Today we're launching Continuous Machine Learning (CML), a new open-source project for CI/CD with ML. Use it to automate parts of your ML workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets. Let's bring the power of DevOps to ML or MLOps.
Today, the DVC team is releasing a new open-source project called Continuous Machine Learning, or CML (https://cml.dev) to mainstream the best engineering practices of CI/CD to AI and ML teams. CML helps to organize MLOps infrastructure on top of the traditional software engineering stack instead of creating separate AI platforms.
Continuous integration and continuous delivery (CI/CD) is a widely-used software engineering practice. It's a validated approach to increasing the agility of software development without sacrificing stability. But why haven't CI/CD practices taken root in machine learning and data science so far?
We see three substantial technical barriers to using standard CI systems with machine learning projects:
-0.35% precisiondoes not answer the question if the ML model is good or not. Detailed reports with metrics and plots are needed to make a good/bad model discussion
Since the beginning, our motivation has been helping ML teams benefit from DevOps. We started DVC because we knew that data management would be a crucial bottleneck, and sure enough, DVC was a big step towards making pipelines and experiments manageable and reproducible. But conversations with our community have brought us to one conclusion again and again: CI/CD for ML is the holy grail.
Over the last 3 years, we've reached some big milestones:
We built DVC to address the ML data management problem. Recently, we released DVC 1.0, marking a new and more stable era for our API.
DVC has become a core part of many ML team's daily operations. The latest ThoughtWorks Technology Radar says:
"… it [DVC] has become a favorite tool for managing experiments in machine learning (ML) projects. Since it's based on Git, DVC is a familiar environment for software developers to bring their engineering practices to ML practice."
An extraordinary team and community have emerged around DVC:
Now that DVC is maturing, we're ready to take the next step: we want to revolutionize the ML development processes. We want ML experiments to have greater visibility to teammates, shorter feedback loops, and more reproducibility. We want teams to spend less time managing their computing resources and experiments, and more time building value. The goal is to extend the amazing results of DevOps from software development to ML and MLOps.
Today, we're releasing an open-source project https://cml.dev to close the gap between machine learning and software development practices.
CML is a library of functions used inside CI/CD runners to make ML compatible with GitHub Actions and GitLab CI. We've created functions to:
The workflow and visual reports are customizable by modifying the CI
configuration file in your GitHub
./github/workflows/*.yaml or GitLab
.gitlab-ci.yml project. Use CML functions in conjunction with your own ML
model training and testing scripts to create your own automated workflow and
# GitLab workflow in '.gitlab-ci.yml' file stages: - cml_run cml: stage: cml_run image: iterativeai/cml:0-dvc2-base1 script: - dvc pull data --run-cache - pip install -r requirements.txt - dvc repro # Compare metrics to master - git fetch --prune - dvc metrics diff --show-md master >> report.md # Visualize loss function diff - dvc plots diff --target loss.csv --show-vega master > vega.json - vl2png vega.json > plot.png - cml publish --md plot.png >> report.md - dvc push data --run-cache - cml send-comment report.md
In this example all the CML functions are defined in the docker images that
is used in the workflow -
iterativeai/cml:0-dvc2-base1. Users can specify any
docker image. The only restriction is that the CML library need to be installed
to enable all the CML commands for the reporting and graphs:
npm i @dvcorg/cml
Examples of docker images can be found in
docker directory of the CML the
repository: CML repository.
As you can see, CML is based on the assumption that MLOps can work with traditional engineering tools. It shouldn't require an entirely separate platform. We're excited about a world where DevOps practitioners can work fluently on both software and ML aspects of a project.
CML and DVC are related projects under the umbrella of the same team, but will have separate websites and independent development. The CML project is hosted on a new web site: https://cml.dev. The source code and issue tracker is on GitHub: https://github.com/iterative/cml
For support and communications, the DVC Discord server is still the place to go:
https://dvc.org/chat We've made a new
#cml channel there to discuss CML, CI/CD
for ML and other MLOps related questions.
With the rise of AI/ML teams and ML platforms in addition to the software engineering stack, we believe that the industry needs a single technology stack to work with software as well as AI projects. A simple layer of a tool is required to close the gap between AI projects and software projects to fit them into the existing stack and CML is the way to make it.
Our philosophy is that ML projects, and MLOps practices, should be built on top of traditional engineering tools and not as a separate stack. A simple layer of tools will be required to close the gap, and CML is part of this ecosystem. We think this is the future of MLOps.