ludwig-ai--ludwig/docker

Ludwig Docker Images

These images provide Ludwig, a toolbox to train and evaluate deep learning models without the need to write code. Ludwig Docker images contain the full set of pre-requisite packages to support these capabilities

• text features
• image features
• audio features
• visualizations
• hyperparameter optimization
• distributed training
• model serving

Publishing images

Images are normally published automatically by CI (.github/workflows/docker.yml) when a release tag is pushed. To publish manually or backfill a release, use the script in this directory:

# Requires: docker login to a ludwigai Docker Hub account
./docker/build_and_push.sh <version> [--latest]

# Examples
./docker/build_and_push.sh 0.14.0 --latest   # new latest release
./docker/build_and_push.sh 0.13.0            # backfill without updating :latest

See RELEASES.md for the full release procedure.

Repositories

These four repositories contain a version of Ludwig with full features built from the project's master branch.

• ludwigai/ludwig Ludwig packaged with PyTorch
• ludwigai/ludwig-gpu Ludwig packaged with gpu-enabled version of PyTorch
• ludwigai/ludwig-ray Ludwig packaged with PyTorch and Ray 2.3.1 (https://github.com/ray-project/ray)
• ludwigai/ludwig-ray-gpu Ludwig packaged with gpu-enabled versions of PyTorch and Ray 2.3.1 (https://github.com/ray-project/ray)

Image Tags

• master - built from Ludwig's master branch
• nightly - nightly build of Ludwig's software.
• sha-<commit point> - version of Ludwig software at designated git sha1 7-character commit point.

Running Containers

Examples of using the ludwigai/ludwig:master image to:

• run the ludwig cli command or
• run Python program containing Ludwig api or
• view Ludwig results with Tensorboard

For purposes of the examples assume this host directory structure

/top/level/directory/path/
    data/
        train.csv
    src/
        config.yaml
        ludwig_api_program.py

Run Ludwig CLI

# set shell variable to parent directory
parent_path=/top/level/directory/path

# invoke docker run command to execute the ludwig cli
# map host directory ${parent_path}/data to container /data directory
# map host directory ${parent_path}/src to container /src directory
docker run -v ${parent_path}/data:/data  \
    -v ${parent_path}/src:/src \
    ludwigai/ludwig:master \
    experiment --config /src/config.yaml \
        --dataset /data/train.csv \
        --output_directory /src/results

Experiment results can be found in host directory /top/level/directory/path/src/results

Run Python program using Ludwig APIs

# set shell variable to parent directory
parent_path=/top/level/directory/path

# invoke docker run command to execute Python interpreter
# map host directory ${parent_path}/data to container /data directory
# map host directory ${parent_path}/src to container /src directory
# set current working directory to container /src directory
# change default entrypoint from ludwig to python
docker run  -v ${parent_path}/data:/data  \
    -v ${parent_path}/src:/src \
    -w /src \
    --entrypoint python \
    ludwigai/ludwig:master /src/ludwig_api_program.py

Ludwig results can be found in host directory /top/level/directory/path/src/results

View Ludwig Tensorboard results

# set shell variable to parent directory
parent_path=/top/level/directory/path

# invoke docker run command to execute Tensorboard
# map host directory ${parent_path}/src to container /src directory
# set up mapping from localhost port 6006 to container port 6006
# change default entrypoint from ludwig to tensorboard
# --logdir container location of tenorboard logs /src/results/<experiment_name>_<model_name>/model/logs
# --bind_all Tensorboard serves on all public container interfaces
docker run  -v ${parent_path}/src:/src \
    -p 6006:6006 \
    --entrypoint tensorboard \
    ludwigai/ludwig:master \
      --logdir /src/results/experiment_run/model/logs \
      --bind_all

Point browser to http://localhost:6006 to see Tensorboard dashboard.

Devcontainer

If you want to contribute to Ludwig, you can setup a Docker container with all the dependencies installed as a full featured development environment. This can be done using devcontainers with VS Code: https://code.visualstudio.com/docs/devcontainers/containers

You can find the devcontainer.json file within the top level .devcontainer folder.