Docker SDK: Added Network doc ( not complated )

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# Docker SDK for Python Working with Containers
This document explains how to **manage Docker containers** using the Docker SDK for Python. Containers are the runtime unit of Docker, so this section focuses on lifecycle management: listing, creating, running, starting, stopping, restarting, pausing, and inspecting logs.
The goal is to understand **how container state changes**, how the Python SDK maps to Docker CLI behavior, and how these operations are typically used in real DevOps automation.
---
## 1. Creating the Docker Client
```python
import docker
import time
client = docker.DockerClient(base_url='unix://var/run/docker.sock')
ping_docker = client.ping()
```
### Explanation
* Establishes a connection to the Docker daemon via the Unix socket.
* `ping()` is used as a sanity check before executing container operations.
In production-grade scripts, this check prevents:
* Silent failures
* Partial execution when Docker is down
---
## 2. Listing Running Containers
```python
def get_containers_list():
containers = client.containers.list()
for c in containers:
print(c.id, c.name, c.status)
```
### Explanation
* `containers.list()` returns **only running containers** by default.
* Each returned object represents a live container instance.
Common attributes:
* `id`: unique container identifier
* `name`: human-readable container name
* `status`: runtime state (running)
This is equivalent to:
* `docker ps`
---
## 3. Listing All Containers (Including Stopped)
```python
def get_all_containers_list():
containers = client.containers.list(all=True)
for c in containers:
print(c.id, c.name, c.status)
```
### Explanation
* `all=True` includes stopped, exited, and created containers.
* Useful for cleanup, auditing, and lifecycle reconciliation.
Equivalent CLI command:
* `docker ps -a`
---
## 4. Creating a Container (Without Starting It)
```python
def create_container():
container = client.containers.create(
image="nginx:latest",
name="my_nginx",
ports={"80/tcp": 8080},
detach=True
)
```
### Explanation
This creates a container in the **created** state.
Key parameters:
* `image`: base image for the container
* `name`: explicit container name
* `ports`: port mapping (container → host)
* `detach=True`: container runs in background when started
Important distinction:
* `create()` does **not** start the container
* This mirrors `docker create`
Use cases:
* Deferred startup
* Multi-step initialization
---
## 5. Running a Container (Create + Start)
```python
def run_container():
container = client.containers.run(
"nginx:latest",
name="nginx_run",
ports={"80/tcp": 8081},
detach=True
)
```
### Explanation
* `run()` is a convenience method.
* Internally performs `create()` followed by `start()`.
Equivalent CLI command:
* `docker run -d -p 8081:80 nginx:latest`
This is the most common approach for:
* Short-lived workloads
* Simple services
---
## 6. Managing Container Lifecycle
```python
def start_container():
container = client.containers.get("my_nginx")
container.start()
time.sleep(1000)
container.restart()
time.sleep(1000)
container.pause()
container.unpause()
time.sleep(1000)
container.stop(timeout=10)
```
### Explanation
This function demonstrates multiple lifecycle transitions.
#### `containers.get(name)`
* Retrieves an existing container by name or ID.
* Raises an exception if the container does not exist.
#### `start()`
* Transitions container from `created` or `stopped``running`.
#### `restart()`
* Stops and immediately starts the container.
* Often used after configuration changes.
#### `pause()` / `unpause()`
* Freezes container processes using cgroups.
* Network and filesystem state remain intact.
#### `stop(timeout=10)`
* Sends SIGTERM, then SIGKILL after timeout.
* Allows graceful shutdown.
The `sleep()` calls simulate:
* Long-running services
* Observing container behavior between states
---
## 7. Reading Container Logs
```python
def log_container():
container = client.containers.get("my_nginx")
logs = container.logs(tail=20)
print(logs.decode())
```
### Explanation
* Retrieves logs from the containers stdout/stderr.
* `tail=20` limits output to the last 20 lines.
Important details:
* Logs are returned as bytes
* Decoding is required for readable output
Equivalent CLI command:
* `docker logs --tail 20 my_nginx`
This is critical for:
* Debugging runtime issues
* Health checks
* Observability tooling
---
## 8. Conditional Execution Based on Docker Availability
```python
if ping_docker:
get_containers_list()
```
### Explanation
* Ensures container operations only run if Docker is reachable.
* Prevents unhandled API errors.
This pattern is common in:
* Entry-point scripts
* Health probes
* Automation jobs
---
## 9. Container States (Conceptual Model)
Understanding container states is essential:
* `created`: container exists but is not running
* `running`: container processes are active
* `paused`: execution is suspended
* `exited`: container stopped normally
* `dead`: container failed unexpectedly
Every method in this document transitions the container between these states.
---
## 10. Summary
In this section, you learned how to:
* List running and stopped containers
* Create containers separately from starting them
* Run containers in a single step
* Control container lifecycle states
* Read container logs
* Safely execute operations based on Docker availability
This container-level control is the foundation for:
* Service orchestration
* CI/CD job execution
* Debugging and recovery automation
---
## References
Official Docker SDK for Python documentation:
* [https://docker-py.readthedocs.io/](https://docker-py.readthedocs.io/)