# WatsonX AutoDeploy
Automated deployment library for IBM WatsonX AI services with LangGraph support.
## Overview
WatsonX AutoDeploy simplifies the process of deploying AI services to IBM WatsonX by providing a clean, pythonic interface for:
- Creating custom environments with specified dependencies
- Building software specifications
- Storing and deploying AI services
- Managing the entire deployment lifecycle
## Installation
### From PyPI (when published)
```bash
pip install watsonx-autodeploy
```
### From Source
```bash
git clone https://github.com/nicknochnack/watsonx-autodeploy.git
cd watsonx-autodeploy
pip install -e .
```
### Development Installation
```bash
git clone https://github.com/nicknochnack/watsonx-autodeploy.git
cd watsonx-autodeploy
pip install -e ".[dev]"
```
## Quick Start
### Environment Setup
First, set up your environment variables:
```bash
# .env file
WATSONX_URL=your_watsonx_url
WATSONX_APIKEY=your_api_key
WATSONX_SPACEID=your_space_id
MODEL_ID=your_model_id
```
### Basic Usage
```python
import os
from dotenv import load_dotenv
from autodeploy import Deployer
load_dotenv()
def my_ai_service(context, **kwargs):
# Your AI service implementation
def generate(context):
return {"body": {"message": "Hello from WatsonX!"}}
def generate_stream(context):
pass
return generate, generate_stream
# Deploy with one line
deployer = Deployer()
deployer.autodeploy(my_ai_service)
```
### Advanced Usage
```python
from autodeploy import Deployer
deployer = Deployer()
# Step-by-step deployment with custom parameters
deployer.export_config(
python_version="3.11",
channels="conda-forge",
dependencies=["custom-package==1.0.0"],
prefix="/opt/anaconda3/envs/custom"
)
deployer.build_environment(
environment_name="my-custom-env",
base_runtime="runtime-24.1-py3.11"
)
deployer.build_software_spec(
spec_name="my-spec",
spec_description="Custom specification"
)
deployer.store_service(
deployable_ai_service=my_ai_service,
service_name="my-service"
)
deployer.deploy_service(deployment_name="production-deployment")
```
## API Reference
### Deployer Class
The main class for managing WatsonX deployments.
#### `__init__()`
Initializes the deployer with WatsonX credentials from environment variables.
#### `autodeploy(deployable_function)`
One-step deployment method that handles the entire deployment pipeline.
**Parameters:**
- `deployable_function`: The AI service function to deploy
#### `export_config(python_version=None, channels=None, dependencies=None, prefix=None)`
Creates a conda environment configuration file.
**Parameters:**
- `python_version` (str, optional): Python version (default: "3.11")
- `channels` (str, optional): Conda channels (default: "empty")
- `dependencies` (list, optional): List of pip dependencies
- `prefix` (str, optional): Environment prefix path
#### `build_environment(python_version=None, environment_name=None, base_runtime=None)`
Creates a package extension for the environment.
**Parameters:**
- `python_version` (str, optional): Python version
- `environment_name` (str, optional): Name for the environment
- `base_runtime` (str, optional): Base runtime specification
#### `build_software_spec(spec_name=None, spec_description=None)`
Creates a software specification.
**Parameters:**
- `spec_name` (str, optional): Name for the software specification
- `spec_description` (str, optional): Description of the specification
#### `store_service(deployable_ai_service, service_name=None)`
Stores the AI service in WatsonX repository.
**Parameters:**
- `deployable_ai_service`: The AI service function
- `service_name` (str, optional): Name for the service
#### `deploy_service(deployment_name=None)`
Deploys the stored AI service.
**Parameters:**
- `deployment_name` (str, optional): Name for the deployment
## Examples
See the `examples/` directory for complete usage examples:
- `examples/basic_usage.py` - Simple one-line deployment
- `examples/advanced_usage.py` - Step-by-step deployment with custom parameters
## Requirements
- Python 3.11+
- IBM WatsonX AI account and credentials
- Required dependencies (automatically installed):
- ibm-watsonx-ai>=1.3.34
- langchain>=0.3.27
- langchain-ibm>=0.3.15
- langgraph>=0.6.5
- python-dotenv>=1.1.1
## Development
### Running Tests
```bash
pytest
```
### Code Formatting
```bash
black src/ examples/
```
### Type Checking
```bash
mypy src/
```
## Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Run the test suite
6. Submit a pull request
## License
MIT License. See LICENSE file for details.
## Support
For issues and questions:
- Open an issue on GitHub
## Changelog
### v0.1.0
- Initial release
- Basic deployment functionality
- Support for custom environments and dependencies
- One-step autodeploy feature
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"description": "# WatsonX AutoDeploy\n\nAutomated deployment library for IBM WatsonX AI services with LangGraph support.\n\n## Overview\n\nWatsonX AutoDeploy simplifies the process of deploying AI services to IBM WatsonX by providing a clean, pythonic interface for:\n\n- Creating custom environments with specified dependencies\n- Building software specifications \n- Storing and deploying AI services\n- Managing the entire deployment lifecycle\n\n## Installation\n\n### From PyPI (when published)\n```bash\npip install watsonx-autodeploy\n```\n\n### From Source\n```bash\ngit clone https://github.com/nicknochnack/watsonx-autodeploy.git\ncd watsonx-autodeploy\npip install -e .\n```\n\n### Development Installation\n```bash\ngit clone https://github.com/nicknochnack/watsonx-autodeploy.git\ncd watsonx-autodeploy\npip install -e \".[dev]\"\n```\n\n## Quick Start\n\n### Environment Setup\n\nFirst, set up your environment variables:\n\n```bash\n# .env file\nWATSONX_URL=your_watsonx_url\nWATSONX_APIKEY=your_api_key\nWATSONX_SPACEID=your_space_id\nMODEL_ID=your_model_id\n```\n\n### Basic Usage\n\n```python\nimport os\nfrom dotenv import load_dotenv\nfrom autodeploy import Deployer\n\nload_dotenv()\n\ndef my_ai_service(context, **kwargs):\n # Your AI service implementation\n def generate(context):\n return {\"body\": {\"message\": \"Hello from WatsonX!\"}}\n \n def generate_stream(context):\n pass\n \n return generate, generate_stream\n\n# Deploy with one line\ndeployer = Deployer()\ndeployer.autodeploy(my_ai_service)\n```\n\n### Advanced Usage\n\n```python\nfrom autodeploy import Deployer\n\ndeployer = Deployer()\n\n# Step-by-step deployment with custom parameters\ndeployer.export_config(\n python_version=\"3.11\",\n channels=\"conda-forge\",\n dependencies=[\"custom-package==1.0.0\"],\n prefix=\"/opt/anaconda3/envs/custom\"\n)\n\ndeployer.build_environment(\n environment_name=\"my-custom-env\",\n base_runtime=\"runtime-24.1-py3.11\"\n)\n\ndeployer.build_software_spec(\n spec_name=\"my-spec\",\n spec_description=\"Custom specification\"\n)\n\ndeployer.store_service(\n deployable_ai_service=my_ai_service,\n service_name=\"my-service\"\n)\n\ndeployer.deploy_service(deployment_name=\"production-deployment\")\n```\n\n## API Reference\n\n### Deployer Class\n\nThe main class for managing WatsonX deployments.\n\n#### `__init__()`\nInitializes the deployer with WatsonX credentials from environment variables.\n\n#### `autodeploy(deployable_function)`\nOne-step deployment method that handles the entire deployment pipeline.\n\n**Parameters:**\n- `deployable_function`: The AI service function to deploy\n\n#### `export_config(python_version=None, channels=None, dependencies=None, prefix=None)`\nCreates a conda environment configuration file.\n\n**Parameters:**\n- `python_version` (str, optional): Python version (default: \"3.11\")\n- `channels` (str, optional): Conda channels (default: \"empty\") \n- `dependencies` (list, optional): List of pip dependencies\n- `prefix` (str, optional): Environment prefix path\n\n#### `build_environment(python_version=None, environment_name=None, base_runtime=None)`\nCreates a package extension for the environment.\n\n**Parameters:**\n- `python_version` (str, optional): Python version\n- `environment_name` (str, optional): Name for the environment\n- `base_runtime` (str, optional): Base runtime specification\n\n#### `build_software_spec(spec_name=None, spec_description=None)`\nCreates a software specification.\n\n**Parameters:**\n- `spec_name` (str, optional): Name for the software specification\n- `spec_description` (str, optional): Description of the specification\n\n#### `store_service(deployable_ai_service, service_name=None)`\nStores the AI service in WatsonX repository.\n\n**Parameters:**\n- `deployable_ai_service`: The AI service function\n- `service_name` (str, optional): Name for the service\n\n#### `deploy_service(deployment_name=None)`\nDeploys the stored AI service.\n\n**Parameters:**\n- `deployment_name` (str, optional): Name for the deployment\n\n## Examples\n\nSee the `examples/` directory for complete usage examples:\n\n- `examples/basic_usage.py` - Simple one-line deployment\n- `examples/advanced_usage.py` - Step-by-step deployment with custom parameters\n\n## Requirements\n\n- Python 3.11+\n- IBM WatsonX AI account and credentials\n- Required dependencies (automatically installed):\n - ibm-watsonx-ai>=1.3.34\n - langchain>=0.3.27\n - langchain-ibm>=0.3.15\n - langgraph>=0.6.5\n - python-dotenv>=1.1.1\n\n## Development\n\n### Running Tests\n```bash\npytest\n```\n\n### Code Formatting\n```bash\nblack src/ examples/\n```\n\n### Type Checking\n```bash\nmypy src/\n```\n\n## Contributing\n\n1. 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