Managing Item Status in Dataloop 🏷️

Welcome to your guide to managing item status in Dataloop! Whether you're tracking annotation progress, managing QA workflows, or organizing your data, understanding item status is key. Let's dive in!

Understanding Item Status 🎓

Think of item status as traffic lights for your data workflow:

🟢 COMPLETE: Item is finished (for annotation tasks)
✅ APPROVE: Item passed review (for QA tasks)
⛔ DISCARD: Item should be excluded
⚪ NO STATUS: Item is available for work

Default Status Types

Annotation Tasks:
- COMPLETE: Work is finished
- DISCARD: Item should be excluded
QA Tasks:
- APPROVE: Item passed review
- DISCARD: Item should be excluded

Working with Item Status 🛠️

Setting Status for Single Items

import dtlpy as dl

# Get your project and resources
project = dl.projects.get(project_name='project_name')
dataset = project.datasets.get(dataset_name='dataset_name')
task = dataset.tasks.get(task_name='task_name')
assignment = dataset.assignments.get(assignment_name='assignment_name')
item = dataset.items.get(item_id='item-id')

# Set status in different contexts
item.update_status(status=dl.ItemStatus.COMPLETED, task_id=task.id)  # For task
item.update_status(status=dl.ItemStatus.APPROVED, assignment_id=assignment.id)  # For assignment
item.update_status(status=dl.ItemStatus.DISCARDED)  # For single-task items

Batch Status Updates 🔄

Method 1: Using Dataset Filters (Recommended for Multi-Task Items)

# Update status for annotated items
filters = dl.Filters(field='annotated', values=True)

# Option 1: Using item list
items = dataset.items.list(filters=filters)
dataset.items.update_status(
    status=dl.ItemStatus.APPROVED,
    items=items
)

# Option 2: Using filters directly
dataset.items.update_status(
    status=dl.ItemStatus.DISCARDED,
    filters=filters
)

# Option 3: Using item IDs
item_ids = ['id1', 'id2', 'id3']
dataset.items.update_status(
    status=dl.ItemStatus.COMPLETED,
    item_ids=item_ids
)

Method 2: Using Task Entity (Recommended for Single-Task Items)

# Option 1: Using filters
filters = dl.Filters(field='annotated', values=True)
item_ids = [item.id for item in dataset.items.list(filters=filters).all()]

# Option 2: Using specific IDs
item_ids = ['id1', 'id2', 'id3']

# Update status through task
task.set_status(
    status=dl.ItemStatus.APPROVED,
    operation='create',
    items=item_ids
)

Clearing Item Status ♻️

Need to make items available for work again? Here's how to clear their status:

# Clear status in different contexts
item.update_status(dl.ITEM_STATUS_DISCARDED, task_id=task.id, clear=True)  # For task
item.update_status(dl.ITEM_STATUS_APPROVED, assignment_id=assignment.id, clear=True)  # For assignment
item.update_status(dl.ITEM_STATUS_COMPLETED, clear=True)  # For single-task items

Custom Status Actions 🎨

Want to create tasks with custom status options? Here's how:

# Create task with custom status actions
task = dataset.tasks.create(
    task_name='custom_status_task',
    assignee_ids=['annotator1@dataloop.ai'],
    available_actions=[
        dl.ItemAction(
            action='needs_review',
            display_name='Needs Review'
        ),
        dl.ItemAction(
            action='skip_item',
            display_name='Skip for Now'
        )
    ]
)

Best Practices 💡

🎯 Choose the Right Method:
- Use dataset methods for items in multiple tasks
- Use task methods for single-task items
🔄 Batch Operations:
- Use filters for bulk updates when possible
- Group similar status updates together
📝 Status Management:
- Clear status when work needs to be redone
- Use custom statuses for specific workflow needs
- Document your status workflow
⚠️ Important Notes:
- Changing task statuses doesn't affect already-status-assigned items
- Always verify status updates with filters or item counts
- Consider using custom statuses for complex workflows

Need More Help? 🤔

Check out our Item Status Documentation
Visit our Task Management Guide

Happy status managing! 🚀