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Model Serving Toolkit
Deploy models with FastAPI, TensorFlow Serving, Triton, and BentoML. Includes A/B testing and canary patterns.
PythonYAMLTOMLJSONMarkdownFastAPI
📁 File Structure 7 files
model-serving-toolkit/
├── LICENSE
├── README.md
├── config.example.yaml
├── pyproject.toml
└── src/
└── model_serving_toolkit/
├── __init__.py
├── core.py
└── utils.py
📖 Documentation Preview README excerpt
Model Serving Toolkit
Deploy models with FastAPI, TensorFlow Serving, Triton, and BentoML. Includes A/B testing and canary patterns.
Contents
config.example.yamlpyproject.tomlsrc/model_serving_toolkit/__init__.pysrc/model_serving_toolkit/core.pysrc/model_serving_toolkit/utils.py
Quick Start
1. Extract the ZIP archive
2. Review the README and documentation
3. Customize configuration files for your environment
4. Follow the setup guide for your specific use case
Requirements
- Python 3.10+ (for Python scripts)
- Relevant CLI tools for your platform
- Access to your target environment
License
MIT License — see LICENSE file.
Support
Questions or issues? Email megafolder122122@hotmail.com
---
Part of [Ml Engineer](https://inity13.github.io/ml-engineer-toolkit/)
📄 Code Sample .py preview
src/model_serving_toolkit/core.py
"""
Model Serving Toolkit — Core Module
Production-ready implementation.
"""
from typing import Any, Dict, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
import json
import logging
logger = logging.getLogger(__name__)
@dataclass
class Config:
"""Configuration for Model Serving Toolkit."""
name: str = "model-serving-toolkit"
version: str = "1.0.0"
debug: bool = False
log_level: str = "INFO"
output_dir: str = "./output"
settings: Dict[str, Any] = field(default_factory=dict)
@classmethod
def from_file(cls, path: str) -> "Config":
with open(path) as f:
data = json.load(f)
return cls(**data)
def to_dict(self) -> Dict[str, Any]:
return {
"name": self.name,
"version": self.version,
"debug": self.debug,
"log_level": self.log_level,
"output_dir": self.output_dir,
"settings": self.settings,
}
class ModelServingToolkit:
"""Main class for Model Serving Toolkit."""
def __init__(self, config: Optional[Config] = None):
self.config = config or Config()
self._setup_logging()
self._results: List[Dict[str, Any]] = []
logger.info(f"Initialized {self.config.name} v{self.config.version}")
def _setup_logging(self):
# ... 40 more lines ...