Result Types

Workspace

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

Click to expand

Applicable Functions

Attributes

Workspace(
    id="ws_1c8aab980f174b0296c7e35e88665b13",
    name="Raighne's Workspace",
    owner="user_6323fea23e292439f31c58cd",
    tier="Developer",
    create_date=1701927649302
)
NameTypeDescription
idstrUnique ID of the current workspace.
namestrName 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.
ownerstrUnique user ID of the workspace owner. This is tied to the email used to sign up for the Nexus account.
tierstrCurrent workspace tier that determines access to advanced features and increased resource quotas. To enjoy these benefits, check out how to Upgrade Your Plan.
create_dateintUNIX timestamp of workspace creation date.

Project

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

Click to expand

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
    )
)
AttributeTypeDescription
idstrProject ID.
namestrProject name.
workspace_idstrWorkspace ID of the project.
typestrProject task type, one of [Classification, ObjectDetection, Segmentation]
create_dateintUNIX timestamp of project creation date.
localizationstrRegion(s) for data localization.
tagslist[str]List of tag names.
groupslist[str]List of assets groups.
statisticdictContains 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.

Click to expand

Applicable Functions

Attributes

Statistic(
    tags_count=[TagsCountItem(name="cat faces", count=0)],
    total_assets=28,
    annotated_assets=0,
    total_annotations=0
)
NameTypeDescription
tags_countlist[TagCountItem]List of TagCountItem objects representing tag counts for each tag in the project.
total_assetsintTotal number of assets in the project.
annotated_assetsintTotal number of annotated assets in the project.
total_annotationsintTotal number of annotations in the project.

User

User metadata.

Applicable Functions

ProjectUser(
    id='user_6323fea23e292439f31c58cd',
    access_type='Owner',
    email='raighne@datature.io',
    nickname='raighne',
    picture='https://s.gravatar.com/avatar/avatars%2Fra.png'
)
AttributeTypeDescription
idstrUser ID.
access_typestrThe access type of the current project, one of [Owner, Collaborator, Labeller]

.
emailstrUser email.
nicknamestrUser nickname.
picturestrUser 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
    )
)
AttributeTypeDescription
idstrAsset ID.
filenamestrFile name of the asset.
projectstrProject ID in which the asset is contained.
statusstrThe status of the asset, enum: [Annotated, Review, Completed, Tofix, None]
create_dateintUNIX timestamp of when the asset was uploaded.
urlstrURL to the raw asset file.
metadatadictAsset metadata.
statisticdictAsset 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'}
)
AttributeTypeDescription
file_sizeintSize of the asset in bytes.
mime_typestrMedia type and format of the asset.
heightintPixel height of the asset.
widthintPixel width of the asset.
groupsList[str]The groups of the asset.
custom_metadatadictThe 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
)
AttributeTypeDescription
tags_countlist[dict]List of tag counts.
total_annotationsintTotal number of annotations in the asset.

GroupStatistics

Asset group statistics.

Applicable Functions

[
    AssetGroup(
        group='1', 
        statistic=AssetGroupStatistic(
            total_assets=1, 
            annotated_assets=0, 
            reviewed_assets=0, 
            to_fixed_assets=0, 
            completed_assets=0
        )
    )
]
AttributeTypeDescription
groupstrName of the asset group.
statisticdictContains 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
)
NameTypeDescription
namestrTag name.
countintTotal 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]
    ]
)
AttributeTypeDescription
idstrUnique ID of the annotation.
project_idstrID of the project containing the annotation.
asset_idstrID of the asset containing the annotation.
tagstrTag name of the annotation.
bound_typestrBound type of the annotation, one of [rectangle, polygon].
boundlist[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
)
AttributeTypeDescription
idstrID of the workflow.
titlestrName of the workflow.
project_idstrProject ID containing the workflow.
update_dateintLast updated UNIX timestamp of the workflow.

TrainingInsight

Insight metadata for a training run.

Applicable Functions

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
)
AttributeTypeDescription
flow_titlestrName of the workflow.
run_idstrID of the training run.
datasetdictContains loss metrics for the following categories:
stepintTotal number of training steps.
create_dateintUNIX timestamp of the training creation date.
metricdictContains loss metrics for the following categories:
- total_loss
- classification_loss
- localization_loss
- regularization_loss
statisticdictContains dataset statistics for the following categories:
- average_annotations: Average number of annotations per asset.
optimizerstrName of the optimizer used in the training.
learning_ratefloatValue of the learning rate used in the training.
momentumfloatValue of the momentum used in the training.
epochsintTotal number of training epochs.
batch_sizeintValue of the batch size used in the training.
model_namestrName of the specific model architecture used in the training.
max_detections_per_classintValue to cap the maximum number of detections per class for the model.
data_typestrAnnotation data type.
num_classesintTotal number of unique classes.
split_ratiofloatTrain-test split ratio.
shuffleboolWhether the dataset was shuffled.
seedintInitialization seed for the training.
checkpoint_every_nintEpoch interval to generate checkpoints.
metric_targetstrMetric 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"
    ]
}
AttributeTypeDescription
idstrTraining run ID.
objectstrType of object.
project_idstrID of the project containing the training run.
flow_idstrID of the workflow used for the training run.
statusdictStatus 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.
executiondictContains 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.
featuresdictContains the activation status of certain advanced visualization features such as Evaluation Preview and Confusion Matrix.
create_dateintUNIX timestamp of the training creation date.
last_modified_dateintUNIX timestamp of the last modified date of the training.
logslist[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

{
    "id": "log_63eb212ff0f856bf95085095",
    "object": "log",
    "event": [
        {
            "ev": "memoryUsage",
            "pl": {},
            "t": 1675669392000
        }
    ]
}
AttributeTypeDescription
idstrLog ID.
objectstrType of object.
eventlist[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"]
}
AttributeTypeDescription
idstrArtifact ID.
objectstrType of object.
is_trainingboolWhether the training is still running.
stepintTotal number of training steps.
flow_titlestrTitle of the workflow.
run_idstrID of the training run of the current artifact.
fileslist[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_idstrID of the project containing the current artifact.
artifact_namestrCheckpoint name of the artifact.
create_dateintUNIX timestamp of the artifact creation date.
metricdictDictionary containing the following metrics:
- total_loss
- classification_loss
- localization_loss
- regularization_loss
is_deployedboolWhether the current artifact has an active deployment.
exportslist[str]List of model formats that the artifact has been exported in.
model_typestrModel architecture name.
exportable_formatslist[str]List of all exportable model formats for the artifact.

ExportedMetadata

Metadata of exported models.

Bases

dict

Applicable Functions

{
    "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"
    }
}
AttributeTypeDescription
idstrID of the exported model.
objectstrType of object.
statusstrStatus of the model export.
formatstrExported model format.
create_dateintUNIX timestamp of the creation date of the exported model.
downloaddictDictionary 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"
}
AttributeTypeDescription
idstrID of the active deployment.
objectstrType of object.
project_idstrProject ID containing the active deployment.
model_idstrID of the model used for the deployment.
scalingdictDictionary containing the following categories:
- mode: Instance scaling mode of the deployment.
- num_instances: Number of instances of the deployment.
statusdictDictionary 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_dateintUNIX timestamp of the creation date of the deployment.
last_modified_dateintUNIX timestamp of the last modified date of the deployment.
lockedboolWhether the deployment is locked.
urlstrAPI URL endpoint for prediction requests.

OperationMetadata

Metadata for background operations.

Bases

dict

Applicable Functions

{
    "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
            }
        }
    }
}
AttributeTypeDescription
idstrUnique operation ID.
objectstrType of object.
op_linkstrOperation link used to retrieve operation status.
statusdictOperation 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
        }
    }
}
AttributeTypeDescription
overviewstrOverview status of current operation.
messagestrStatus message of current operation.
time_updatedintLast updated UNIX timestamp of current operation status.
time_scheduledintUNIX timestamp of when the operation was first scheduled.
progressdictOperation progress status indicator.