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54 lines
1.6 KiB
Markdown
54 lines
1.6 KiB
Markdown
# Using Docker
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To quickly get started, run the command `make docker`.
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If docker is installed the Makefile will export extra targets in the fomrat `docker.*` to build and run the docker image. Type `make` for a list of available tasks.
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There is a basic `docker-compose.yml` in the docker directory.
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## Building the development image
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Using `make docker` will build the dev image if it does not exist, then drops
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you inside the container with the langchain environment available in the shell.
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### Customizing the image and installed dependencies
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The image is built with a default python version and all extras and dev
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dependencies. It can be customized by changing the variables in the [.env](/docker/.env)
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file.
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If you don't need all the `extra` dependencies a slimmer image can be obtained by
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commenting out `POETRY_EXTRA_PACKAGES` in the [.env](docker/.env) file.
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### Image caching
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The Dockerfile is optimized to cache the poetry install step. A rebuild is triggered when there a change to the source code.
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## Example Usage
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All commands from langchain's python environment are available by default in the container.
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A few examples:
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```bash
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# run jupyter notebook
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docker run --rm -it IMG jupyter notebook
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# run ipython
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docker run --rm -it IMG ipython
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# start web server
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docker run --rm -p 8888:8888 IMG python -m http.server 8888
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```
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## Testing / Linting
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Tests and lints are run using your local source directory that is mounted on the volume /src.
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Run unit tests in the container with `make docker.test`.
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Run the linting and formatting checks with `make docker.lint`.
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Note: this task can run in parallel using `make -j4 docker.lint`.
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