# Superb AI On-premise SDK
Python SDK for Superb AI's On-premise solution. This SDK provides a simple interface to interact with your on-premise Superb AI installation.
## Installation
```bash
pip install superb-ai-onprem
```
## Quick Start
```python
from spb_onprem import DatasetService, DataService
from spb_onprem.data.enums import DataType
# Initialize services
dataset_service = DatasetService()
data_service = DataService()
# Create a dataset
dataset = dataset_service.create_dataset(
name="my-dataset",
description="My first dataset"
)
# Upload an image with annotation
with open("image.jpg", "rb") as f:
image_data = BytesIO(f.read())
data = data_service.create_image_data(
dataset_id=dataset.id,
key="image_1",
image_content=image_data,
annotation={
"labels": ["car", "person"],
"boxes": [
{"x": 100, "y": 100, "width": 200, "height": 200}
]
}
)
```
## Features
- Dataset Management
- Create, update, and delete datasets
- List and filter datasets
- Data Management
- Upload images with annotations
- Update annotations
- Add/remove data from slices
- Manage metadata
- Slice Management
- Create and manage data slices
- Filter and organize your data
## Usage Examples
### Dataset Operations
```python
from spb_onprem import DatasetService
from spb_onprem import DatasetsFilter, DatasetsFilterOptions
# Initialize service
dataset_service = DatasetService()
# Create a dataset
dataset = dataset_service.create_dataset(
name="my-dataset",
description="Dataset description"
)
# List datasets with filtering
filter = DatasetsFilter(
must_filter=DatasetsFilterOptions(
name_contains="test"
)
)
datasets = dataset_service.get_datasets(filter=filter)
```
### Data Operations
```python
from spb_onprem import DataService
from spb_onprem import DataListFilter, DataFilterOptions
# Initialize service
data_service = DataService()
# List data with filtering
filter = DataListFilter(
must_filter=DataFilterOptions(
key_contains="image_",
annotation_exists=True
)
)
data_list = data_service.get_data_list(
dataset_id="your-dataset-id",
filter=filter
)
# Update annotation
data_service.update_annotation(
dataset_id="your-dataset-id",
data_id="your-data-id",
annotation={
"labels": ["updated_label"],
"boxes": [...]
}
)
```
### Slice Operations
```python
from spb_onprem import SliceService
# Initialize service
slice_service = SliceService()
# Create a slice
slice = slice_service.create_slice(
dataset_id="your-dataset-id",
name="validation-set",
description="Validation data slice"
)
# Add data to slice
data_service.add_data_to_slice(
dataset_id="your-dataset-id",
data_id="your-data-id",
slice_id=slice.id
)
```
## Error Handling
The SDK provides specific error types for different scenarios:
```python
from spb_onprem.exceptions import (
BadParameterError,
NotFoundError,
UnknownError
)
try:
dataset = dataset_service.get_dataset(dataset_id="non-existent-id")
except NotFoundError:
print("Dataset not found")
except BadParameterError as e:
print(f"Invalid parameter: {e}")
except UnknownError as e:
print(f"An unexpected error occurred: {e}")
```
## Configuration
The SDK supports two authentication methods:
### 1. Config File Authentication (Default)
Create a config file at `~/.spb/onprem-config`:
```ini
[default]
host=https://your-onprem-host
access_key=your-access-key
access_key_secret=your-access-key-secret
```
This is the default authentication method when `SUPERB_SYSTEM_SDK=false` or not set.
### 2. Environment Variables (for Airflow DAGs)
When running in an Airflow DAG or other system environments, you can use environment variables for authentication. This method is activated by setting `SUPERB_SYSTEM_SDK=true`.
Required environment variables:
```bash
# Enable system SDK mode
export SUPERB_SYSTEM_SDK=true
# Set the host URL (either one is required)
export SUPERB_SYSTEM_SDK_HOST=https://your-superb-ai-host
# or
export SUNRISE_SERVER_URL=https://your-superb-ai-host
# Set the user email
export SUPERB_SYSTEM_SDK_USER_EMAIL=user@example.com
```
You can set these environment variables:
- Directly in your shell
- In your Airflow DAG configuration
- Through your deployment environment
- Using a `.env` file with your preferred method of loading environment variables
Note:
- When `SUPERB_SYSTEM_SDK=true`, the SDK will ignore the config file (`~/.spb/onprem-config`) and use environment variables exclusively.
- When `SUPERB_SYSTEM_SDK=false` or not set, the SDK will look for authentication credentials in `~/.spb/onprem-config`.
## Requirements
- Python >= 3.7
- requests >= 2.22.0
- urllib3 >= 1.21.1
- pydantic >= 1.8.0
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Support
For support or feature requests, please contact the Superb AI team or create an issue in this repository.
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"description": "# Superb AI On-premise SDK\n\nPython SDK for Superb AI's On-premise solution. This SDK provides a simple interface to interact with your on-premise Superb AI installation.\n\n## Installation\n\n```bash\npip install superb-ai-onprem\n```\n\n## Quick Start\n\n```python\nfrom spb_onprem import DatasetService, DataService\nfrom spb_onprem.data.enums import DataType\n\n# Initialize services\ndataset_service = DatasetService()\ndata_service = DataService()\n\n# Create a dataset\ndataset = dataset_service.create_dataset(\n name=\"my-dataset\",\n description=\"My first dataset\"\n)\n\n# Upload an image with annotation\nwith open(\"image.jpg\", \"rb\") as f:\n image_data = BytesIO(f.read())\n\ndata = data_service.create_image_data(\n dataset_id=dataset.id,\n key=\"image_1\",\n image_content=image_data,\n annotation={\n \"labels\": [\"car\", \"person\"],\n \"boxes\": [\n {\"x\": 100, \"y\": 100, \"width\": 200, \"height\": 200}\n ]\n }\n)\n```\n\n## Features\n\n- Dataset Management\n - Create, update, and delete datasets\n - List and filter datasets\n- Data Management\n - Upload images with annotations\n - Update annotations\n - Add/remove data from slices\n - Manage metadata\n- Slice Management\n - Create and manage data slices\n - Filter and organize your data\n\n## Usage Examples\n\n### Dataset Operations\n\n```python\nfrom spb_onprem import DatasetService\nfrom spb_onprem import DatasetsFilter, DatasetsFilterOptions\n\n# Initialize service\ndataset_service = DatasetService()\n\n# Create a dataset\ndataset = dataset_service.create_dataset(\n name=\"my-dataset\",\n description=\"Dataset description\"\n)\n\n# List datasets with filtering\nfilter = DatasetsFilter(\n must_filter=DatasetsFilterOptions(\n name_contains=\"test\"\n )\n)\ndatasets = dataset_service.get_datasets(filter=filter)\n```\n\n### Data Operations\n\n```python\nfrom spb_onprem import DataService\nfrom spb_onprem import DataListFilter, DataFilterOptions\n\n# Initialize service\ndata_service = DataService()\n\n# List data with filtering\nfilter = DataListFilter(\n must_filter=DataFilterOptions(\n key_contains=\"image_\",\n annotation_exists=True\n )\n)\ndata_list = data_service.get_data_list(\n dataset_id=\"your-dataset-id\",\n filter=filter\n)\n\n# Update annotation\ndata_service.update_annotation(\n dataset_id=\"your-dataset-id\",\n data_id=\"your-data-id\",\n annotation={\n \"labels\": [\"updated_label\"],\n \"boxes\": [...]\n }\n)\n```\n\n### Slice Operations\n\n```python\nfrom spb_onprem import SliceService\n\n# Initialize service\nslice_service = SliceService()\n\n# Create a slice\nslice = slice_service.create_slice(\n dataset_id=\"your-dataset-id\",\n name=\"validation-set\",\n description=\"Validation data slice\"\n)\n\n# Add data to slice\ndata_service.add_data_to_slice(\n dataset_id=\"your-dataset-id\",\n data_id=\"your-data-id\",\n slice_id=slice.id\n)\n```\n\n## Error Handling\n\nThe SDK provides specific error types for different scenarios:\n\n```python\nfrom spb_onprem.exceptions import (\n BadParameterError,\n NotFoundError,\n UnknownError\n)\n\ntry:\n dataset = dataset_service.get_dataset(dataset_id=\"non-existent-id\")\nexcept NotFoundError:\n print(\"Dataset not found\")\nexcept BadParameterError as e:\n print(f\"Invalid parameter: {e}\")\nexcept UnknownError as e:\n print(f\"An unexpected error occurred: {e}\")\n```\n\n## Configuration\n\nThe SDK supports two authentication methods:\n\n### 1. Config File Authentication (Default)\n\nCreate a config file at `~/.spb/onprem-config`:\n\n```ini\n[default]\nhost=https://your-onprem-host\naccess_key=your-access-key\naccess_key_secret=your-access-key-secret\n```\n\nThis is the default authentication method when `SUPERB_SYSTEM_SDK=false` or not set.\n\n### 2. Environment Variables (for Airflow DAGs)\n\nWhen running in an Airflow DAG or other system environments, you can use environment variables for authentication. This method is activated by setting `SUPERB_SYSTEM_SDK=true`.\n\nRequired environment variables:\n```bash\n# Enable system SDK mode\nexport SUPERB_SYSTEM_SDK=true\n\n# Set the host URL (either one is required)\nexport SUPERB_SYSTEM_SDK_HOST=https://your-superb-ai-host\n# or\nexport SUNRISE_SERVER_URL=https://your-superb-ai-host\n\n# Set the user email\nexport SUPERB_SYSTEM_SDK_USER_EMAIL=user@example.com\n```\n\nYou can set these environment variables:\n- Directly in your shell\n- In your Airflow DAG configuration\n- Through your deployment environment\n- Using a `.env` file with your preferred method of loading environment variables\n\nNote: \n- When `SUPERB_SYSTEM_SDK=true`, the SDK will ignore the config file (`~/.spb/onprem-config`) and use environment variables exclusively.\n- When `SUPERB_SYSTEM_SDK=false` or not set, the SDK will look for authentication credentials in `~/.spb/onprem-config`.\n\n## Requirements\n\n- Python >= 3.7\n- requests >= 2.22.0\n- urllib3 >= 1.21.1\n- pydantic >= 1.8.0\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Support\n\nFor support or feature requests, please contact the Superb AI team or create an issue in this repository.\n\n",
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