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# Docker SDK for Python – Working with Images
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This document explains how to **manage Docker images** using the Docker SDK for Python. Instead of treating the SDK as a set of function calls, we’ll approach images the same way Docker itself does: as immutable artifacts that are pulled, built, tagged, pushed, inspected, and eventually cleaned up.
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All examples assume:
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* Docker is installed and running
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* The Python process has access to the Docker socket
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---
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## 1. Creating the Docker Client
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```python
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import docker
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client = docker.DockerClient(base_url='unix://var/run/docker.sock')
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ping_docker = client.ping()
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```
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### Explanation
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* `DockerClient` establishes a connection to the Docker daemon.
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* `ping()` verifies that Docker is reachable before doing any real work.
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This is a common pattern in automation:
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* Fail fast if Docker is unavailable
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* Avoid partial execution later in the script
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---
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## 2. Pulling Images from a Registry
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```python
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def pull_image(name_image, tag_image):
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image = client.images.pull(name_image, tag=tag_image)
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print(image)
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```
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### What this does
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* Downloads an image from a registry (Docker Hub or private registry).
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* If the image already exists locally, Docker may reuse layers.
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Parameters:
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* `name_image`: repository name (e.g. `alpine`, `nginx`, `myrepo/app`)
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* `tag_image`: specific version or variant (e.g. `3.20`, `latest`)
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Returned value:
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* An `Image` object representing the pulled image
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Why this matters:
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* Pulling explicitly avoids relying on implicit image downloads
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* Makes automation predictable and repeatable
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---
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## 3. Building Images from a Dockerfile
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### Basic build
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```python
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def build_image():
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image, logs = client.images.build(
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path=".",
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tag="myapp:1.0"
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)
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for log in logs:
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if "stream" in log:
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print(log["stream"].strip())
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```
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### Explanation
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* `path="."` tells Docker to use the current directory as the build context.
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* Docker automatically looks for a file named `Dockerfile`.
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* `tag="myapp:1.0"` assigns a name and version to the resulting image.
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The build process returns:
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* `image`: the final built image object
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* `logs`: a stream of build output messages
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Printing build logs is important because:
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* Docker build failures are only visible in logs
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* CI pipelines rely on this output for debugging
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---
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## 4. Advanced Build with Custom Dockerfile and Build Arguments
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```python
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def build_image_2():
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image, logs = client.images.build(
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path=".",
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dockerfile="Dockerfile.prod",
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tag="myapp:prod",
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buildargs={
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"APP_ENV": "production",
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"VERSION": "1.0.0"
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}
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)
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```
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### Explanation
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This version adds more control:
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* `dockerfile="Dockerfile.prod"`
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* Allows multiple Dockerfiles per project
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* Common for dev vs prod builds
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* `buildargs`
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* Passed to `ARG` instructions inside the Dockerfile
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* Enables parameterized builds without editing the Dockerfile
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Typical DevOps use cases:
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* Environment-specific builds
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* Injecting version numbers
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* Feature flags during build time
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---
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## 5. Tagging Images
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```python
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def tag_image():
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image = client.images.get("myapp:1.0")
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image.tag("myrepo/myapp", tag="latest")
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```
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### Explanation
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* Docker images are immutable, but tags are not.
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* This creates an additional reference to the same image ID.
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Why tagging is important:
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* One image can have multiple tags
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* Tags represent lifecycle stages (`1.0`, `prod`, `latest`)
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This is how promotion pipelines work:
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* Build once
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* Tag many times
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* Push selectively
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---
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## 6. Removing Images
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```python
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def remove_image():
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client.images.remove("myapp:1.0", force=True)
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```
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### Explanation
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* Removes the image reference from the local Docker host.
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* `force=True` removes the image even if it is in use by stopped containers.
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Use with care:
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* Running containers still prevent deletion
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* Forced removal is destructive
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---
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## 7. Pushing Images to a Registry
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```python
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def push_image():
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client.images.push(
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repository="myrepo/myapp",
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tag="latest"
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)
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```
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### Explanation
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* Uploads the image layers to a registry.
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* Requires prior authentication (`client.login`).
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Important notes:
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* Only new or changed layers are pushed
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* Tags determine what remote users pull
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This step is usually automated in:
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* CI/CD pipelines
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* Release workflows
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---
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## 8. Cleaning Up Unused Images
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```python
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def prune_images():
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result = client.images.prune()
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print(result)
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```
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### Explanation
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* Removes dangling images (untagged and unused).
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* Helps reclaim disk space on build servers.
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The result includes:
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* Number of images removed
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* Amount of disk space freed
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This is essential for:
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* CI runners
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* Long-lived build machines
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---
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## 9. Inspecting Image Metadata
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```python
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def inspect_image():
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alpine_image = client.images.get("alpine:3.20")
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print(alpine_image.attrs)
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print(alpine_image.attrs["Id"])
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print(alpine_image.attrs["Size"])
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print(alpine_image.attrs["Config"]["Env"])
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print(alpine_image.attrs["Config"]["Cmd"])
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```
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### Explanation
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* `attrs` exposes the raw Docker image inspection data.
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* This is equivalent to `docker image inspect`.
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Useful fields:
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* `Id`: content-addressable image hash
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* `Size`: image size in bytes
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* `Config.Env`: environment variables baked into the image
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* `Config.Cmd`: default command
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This information is often used for:
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* Debugging unexpected behavior
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* Auditing images
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* Validating build output
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---
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## 10. Listing Local Images
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```python
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def image_list():
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images = client.images.list()
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for item in images:
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print(item.id, item.tags)
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```
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### Explanation
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* Lists all images stored locally.
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* Each image may have multiple tags or none.
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This mirrors:
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* `docker images`
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---
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## 11. Ensuring an Image Exists
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```python
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def ensure_image(name):
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try:
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client.images.get(name)
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print(f"{name} exists")
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except docker.errors.ImageNotFound:
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print(f"Pulling {name}")
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client.images.pull(name)
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```
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### Explanation
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This is a very common DevOps pattern:
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* Check if the image exists locally
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* Pull it only if necessary
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Benefits:
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* Avoids unnecessary network calls
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* Makes scripts idempotent
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---
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## 12. Important Exceptions to Know
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When working with images, you must handle failures explicitly.
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### `docker.errors.ImageNotFound`
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* Raised when an image does not exist locally
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* Common when calling `get()`
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### `docker.errors.BuildError`
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* Raised when an image build fails
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* Usually due to Dockerfile errors or missing files
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### `docker.errors.APIError`
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* Raised for general Docker API failures
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* Includes permission issues, daemon errors, and network problems
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Catching these exceptions is critical for:
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* Reliable automation
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* Meaningful error reporting
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* CI/CD stability
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---
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## 13. Summary
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In this section, you learned how to:
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* Pull images from registries
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* Build images using Dockerfiles
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* Use build arguments and multiple Dockerfiles
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* Tag and push images
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* Inspect and list images
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* Clean up unused images
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* Handle common Docker image errors
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This image workflow is the backbone of:
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* CI pipelines
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* Release automation
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* Platform engineering systems
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---
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## References
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Official Docker SDK for Python documentation:
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* [https://docker-py.readthedocs.io/](https://docker-py.readthedocs.io/)
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@@ -0,0 +1,284 @@
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# Docker SDK for Python – Working with Containers
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|
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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.
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|
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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.
|
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|
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---
|
||||
|
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## 1. Creating the Docker Client
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|
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```python
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import docker
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import time
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|
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client = docker.DockerClient(base_url='unix://var/run/docker.sock')
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ping_docker = client.ping()
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```
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|
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### Explanation
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|
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* Establishes a connection to the Docker daemon via the Unix socket.
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* `ping()` is used as a sanity check before executing container operations.
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|
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In production-grade scripts, this check prevents:
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|
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* Silent failures
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* Partial execution when Docker is down
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|
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---
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## 2. Listing Running Containers
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```python
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def get_containers_list():
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containers = client.containers.list()
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|
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for c in containers:
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print(c.id, c.name, c.status)
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```
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|
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### Explanation
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|
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* `containers.list()` returns **only running containers** by default.
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* Each returned object represents a live container instance.
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|
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Common attributes:
|
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|
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* `id`: unique container identifier
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* `name`: human-readable container name
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* `status`: runtime state (running)
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|
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This is equivalent to:
|
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|
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* `docker ps`
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|
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---
|
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|
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## 3. Listing All Containers (Including Stopped)
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|
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```python
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def get_all_containers_list():
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containers = client.containers.list(all=True)
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|
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for c in containers:
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print(c.id, c.name, c.status)
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```
|
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|
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### Explanation
|
||||
|
||||
* `all=True` includes stopped, exited, and created containers.
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* Useful for cleanup, auditing, and lifecycle reconciliation.
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|
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Equivalent CLI command:
|
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|
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* `docker ps -a`
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|
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---
|
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|
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## 4. Creating a Container (Without Starting It)
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|
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```python
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def create_container():
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container = client.containers.create(
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image="nginx:latest",
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name="my_nginx",
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ports={"80/tcp": 8080},
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detach=True
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)
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```
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|
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### Explanation
|
||||
|
||||
This creates a container in the **created** state.
|
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|
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Key parameters:
|
||||
|
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* `image`: base image for the container
|
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* `name`: explicit container name
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* `ports`: port mapping (container → host)
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* `detach=True`: container runs in background when started
|
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|
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Important distinction:
|
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|
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* `create()` does **not** start the container
|
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* This mirrors `docker create`
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|
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Use cases:
|
||||
|
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* Deferred startup
|
||||
* Multi-step initialization
|
||||
|
||||
---
|
||||
|
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## 5. Running a Container (Create + Start)
|
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|
||||
```python
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def run_container():
|
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container = client.containers.run(
|
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"nginx:latest",
|
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name="nginx_run",
|
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ports={"80/tcp": 8081},
|
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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():
|
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container = client.containers.get("my_nginx")
|
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|
||||
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 container’s 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/)
|
||||
|
||||
Reference in New Issue
Block a user