python-pod-manager 1.0.0b2

A pod manager for version dependency python projects.

Python Pod Manager

Python Pod Manager is a library designed to manage Dockerized environments for Python projects with flexible dependency management and deployment options. It provides a way to run applications with specific library versions in an isolated pod environment, avoiding conflicts with the host system.

Overview

With Python Pod Manager, you can configure a pod to install and run all necessary libraries without impacting the libraries on your local machine. This setup makes it easy to share your requirements.txt file and configuration with other users, ensuring they can reproduce the same environment without modifying their local libraries.

Configuration File Structure

Here is an example configuration file (config.yaml) with explanations for each section:

docker: 
  image_name: "python:latest"          # Docker image to use
  working_dir: "/app"                  # Working directory inside the container

libraries:
  requirements_file: "requirements.txt"  # Path to requirements file name
  path: "/path/to/your/libraries"        # Path to the requirements_file. 

  # Or like this:
  - name: "numpy" # Library name
    version: "2.1.0" # Library Version (Optional)
  - name: "pandas"
    version: "1.3.3"

pod:
  version: "latest"                  # Pod version to use
  use_single_pod: false              # Use a single pod (true) or multiple (false)
  custom_pod_name: "my_custom_pod"   # Custom name for the pod

custom_commands:
  - name: "RUN"                      # Command to install packages within the container
    command: "pip install {packages} --target /app/lib"
  - name: "CMD"                      # Command to keep the container running
    command: "[\"tail\", \"-f\", \"/dev/null\"]"

mount:
  host_mount_path: "/path/to/local/mount"  # Local path to mount
  pod_mount_path: "/app/lib"               # Container path for mounting

deployment:
  deployment_mode: "stack"                # Deployment mode (stack or container)
  stack_name: "my_docker_stack"           # Docker stack name
  compose_file: "docker-compose.yml"      # Docker Compose file for stack deployment
  retry_attempts: 3                       # 
  retry_interval: 5                       #

additional:
  logging:
    log_level: "INFO"                     # Log level (INFO, DEBUG, etc.)
    log_path: "."                         # Log file storage path
    log_file: "log.txt"                   # Name of the log file

Explanation of Each Section

Docker

• image_name: Specifies the Docker image to be used for creating the container.
• working_dir: Defines the working directory inside the container where the application will reside.

Libraries

• requirements_file: Name the requirements_file. Mostly people use the requirements.txt but if you want to another name you can specified it.
• path: Directory containing additional library files for requirements_file. If path not spesified library search the current working directory.
• Library List: Optionally, individual libraries can be listed with their respective versions. If you don't want to use requirements.txt use this.

Pod

• version: Version of the pod build.
• use_single_pod: Determines whether a single pod should be used (true) or multiple pods (false).
• custom_pod_name: Assign a custom name to the single pod if needed.

Custom Commands

• This addind the lines for Dockerfile. If you want to use ENV or another Docker options you can add the custom command section.

Mount

• host_mount_path: Path on the host system to mount inside the container.
• pod_mount_path: Target path inside the container where the host path is mounted.

Deployment (Optinal)

• deployment_mode: Choose between stack or compose deployment. Default is compose.
• stack_name: The name assigned to the Docker stack when deployed in stack mode. Default is my_stack.
• compose_file: Is You want to add a spesific exept docker-compose.yml use this. Default is docker-compose.yml.
• retry_attempts:
• retry_interval:

Additional

• Logging Configuration
  • log_level: Logging level (e.g., INFO, DEBUG). If You dont want to use if Use FATAL.
  • log_path: Path where log files will be stored.
  • log_file: Name of the log file.

Usage

1. Create and customize the configuration file (config.yaml) as needed on your current working directory.
2. Initialize Python Pod Manager and run the application in the pod.

Example

Use the following script to set up and manage a specific application within the pod.

from pypodman import DockerManagerApp, AppManager

# Initialize and run the pod
app = DockerManagerApp()
app.run()

# Start the application
AppManager().start_app()

# Import and use required libraries as usual
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA

class IrisDataAnalysis:
    # Application code using libraries in the pod environment
    def __init__(self):
        self.data = load_iris()
        self.df = pd.DataFrame(data=self.data.data, columns=self.data.feature_names)
        self.df['target'] = self.data.target
        self.pca_result = None
        self.n_components = 2

    def summarize_data(self):
        print("Summary of the Iris dataset:")
        print(self.df.describe())

    def draw_pairplot(self):
        sns.set_theme(style="ticks")
        sns.pairplot(self.df, hue="target", palette="husl", markers=["o", "s", "D"])
        plt.suptitle('Iris Pair Plot', y=1.02)
        plt.show()

    def apply_pca(self):
        pca = PCA(n_components=self.n_components)
        self.pca_result = pca.fit_transform(self.df.iloc[:, :-1])  # Exclude target column
        self.df['pca_one'] = self.pca_result[:, 0]
        self.df['pca_two'] = self.pca_result[:, 1]

    def plot_pca_result(self):
        if self.pca_result is None:
            print("PCA has not been applied yet. Call the 'apply_pca()' method first.")
            return

        plt.figure(figsize=(8, 6))
        sns.scatterplot(x="pca_one", y="pca_two", hue="target", palette="deep", data=self.df, s=100)
        plt.title("PCA on Iris Dataset")
        plt.xlabel("PCA One")
        plt.ylabel("PCA Two")
        plt.legend(title='Target')
        plt.show()

    def display_correlation_heatmap(self):
        plt.figure(figsize=(10, 6))
        corr = self.df.iloc[:, :-3].corr()  # Exclude PCA columns and target for correlation
        sns.heatmap(corr, annot=True, cmap='coolwarm', linewidths=0.5)
        plt.title("Correlation Heatmap of Iris Dataset")
        plt.show()

if __name__ == "__main__":
    iris_analysis = IrisDataAnalysis()
    iris_analysis.summarize_data()
    iris_analysis.draw_pairplot()
    iris_analysis.apply_pca()
    iris_analysis.plot_pca_result()
    iris_analysis.display_correlation_heatmap()

Benefits of Using a Pod

• Dependency Isolation: The specified libraries are installed inside the pod, so there is no need to install or uninstall them on your local machine.
• Easy Sharing: Share the requirements.txt file and configuration with other users. They can reproduce the same environment without affecting their own library versions.
• Seamless Operation: With the pod set up, you only need to run the code as usual without worrying about environment issues on the host machine.

License

This project is licensed under the MIT License. See the LICENSE file for details.