Langkit Container Examples

The langkit container is a containerized solution that hosts langkit. It simplifies the deployment of langkit by hosting model assets, required dependencies, metric configuration, as well as integration with the WhyLabs platform. The container exposes endpoints for computing these metrics that can be called from LLM applications. It can also be configured to generate validation reports based on thresholds for each metric.

This repo has various examples for configuring and using the WhyLabs langkit container. Each example in the examples folder showcases a different use case and contains a test directory with working Python code that demonstrates how to use it. Browse that folder if you're looking for a specific example. Each example either configures or calls a deployed instance of the container.

Container Access

We distrubite the container from our private Gitlab repository. We'll generate a user name and password for you to pull the container. Once you have those credentials, you can authenticate and pull whichever tag you need. Check our release notes to see the latest version.

# Manually pulling via Docker
docker login registry.gitlab.com
docker pull registry.gitlab.com/whylabs/langkit-container:latest

If you're deploying through Helm then see our Helm instructions to use these credentials to pull images.

Zero Configuration

If you want to use the container only for logging (no validation) then you can jump directly to testing it out with the no_configuration example. Without configuration, you'll be able to use the /evaluate endpoint to compute LLM metrics, upload and monitor them in WhyLabs, but you won't be able to use the validation functionality because validation thresholds are defined in custom configuration.

See no_configuration example for more.

Custom Configuration

There are a few differemt configuration methods you can choose. The only one that doesn't involve building a container that extends from ours is s3 polling. See the s3 example for more information. The rest of this section describes the general custom building process and points to additional examples in this repo that demonstrate it.

When you configure the container, you should think about it as a standalone project that you can build and test. Each of the examples are, in fact, stand alone projects that have their own Poetry dependencies and build steps. The only real requirement in the end is that you have a Dockerfile that extends our image and copies certain files into a certain spot. This section will describe how that build process works.

Step 1: Pick an Image Tag

You'll want to either use latest or browse the available tags from our Gitlab respository with

curl "https://gitlab.com/api/v4/projects/55361491/registry/repositories/6125233/tags" --header "PRIVATE-TOKEN: <api token>"

Reach out to us at support@whylabs.ai to get an API token.

Step 2: Create a Configuration File

Depending on whether you're using Python, Yaml, or both, you'll be creating different files. Yaml is the easiest way to configure the container but sometimes you need more control. If you're going to be deploying custom models, for example, then you'll need to use Python most likely since you'll probably have to reference libraries like torch and execute some setup code.

Step 2.1: Custom Yaml Configuration

This is what your project will look like.

.
├── Dockerfile
├── Makefile
├── pyproject.toml
└── whylogs_config
    ├── model-1.yaml
    └── model-2.yaml

These files will be included by your Dockerfile in the section below. The container is hard coded to search
/opt/whylogs-container/whylogs_container/whylogs_config/ for yaml files that it understands at startup.

See configure_container_yaml to see what you can put in there.

Step 2.2: Custom Python Configuration

This is what your project will look like.

.
├── Dockerfile
├── Makefile
├── pyproject.toml
└── whylogs_config
    └── config.py

The container is hard coded to import whatever is at /opt/whylogs-container/whylogs_container/whylogs_config/config.py. This is the mechanism by which the custom configuration code is evaluated. If you need to deploy and reference other python files then you can tag them along as well and use relative imports and that will work out at runtime.

See configure_container_python for an example that demonstrates using Python to configure the container.

Step 3: Create a Dockerfile

What goes into this Dockerfile can depend on what you're trying to do. The simplest Dockerfile would look like this.

FROM registry.gitlab.com/whylabs/langkit-container:1.0.19

# Force the container to fail if the config is not present. Safeguard for messing up the
# build in such a way that the config is not included correctly.
ENV FAIL_STARTUP_WITHOUT_CONFIG=True

# Copy our custom config code
COPY ./whylogs_config /opt/whylogs-container/whylogs_container/whylogs_config/

You're in full control of this Dockerfile and build and you can do basically anything you need to do to the base image here. For example, you might want to include some additional pip dependencies as well, which could look like this.

FROM registry.gitlab.com/whylabs/langkit-container:1.0.19

# Force the container to fail if the config is not present. Safeguard for messing up the
# build in such a way that the config is not included correctly.
ENV FAIL_STARTUP_WITHOUT_CONFIG=True

COPY ./requirements.txt ./requirements.txt
RUN /bin/bash -c "source .venv/bin/activate; pip install -r ./requirements.txt"

Just make sure you use the virtualenv that we packaged along with the image so dependencies are installed correctly. You only need to include a requirements file if you need something that isn't in the container already. Each image ships with its pyproject.toml file so you can find the dependencies it was bundled with. You can get this from the image with this command.

## Get the declared dependnecies
docker run --platform=linux/amd64 --rm --entrypoint /bin/bash registry.gitlab.com/whylabs/langkit-container:latest -c 'cat pyproject.toml'

## Or start an interactive Python shell and test imports
docker run -it --platform=linux/amd64 --rm --entrypoint /bin/bash registry.gitlab.com/whylabs/langkit-container:latest -c "source .venv/bin/activate; python3.10"

In general, you can expect pandas, whylogs, langkit, and torch==2.0.0 to be present, as well as whatever dependencies they pull in.

See the custom_model for a complete example that packages extra dependencies.

Step 4: Build the Image

The build time will vary depending on what you put in your Dockerfile. The simple example of copying your python code into the image will be very fast, while installing dependencies can be very slow.

docker build . -t my_llm_container

Each of the examples do this with make build

Step 5: Deploy a Container

This will depend heavily on your infrastructure. The simplest deployment method is Docker of course.

docker run -it --rm -p 127.0.0.1:8000:8000 --env-file local.env my_llm_container

Each of the examples do this with make run

See our sample Helm file for an example of deploying via Helm.

Step 6: Call the Container

The container has a client that you can use to call it, whylogs-container-client. If you prefer using other languages, curl, or generic http then see the api docs for request formats.