watsonx-autodeploy 0.1.2

Automated deployment library for IBM WatsonX AI services with LangGraph support

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)

pip install watsonx-autodeploy

From Source

git clone https://github.com/nicknochnack/watsonx-autodeploy.git
cd watsonx-autodeploy
pip install -e .

Development Installation

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:

# .env file
WATSONX_URL=your_watsonx_url
WATSONX_APIKEY=your_api_key
WATSONX_SPACEID=your_space_id
MODEL_ID=your_model_id

Basic Usage

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

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

pytest

Code Formatting

black src/ examples/

Type Checking

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