wcontainer 0.1.1

Docker control with python

Wcontainer

Wcontainer is a library for specific docker container operations. The library offers a number of modules that make using docker containers easier and simpler.

Description

Wcontainer is a library for specific docker container operations. The library offers a number of modules that make using docker containers easier and simpler.

Installation

To install the library, use pip:

pip install wcontainer

Description

The wcontainer library offers a number of modules that make using docker containers easier and simpler. The library is designed to be used in conjunction with the docker library, which is the official Python client for the Docker Engine API.

Examples

This directory contains a collection of examples that demonstrate the usage of various modules and functionalities in this project. Each subfolder corresponds to a specific module and includes example scripts to help you understand how to use that module.

Directory Structure

The examples are organized as follows:

examples/
    4-monitoreo_RT/
        monitoreo_tiempo_real.py
    3-avanzados/
        1_ajuste_dinamico_recursos.py
        2_escalar_contenedores_segun_uso_recursos.py
        3_escalar_antes_desplegar.py
        4_autonamizar_generacion_informe_errores.py
        5_usar_ia_detectar_fallos_para_reinicio_automatico.py
    2-intermedios/
        1_buscar_vulnerabilidades.py
        2_read_metrics.py
        3_informe de errores.py
    1-basicos/
        1_list_container.py
        2_start_new_container.py
        3_new_resources.py

How to Use

1. Navigate to the module folder of interest, e.g., examples/module1/.
2. Open the README.md in that folder to get detailed information about the examples.
3. Run the scripts directly using:

   python example1.py
   

Modules and Examples

1-basicos

Description

This module demonstrates specific functionalities.

• 1_list_container.py: Example demonstrating functionality.

# ✅ 1.1 Listar contenedores en ejecución

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)

# List only running containers
print(wc.list_containers())

# List all containers (including stopped ones)
print(wc.list_containers(all_containers=True))

• 2_start_new_container.py: Example demonstrating functionality.

# ✅ 1.2 Iniciar un contenedor con parámetros básicos

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)

# Start an Nginx container exposing port 8080
print(wc.start_container("nginx", "my_nginx", ports={"80/tcp": 8080}))

• 3_new_resources.py: Example demonstrating functionality.

# ✅ 1.3 Ajustar recursos de un contenedor en tiempo real

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


# Limit a running container to 2 CPU cores and 1GB RAM
print(wc.adjust_container_resources("my_nginx", cpu_limit=2.0, memory_limit="1g"))

2-intermedios

Description

This module demonstrates specific functionalities.

• 1_buscar_vulnerabilidades.py: Example demonstrating functionality.

# ✅ 2.2 Escanear una imagen Docker en busca de vulnerabilidades con Trivy

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


# Scan the latest Nginx image for security vulnerabilities
print(wc.scan_image_with_trivy("nginx"))

• 2_read_metrics.py: Example demonstrating functionality.

# ✅ 2.1 Obtener métricas de uso de CPU, RAM y GPU

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


# Get system-wide CPU, RAM, and GPU usage
print(wc.get_resource_metrics())

• 3_informe de errores.py: Example demonstrating functionality.

# ✅ 2.3 Generar un informe de errores de un contenedor

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


# Analyze logs and generate a JSON error report for the container
print(wc.generate_error_report("my_nginx", output_file="nginx_errors.json"))

3-avanzados

Description

This module demonstrates specific functionalities.

• 1_ajuste_dinamico_recursos.py: Example demonstrating functionality.

# ✅  3.1 Autoajuste dinámico de recursos

import random
import time
from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


# Dynamically adjust CPU and RAM allocation every 10 seconds
for _ in range(5):
    print(
        wc.adjust_container_resources(
            "my_nginx", cpu_limit=random.uniform(1.0, 4.0), memory_limit="512m"
        )
    )
    time.sleep(10)


# 🔹 Explicación: Cada 10 segundos, el script cambia la cantidad de CPU y RAM asignada al contenedor.

• 2_escalar_contenedores_segun_uso_recursos.py: Example demonstrating functionality.

# ✅  3.2 Detectar uso alto de recursos y escalar contenedores

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


resource_metrics = wc.get_resource_metrics()

# If CPU usage is higher than 80%, start another instance
if float(resource_metrics["cpu_usage"].strip("%")) > 80:
    print(wc.start_container("nginx", "nginx_replica"))

# 🔹 Explicación: Si el uso de CPU del sistema supera el 80%, lanza una nueva instancia del contenedor para distribuir la carga.

• 3_escalar_antes_desplegar.py: Example demonstrating functionality.

# ✅  3.3 Escanear imágenes antes de desplegar en producción

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


# Run a security check before deployment
scan_result = wc.scan_image_with_trivy("my_application_image")

# If vulnerabilities are found, abort deployment
if "error" not in scan_result and any(scan_result.get("Vulnerabilities", [])):
    print("Deployment aborted: vulnerabilities detected.")
else:
    print("No critical vulnerabilities found, proceeding with deployment...")
    print(
        wc.start_container(
            "my_application_image", "production_app", ports={"5000/tcp": 5000}
        )
    )

# 🔹 Explicación: Antes de desplegar una imagen, el sistema revisa vulnerabilidades con Trivy. Si se encuentran fallos críticos, se cancela el despliegue.

• 4_autonamizar_generacion_informe_errores.py: Example demonstrating functionality.

# ✅  3.4 Automatizar la generación de informes de errores

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


# Check logs of multiple containers and generate error reports
containers = ["my_nginx", "backend_service", "database"]
for container in containers:
    print(wc.generate_error_report(container, output_file=f"{container}_errors.json"))


# 🔹 Explicación: Revisa los logs de varios contenedores y genera informes de errores individuales en formato JSON.

• 5_usar_ia_detectar_fallos_para_reinicio_automatico.py: Example demonstrating functionality.

# ✅  3.5 Detectar fallos en contenedores con IA y reiniciarlos automáticamente

from wcontainer import Wcontainer


wc = Wcontainer(verbose=True)


containers = ["my_nginx", "backend_service"]

for container in containers:
    failure_prediction = wc.predict_container_failure(container)
    print(failure_prediction)

    if "ALERT" in failure_prediction:
        print(f"Restarting {container} due to high failure probability...")
        print(wc.start_container("nginx", f"{container}_restarted"))


# 🔹 Explicación: Usa la predicción de fallos con IA y reinicia automáticamente los contenedores con alta probabilidad de error.

4-monitoreo_RT

Description

This module demonstrates specific functionalities.

• monitoreo_tiempo_real.py: Example demonstrating functionality.

# ✅  4️. Ejemplo Completo: Monitoreo en Tiempo Real

from wcontainer import Wcontainer
import time


wc = Wcontainer(verbose=True)


while True:
    print("\n📊 Checking system resources...")
    print(wc.get_resource_metrics())

    print("\n🔍 Scanning image for vulnerabilities...")
    print(wc.scan_image_with_trivy("nginx"))

    print("\n📂 Checking error logs for running containers...")
    containers = ["my_nginx", "backend_service"]
    for container in containers:
        print(wc.generate_error_report(container))

    print("\n⚡ Adjusting container resources...")
    print(wc.adjust_container_resources("my_nginx", cpu_limit=2.0, memory_limit="1g"))

    print("\n🛑 Predicting failures in containers...")
    for container in containers:
        failure_prediction = wc.predict_container_failure(container)
        print(failure_prediction)

        if "ALERT" in failure_prediction:
            print(f"Restarting {container} due to high failure probability...")
            print(wc.start_container("nginx", f"{container}_restarted"))

    print("\n⏳ Waiting 60 seconds before next check...\n")
    time.sleep(60)


# 🔹 Explicación:
# ✅ Recolecta métricas del sistema cada 60 segundos
# ✅ Escanea la imagen Nginx con Trivy
# ✅ Analiza los logs de errores de todos los contenedores
# ✅ Ajusta dinámicamente la asignación de recursos
# ✅ Detecta fallos con IA y reinicia contenedores automáticamente

License

MIT

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