> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sinkove.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick Start Guide

> Generate your first synthetic medical imaging dataset

## Creating a Synthetic Dataset

Follow these simple steps to generate your synthetic dataset using Sinkove's platform:

### Step 1: Select Model

Navigate to the **Generator** tab and choose a suitable generative model from the dropdown menu. Our models are optimized for different types of medical imaging scenarios.

<Note>
  Different models may specialize in specific imaging modalities or medical
  conditions. Choose the one that best fits your research requirements.
</Note>

### Step 2: Configure Dataset

Fill in the provided fields with the details of the medical images you wish to generate:

* **Prompts/Descriptions**: Enter relevant medical descriptions (e.g., "Severe cardiomegaly", "Normal chest X-ray")
* **Dataset Size**: Currently limited to approximately 1000 images
* **Additional Parameters**: Configure any model-specific settings as needed

<Tip>
  Use specific, clinical terminology in your prompts for the most accurate
  synthetic image generation.
</Tip>

### Step 3: Generate Data

You have two options for data generation:

#### Preview Generation

* Click **Generate sample** to preview a single synthetic image
* Review the quality and adjust parameters if needed
* This helps ensure the output matches your expectations

#### Full Dataset Generation

* Set your desired dataset size (up to \~1000 images)
* Click **Generate dataset** to initiate full dataset generation
* Monitor the progress in your dashboard

<Warning>
  Dataset generation may take several minutes to hours depending on the size and
  complexity of your request.
</Warning>

## Downloading Your Dataset

Once your dataset is ready, use our Python SDK for quick and efficient downloading:

```python theme={null}
import uuid
from sinkove import Client

# Initialize the client with your organization ID
# API key is read from SINKOVE_API_KEY environment variable
client = Client(uuid.UUID("your-organization-id"))

# Get your dataset by ID
dataset_id = uuid.UUID("your-dataset-id")
dataset = client.datasets.get(dataset_id)

# Check if dataset is ready
if dataset.ready:
    # Download the dataset
    dataset.download("./dataset.zip", strategy="replace")
    print("Dataset downloaded successfully!")
else:
    print(f"Dataset not ready. Current state: {dataset.state}")
    
    # Or wait for it to be ready
    dataset.wait()  # Blocks until ready
    dataset.download("./dataset.zip", strategy="replace")
    print("Dataset downloaded successfully!")
```

<Info>
  Need to set up the SDK? Check our [SDK Installation Guide](/essentials/sdk-installation) or jump straight to the [SDK Quick Start](/essentials/sdk-quickstart).
</Info>

## Quick Links

<CardGroup cols={2}>
  <Card title="SDK Installation" icon="download" href="/essentials/sdk-installation">
    Install and configure the Python SDK
  </Card>

  <Card title="SDK Quick Start" icon="rocket" href="/essentials/sdk-quickstart">
    Create your first dataset with code
  </Card>

  <Card title="API Keys" icon="key" href="/essentials/api-keys">
    Set up API authentication
  </Card>

  <Card title="SDK Examples" icon="code" href="/essentials/sdk-examples">
    Browse practical code examples
  </Card>
</CardGroup>

## Need Help?

If you encounter any issues or have questions:

* Check our [SDK API Reference](/essentials/sdk-reference) for technical details
* Contact our support team at [support@sinkove.com](mailto:support@sinkove.com)
* Browse [SDK Examples](/essentials/sdk-examples) for common use cases
* Review the complete [Python SDK documentation](/essentials/python-sdk)