage.googleapis.com/exports.datature.ioa2d89"
    }
}

Attribute	Type	Description
`id`	`str`	ID of the exported model.
`object`	`str`	Type of object.
`status`	`str`	Status of the model export.
`format`	`str`	Exported model format.
`create_date`	`int`	UNIX timestamp of the creation date of the exported model.
`download`	`dict`	Dictionary containing the download metadata of the exported model: `method`: Request method `expiry`: UNIX timestamp of the expiry of the download link. `url`: Download link of the exported model.

DeploymentMetadata

Metadata for active deployments.

Bases

dict

Applicable Functions

{
    "id": "deploy_30922d5e-b2f6-43dc-b7b4-e29e2c30fb45",
    "object": "deploy",
    "project_id": "proj_cd067221d5a6e4007ccbb4afb5966535",
    "model_id": "model_5cbcf0fb9f692621f9cfc1ea8f1d68c7",
    "scaling": {
        "mode": "fixed",
        "num_instances": 1
    },
    "status": {
        "overview": "AVAILABLE",
        "message": "Created service successfully",
        "status_date": 1673936127872
    },
    "create_date": 1673935589700,
    "last_modified_date": 1673936127872,
    "locked": false,
    "url": "https://inference.datature.io/neural/3092245/predict"
}

Attribute	Type	Description
`id`	`str`	ID of the active deployment.
`object`	`str`	Type of object.
`project_id`	`str`	Project ID containing the active deployment.
`model_id`	`str`	ID of the model used for the deployment.
`scaling`	`dict`	Dictionary containing the following categories: `mode`: Instance scaling mode of the deployment. `num_instances`: Number of instances of the deployment.
`status`	`dict`	Dictionary of the deployment status containing the following categories: `overview`: Overview status of the deployment. `message`: Status message of the deployment. `status_date`: UNIX timestamp of the last update of the deployment status.
`create_date`	`int`	UNIX timestamp of the creation date of the deployment.
`last_modified_date`	`int`	UNIX timestamp of the last modified date of the deployment.
`locked`	`bool`	Whether the deployment is locked.
`url`	`str`	API URL endpoint for prediction requests.

OperationMetadata

Metadata for background operations.

Bases

dict

Applicable Functions

datature.Operation.retrieve()

{
    "id": "op_508fc5d1-e908-486d-9e7b-1dca99b80024",
    "object": "operation",
    "op_link": "users/api|affaf/proje-1dca99b80024",
    "status": {
        "overview": "Queued",
        "message": "Operation queued",
        "time_updated": 1676621361765,
        "time_scheduled": 1676621361765,
        "progress": {
            "unit": "whole operation",
            "with_status": {
                "queued": 1,
                "running": 0,
                "finished": 0,
                "cancelled": 0,
                "errored": 0
            }
        }
    }
}

Attribute	Type	Description
`id`	`str`	Unique operation ID.
`object`	`str`	Type of object.
`op_link`	`str`	Operation link used to retrieve operation status.
`status`	`dict`	Operation status metadata.

OperationStatus

Metadata of operation status.

Bases

dict

{
    "overview": "Queued",
    "message": "Operation queued",
    "time_updated": 1676621361765,
    "time_scheduled": 1676621361765,
    "progress": {
        "unit": "whole operation",
        "with_status": {
            "queued": 1,
            "running": 0,
            "finished": 0,
            "cancelled": 0,
            "errored": 0
        }
    }
}

Attribute	Type	Description
`overview`	`str`	Overview status of current operation.
`message`	`str`	Status message of current operation.
`time_updated`	`int`	Last updated UNIX timestamp of current operation status.
`time_scheduled`	`int`	UNIX timestamp of when the operation was first scheduled.
`progress`	`dict`	Operation progress status indicator.