Flyte enables production-grade orchestration for machine learning workflows and data processing created to accelerate local workflows to production.

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Hi everyone! I've recently started trying Flyte in order to deploy some ML pipelines. Since i use some privately developed libraries in my code i figured out that i must dockerize and push it to the registry in order to execute the flows w/ pyflyte . However, i haven't use Docker extensively so i have trouble pushing and using. Is there any sort of guide on how to extend the demo image or a base dockerfile to begin with? Thank you very much!

```FROM <http://ghcr.io/flyteorg/flytekit:tag|ghcr.io/flyteorg/flytekit:tag> # whichever tag you want to build on

USER root

# Upgrade pip, git not needed if you don't want it
RUN apt-get -y install git &amp;&amp; pip install --upgrade pip

# Install python packages
RUN pip install ........ \
     .......

# If we want to install custom git packages we do something like this:
# RUN pip install git+<https://gitrepolinkhere>

USER flytekit

CMD [] # Not needed```
This is a basic Dockerfile structure we use to build custom images

You can also use ImageSpec from 1.6.0 onwards I believe, but this is more explicit and manual

<@U05F48ZQB5E>

In terms of registry that would be something you set up on something like AWS ECR or Dockerhub etc

then you would build the images on your local machine or in a pipeline with:

docker build,

then you docker tag

then you docker push after authenticating to your registry

Thanks for the quick reply! One question more. If my modules are local .py files can i import them directly or must i setup a private repository?

You can just use the inbuilt `COPY` command for Docker to copy the modules from your local build path into the Docker container's working directory. By default it's /root or $HOME

So
If you want to use relative paths with "./"
```COPY ./path/to/your/modules/on/local ./path/to/docker/container/directory```
Or absolute paths in terms of PATH
```COPY /absolute/path/here/on/local /absolute/path/inside/docker/container```

then pip install or just refer to it in your code and it should work as long as you've copied it into a PATH that your docker container + code can find

Hello again. While toying with docker and the dockerfiles i wondered if i could extend the demo or sandbox environment in order to have the libraries and local files i want