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https://github.com/duaraghav8/dockershrink

Dockershrink is an Open Source, AI-powered tool to reduce the size of your Docker Images
https://github.com/duaraghav8/dockershrink

containers docker docker-image optimization size-optimization

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Dockershrink is an Open Source, AI-powered tool to reduce the size of your Docker Images

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README 

        
# DockerShrink



[Talk to us on Slack](https://join.slack.com/t/dockershrink/shared_invite/zt-2vwkeqw0k-JXmHBNCQnG4X1BBf1t3xyg) **|** [Vote for Feature Requests](https://github.com/duaraghav8/dockershrink/issues?q=is%3Aopen+is%3Aissue+label%3Afeature-request)



Dockershrink is an AI-powered Commandline Tool that helps you reduce the size of your Docker images



![Typical interaction with dockershrink CLI](./assets/images/dockershrink-how-it-works.gif)



It combines the power of traditional Rule-based analysis with Generative AI to apply state-of-the-art optimizations to your Image configurations :brain:



Dockershrink can automatically apply techniques like Multi-Stage builds, switching to Lighter base images like alpine and running dependency checks. PLUS a lot more is on the roadmap :rocket:



Currently, the tool only supports [NodeJS](https://nodejs.org/en) applications.



> [!IMPORTANT]

> Dockershrink is **BETA** software.

> 

> You can provide your feedback by [creating an Issue](https://github.com/duaraghav8/dockershrink/issues) in this repository.



## Why does dockershrink exist?

Every org using containers in development or production environments understands the pain of managing hundreds or even thousands of bloated Docker images in their infrastructure.



High data storage and transfer costs, long build times, underprodctive developers - we've seen it all.



The issue becomes even more painful and costly with interpreted languages such as Nodejs & Python.

Apps written in these languages need to pack the interpreters and all their dependencies inside their container images, significantly increasing their size.



But not everyone realizes that by just implementing some basic techniques, they can reduce the size of a 1GB Docker image down to **as little as 100 MB**!



([I also made a video on how to do this.](https://youtu.be/vHBHxQfK6cM))



Imagine the costs saved in storage & data transfer, decrease in build times AND the productivity gains for developers :exploding_head:



Dockershrink aims to automatically apply advanced optimization techniques so engineers don't have to waste time on it and the organization still saves :moneybag:!



You're welcome :wink:



## How it works

When you invoke the dockershrink CLI on your project, it analyzes code files.



Dockershrink looks for the following files:



:point_right: `Dockerfile` (Required)



:point_right: `package.json` (Optional)



:point_right: `.dockerignore` (Optional, created if it doesn't already exist)



It then creates a new directory (default: `dockershrink.optimized`) inside the project, which contains modified versions of your configuration files that will result in a smaller Docker Image.



The CLI outputs a list of actions it took over your files.



It may also include suggestions on further improvements you could make.



## Installation

You can install dockershrink using PIP or PIPX

```bash

$ pip install dockershrink

```

```bash

$ pipx install dockershrink

```



Alternatively, you can also install it using [Homebrew](https://brew.sh/):

```bash

brew install duaraghav8/tap/dockershrink

```



But you should prefer to use `pip` instead because installation via brew takes a lot longer and occupies significanlty more space on your system (see [this issue](https://github.com/duaraghav8/homebrew-dockershrink/issues/3))



## Usage



Navigate into the root directory of one of your Node.js projects and invoke dockershrink with the `optimize` command:



```bash

$ dockershrink optimize

```



Dockershrink will create a new directory with the optimized files and output the actions taken and (maybe) some more suggestions.



For detailed information about the `optimize` command, run



```bash

dockershrink optimize --help

```



You can also use the `--verbose` option to get stack traces in case of failures:



```bash

$ dockershrink optimize --verbose

```



To enable `DEBUG` logs, you can set the environment variable



```bash

export DOCKERSHRINK_CLI_LOGLEVEL=DEBUG

dockershrink optimize

```



### Using AI Features



> [!NOTE]

> Using AI features is optional, but **highly recommended** for more customized and powerful optimizations.

>

> To use AI, you need to supply your own OpenAI API key, so even though Dockershrink itself is free, openai usage might incur some cost for you.



By default, dockershrink only runs rule-based analysis to optimize your image definition.



If you want to enable AI, you must supply your [OpenAI API Key](https://openai.com/index/openai-api/).



```bash

dockershrink optimize --openai-api-key 



# Alternatively, you can supply the key as an environment variable

export OPENAI_API_KEY=

dockershrink optimize

```



> [!NOTE]

> Dockershrink does not store your OpenAI API Key.

>

> So you must provide your key every time you want "optimize" to use AI features.

> This is to avoid any unexpected costs.



### Default file paths

By default, the CLI looks for the files to optimize in the current directory.



You can also specify the paths to all files using options (see `dockershrink optimize --help` for the available options).



---



## Development :computer:



> [!NOTE]

> This section is for authors and contributors.

> If you're simply interested in using Dockershrink, you can skip this section.



1. Clone this repository

2. Navigate inside the root directory of the project and create a new virtual environment

```bash

python3 -m venv .venv

source .venv/bin/activate

```

3. Install all dependencies

```bash

pip install --no-cache-dir -r requirements.txt

```

4. Install the editable CLI tool

```bash

# -e ensures that the tool is editable, ie, code changes reflect in the tool immediately, without having to re-install it

pip install -e .



# Try running the cli

dockershrink --help

```

5. Make your code changes

6. Run black

```bash

black .

```

7. In case of any changes in dependencies, update requirements.txt

```bash

pip freeze > requirements.txt

```



### Release :rocket:

Once all code changes have been made for the next release:

- Upgrade the version in [pyproject.toml](./pyproject.toml) and [cli.py](./dockershrink/cli.py)

- Make sure that dependencies and their versions are updated in [requirements.txt](./requirements.txt) and pyproject.toml



Then proceed to follow these steps to release new dockershrink version on PyPI:



1. Build dockershrink from source

```bash

python -m build

```

2. Upload to testpypi

```bash

twine upload --repository testpypi dist/*

```

3. The new version of the package should now be available in [TestPyPI](https://test.pypi.org/project/dockershrink/).

4. Try installing the test package

```bash

pip install --index-url https://test.pypi.org/simple/ --no-deps dockershrink

```

5. Create a new Tag and push it

```bash

git tag -a  -m "Tag version "

git push origin 

```

6. [Create a new release](https://github.com/duaraghav8/dockershrink/releases/new) in this repository

7. Upload the package to PyPI

```bash

twine upload dist/*

```

8. The new version of the package should now be available in [PyPI](https://pypi.org/project/dockershrink/)

9. Update the package in [Dockershrink Homebrew Tap](https://github.com/duaraghav8/homebrew-dockershrink) as well.