Effortless ML Training in GCP: Create and Distribute Custom Python Distribution Package

Here is a step-by-step guide to creating a Python .tar.gz file for your ML code. This file can be uploaded to Google Cloud Platform (GCP) for custom ML training:

1. Organize Your Code:

• Create a new directory for your project, if you haven’t already.
• Inside the project directory, organize your code into subdirectories as needed. A common structure might include subdirectories like src for source code and data for data files.

project_name/
│
├── src/
│   ├── __init__.py
│   ├── module1.py
│   ├── module2.py
│   └── ...
│
├── data/
│   ├── dataset1.csv
│   ├── dataset2.csv
│   └── ...
│
├── tests/
│   ├── __init__.py
│   ├── test_module1.py
│   ├── test_module2.py
│   └── ...
│
├── docs/
│   └── ...
│
├── setup.py
├── MANIFEST.in (optional)
└── README.md

2. Create a setup.py Script:

• Create a setup.py script at the root of your project directory.
• This script should include metadata about your project and instructions for how to build and install it. Here’s a basic example:

from setuptools import find_packages
from setuptools import setup

setup(
    name='irys-market-predictor',
    version='0.1',
    packages=find_packages()…