Machine learning (ML) models are becoming increasingly popular in various industries, and as a result, the need to distribute and deploy these models in a secure, portable, and efficient manner has become crucial. One way to accomplish this is by creating command line tools from ML models. Command line tools are easy to use, efficient and can be integrated with other tools and workflows.
This article will cover the following topics:
- Why use command line tools for ML models
- Creating a command line tool using argparse
- Integrating the model into the command line tool
- Packaging the command line tool as a Python PEX file
- Examples of command line tools for ML models
Why use command line tools for ML models
Command line tools are a simple and efficient way to interact with an ML model. They can be easily integrated into existing systems and workflows, and can be run on a variety of platforms, including Windows, Linux, and Mac. Additionally, command line tools are easy to use and can be invoked from the command line, making them accessible to non-technical users.
Creating a command line tool using argparse
argparse is a built-in Python library that makes it easy to create command line tools. It allows you to define the arguments that the tool takes and provides a simple interface for parsing those arguments.
import argparse parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument('integers', metavar='N', type=int, nargs='+', help='an integer for the accumulator') parser.add_argument('--sum', dest='accumulate', action='store_const', const=sum, default=max, help='sum the integers (default: find the max)') args = parser.parse_args() print(args.accumulate(args.integers))
This example creates a command line tool that takes a list of integers and an optional "--sum" flag. If the "--sum" flag is provided, the tool will sum the integers, otherwise, it will find the max.
Integrating the model into the command line tool
Once you have created the command line interface using argparse, the next step is to integrate the ML model into the tool. This can be done by importing the model and using it to process the input arguments.
import mymodel def process_input(inputs): result = mymodel.predict(inputs) print(result) if __name__ == '__main__': args = parser.parse_args() process_input(args.inputs)
In this example, the
mymodel module is imported and its
predict function is used to process the inputs passed in through the command line
Packaging the command line tool as a Python PEX file
Once you have created the command line tool and integrated the ML model, the next step is to package it as a Python PEX (Python executable) file. This can be done using the pex library. PEX files are self-contained and do not require a specific environment or dependencies to be installed on the machine, making it easy to distribute and run the tool on different systems. Additionally, PEX files can be optimized for performance and can be signed, which allows users to verify the authenticity of the file.
To create a PEX file, you first need to install the pex library:
pip install pex
Then, you can use the following command to create a PEX file for your command line tool:
pex . -o mytool.pex
This will create a PEX file called "mytool.pex" in the current directory, which can be distributed and run on any system with Python installed.
Examples of command line tools for ML models
Here are a few examples of command line tools for ML models:
TensorFlow Serving: TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It allows you to easily deploy new versions of your model and roll back to previous versions if needed.
Keras-CLI: Keras-CLI is a command line interface for Keras, a popular deep learning framework. It allows you to easily train and evaluate models, and also provides a simple interface for making predictions.
Scikit-learn Command Line Interface: sklearn-cli is a command line interface for scikit-learn, a popular machine learning library. It allows you to easily train and evaluate models, and also provides a simple interface for making predictions.
In conclusion, creating command line tools from ML models is a simple and efficient way to distribute and deploy models. By using
pex libraries, it is easy to create a command line interface and package it in a portable and efficient way. These tools can be integrated into existing systems and workflows, and can be run on a variety of platforms.