AutoMLPipeline.jl

(★ 370)

A package that makes it trivial to create and evaluate machine learning pipeline architectures.

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- - CompatHelper.yml
  - docs.yml
  - docscleanup.yml
  - register.yml
  - TagBot.yml
  - tests-nightly.yml
  - tests.yml
- - argo-k8s.py
  - run.sh
- - install-apps.sh
  - install-k0s-cluster.sh
  - install-k3s-cluster.sh
  - install-kind-cluster-with-podman.sh
  - k0sctl.yaml
  - objstore.yml
  - prometheus-with-thanos-values.yaml
  - thanos-values.yaml
- automl-anomalydetection-template.yaml
- automl-classification-template.yaml
- automl-dualsearch-template.yaml
- automl-regression-template.yaml
- automl-unisearch-template.yaml
- automlad-ensemble-template.yaml
- automlai-dualsearch-template.yaml
- automlai-prediction-classification-template.yaml
- automlai-prediction-regression-template.yaml
- automlai-prediction-template.yaml
- automlai-unisearch-template.yaml
- multi-agent-argo-k8s-mcp-flowise.json
- run.sh
- - node_cpu_ratio_rate_5m_1d_1m.csv
- - AutoAD.jl
  - autoanomalydetection.jl
  - automlflowanomalydetection.jl
  - caretanomalydetector.jl
  - mlflowutils.jl
  - skanomalydetector.jl
- - runtests.jl
  - test_caret_anomalydetector.jl
  - test_skanomalydetector.jl
- CondaPkg.toml
- Project.toml
- - AutoAI.ipynb
- - AutoAI.ipynb
  - combine.jl
  - driver.jl
- - AutoAI.jl
  - autoclassification.jl
  - automlflowclassification.jl
  - automlflowregression.jl
  - autoregression.jl
  - main.jl
  - mlflowutils.jl
  - pipelinesearch.jl
- - runtests.jl
  - test_automl.jl
  - test_automlflow.jl
- CondaPkg.toml
- Project.toml
- todo
- - automlflowtsprediction.jl
  - AutoTS.jl
  - carettspredictor.jl
  - main.jl
  - mlflowutils.jl
- - runtests.jl
  - test_caret_tspredictor.jl
- CondaPkg.toml
- Project.toml
- .codecov.yml
- Project.toml
- cmc.csv
- diabetes.csv
- iris.csv
- profb.csv
- testdata.csv
- testdata.csv.bz2
- automl_precompile.jl
- automl_sysimage.jl
- CondaPkg.toml
- Dockerfile
- iris.csv
- iris_reg.csv
- main.jl
- Project.toml
- run.sh
- automl_precompile.jl
- automl_sysimage.jl
- CondaPkg.toml
- Dockerfile
- iris.csv
- iris_reg.csv
- main.jl
- Project.toml
- run.sh
- automl_precompile.jl
- automl_sysimage.jl
- CondaPkg.toml
- Dockerfile
- iris.csv
- iris_reg.csv
- main.jl
- Project.toml
- run.sh
- - index.md
- make.jl
- Project.toml
- - automl_shap.jl
  - Project.toml
- - main.jl
  - Project.toml
  - twoblocks.jl
- - argo-submit.png
  - argo-workflow.png
  - cluster-template.png
  - cluster.png
  - dashboard.png
  - e2eai-backup.ipynb
  - e2eai-components.png
  - e2eai-long.ipynb
  - e2eai-sphere.png
  - e2eai.ipynb
  - e2eai.slides.html
  - e2eai.yaml
  - eu-funding.png
  - flowchart.png
  - high-comp.png
  - inventory.png
  - juliacon2024_e2eai.pdf
  - lifecycle.png
  - low-comp.png
  - low-high-comp.png
  - lowcode.png
  - mlpipeline.png
  - nodes.txt
  - Project.toml
  - rise.css
  - SUNRISE-6G_logo.png
  - SUNRISE-6G_QR.png
  - template.png
  - topdf.sh
  - yaml-template.png
  - yamlcontents.png
- - automlpipeline.yaml
  - demo.sh
  - dockerbuild.sh
  - Dockerfile
  - interactive-pod.yaml
  - k8commands.sh
  - main.jl
  - permissions.yaml
  - Project.toml
  - twoblocks.jl
- - automlpipeline.yaml
  - demo.sh
  - dockerbuild.sh
  - Dockerfile
  - interactive-pod.yaml
  - k8commands.sh
  - main.jl
  - permissions.yaml
  - Project.toml
  - twoblocks.jl
- discourse_zevelev.jl
- double_parallel_example.jl
- juliacon2020.jl
- juliacon2021.jl
- Project.toml
- sample_pipeline_expressions.jl
- single_parallel_example.jl
- thread_example.jl
- .gitignore
- .latexmkrc
- abstracttypes.jl
- algorithm.sty
- algorithmic.sty
- amlp.bib
- amlp.bib.old
- amlpcode.tex
- bib.tex
- complexity-surrogate.tex
- doi.txt
- header.tex
- jlcode.sty
- journal_dat.tex
- juliacon.bst
- juliacon.cls
- juliagraphs.png
- logojuliacon.pdf
- oneblock-all-all.tex
- oneblock-all-one.tex
- oneblock-skrf.tex
- openmldata.tex
- paper.tex
- paper.yml
- prep.rb
- t-test.R
- twoblocks-all-one.tex
- AutoMLPipeline.jl
- skcrossvalidator.jl
- sklearners.jl
- skpreprocessor.jl
- runtests.jl
- test_basefilter.jl
- test_skcrossvalidator.jl
- test_sklearner.jl
- test_skpreprocessing.jl
.gitattributes
.gitignore
.whitesource
CITATION.cff
CondaPkg.toml
CONTRIBUTORS.md
LICENSE
MAINTAINERS.md
Project.toml
README.md
renovate.json
travis.yml

# Installation Guide

1. Get the code

git clone https://github.com/IBM/AutoMLPipeline.jl

Downloads the entire project code from GitHub to your computer.

cd AutoMLPipeline.jl

Moves into the project folder you just downloaded.

2. Docker

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Docker Desktop Needed to build and run containers. Install it and keep it running in the background.

docker build -f DockerizedAutoAD/Dockerfile -t automlpipeline-jl .

Builds a runnable image based on the Dockerfile.

docker run -p 8080:80 automlpipeline-jl

Runs the built image as an actual container.

✅ Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.