Welcome to Dataloop SDK: Your AI Development Companion 🚀

Ready to supercharge your AI development? The Dataloop SDK is your all-in-one toolkit for managing the complete AI lifecycle. Think of it as your command center for everything from data management to production deployment!

What Can You Do? 🎯

With our powerful Python SDK, you'll have full control over:

📁 Projects - Your high-level workspaces
📊 Datasets - Your data collections
🖼️ Items - Your individual files
✏️ Annotations - Your data labels
📝 Metadata - Your custom data attributes

Getting Started: Your Journey Begins Here 🌟

Let's get you up and running with everything you need:

1. Setting Up Your Environment 🛠️

First, let's get the essential tools installed:

Python Installation 🐍

Visit Python Downloads
Get Python 3.8 or later
Follow the installation wizard

💡 Pro Tip: Make sure to check "Add Python to PATH" during installation!

Dataloop SDK Installation 📦

# Install using pip
pip install dtlpy

# Verify installation
pip show dtlpy  # Should show version 1.64.9 or later

2. Logging In: Your Gateway to Dataloop 🔑

import dtlpy as dl

# Smart login that handles token expiration
if dl.token_expired():
    dl.login()

For Long-Running Jobs: M2M Authentication 🤖

# Using API key (recommended for automation)
dl.login_api_key(api_key=os.environ['DTLPY_API_KEY'])

⚠️ Security Tip: Always store API keys as environment variables!

3. Creating Your First Project 🎨

# Create a new project
project = dl.projects.create(project_name='My-Awesome-Project')

# Or get an existing one
project = dl.projects.get(project_name='My-Awesome-Project')

4. Managing Team Access 👥

# Add a team member
project.add_member(
    email='teammate@company.com',
    role=dl.MemberRole.Developer
)

# Update roles
project.update_member(
    email='teammate@company.com',
    role=dl.MemberRole.Annotator
)

5. Working with Datasets 📊

# Create a new dataset
dataset = project.datasets.create(dataset_name='My-First-Dataset')

# Upload items
item = dataset.items.upload(
    local_path=r'C:\path\to\your\image.jpg'
)

🎯 Pro Tip: Use remote_path to organize files in folders!

6. Adding Annotations ✏️

# Create an annotation builder
builder = item.annotations.builder()

# Add a classification
builder.add(
    annotation_definition=dl.Classification(
        label='cat'
    )
)

# Add a bounding box
builder.add(
    annotation_definition=dl.Box(
        left=100,
        top=100,
        right=200,
        bottom=200,
        label='cat-face'
    )
)

# Save annotations
item.annotations.upload(builder)

7. Smart Filtering 🔍

# Create a filter
filters = dl.Filters()

# Find all items with 'cat' annotations
filters.add_join(field='label', values='cat')

# Get filtered items
items = dataset.items.list(filters=filters)
for item in items.all():
    print(f"Found cat in: {item.name}")

8. Working with Metadata 📝

# Add metadata to an item
item.metadata['user'] = {
    'photographer': 'John Doe',
    'location': 'New York',
    'date': '2024-03-20'
}
item = item.update()

# Filter by metadata
filters = dl.Filters()
filters.add(field='metadata.user.location', values='New York')

9. Creating Tasks 📋

# Create an annotation task
task = dataset.tasks.create(
    task_name='Annotate Cats',
    assignee_ids=['annotator@company.com'],
    due_date=datetime.datetime(2024, 12, 31).timestamp()
)

Best Practices 👑

Error Handling 🛡️

try:
    item = dataset.items.get(item_id='your-item-id')
except dl.exceptions.NotFound:
    print("Item not found!")

Batch Operations ⚡

# Upload multiple items
dataset.items.upload(
    local_path=r'C:\path\to\folder',
    local_annotations_path=r'C:\path\to\annotations'
)

Resource Cleanup 🧹
```
# Always logout when done
dl.logout()
```

Ready to Build Something Amazing? 🚀

You now have all the tools to:

Manage your AI projects
Organize your datasets
Label your data
Create annotation tasks
Track everything with metadata

Happy coding! 🎉

📚 Want to Learn More? Check out our full onboarding!