ModelDB is an open-source system to version machine learning models including their ingredients code, data, config, and environment and to track ML metadata across the model lifecycle.
Use ModelDB in order to:
Features:
If you are looking for a hosted version of ModelDB, please reach out at modeldb@verta.ai.
This version of ModelDB is built upon its predecessor from CSAIL, MIT. The previous version can be found on Github here. The ModelDB project is now maintained by Verta.ai.
Install Docker (and Docker Compose)
Setup ModelDB via Docker Compose
docker-compose -f docker-compose-all.yaml up
Note: modeldb-backend service needs backend/config/config.yaml to run, either clone the repo before running docker-compose or create the file manually.
Install the ModelDB pip package. Note it comes packaged in the verta package.
pip install verta
Version a model or log a workflow. Alternatively, run any of the detailed examples in our repository.
from verta import Client
client = Client("http://localhost:3000")
proj = client.set_project("My first ModelDB project")
expt = client.set_experiment("Default Experiment")
# log the first run
run = client.set_experiment_run("First Run")
run.log_hyperparameters({"regularization" : 0.5})
# ... model training code goes here
run.log_metric('accuracy', 0.72)
# log the second run
run = client.set_experiment_run("Second Run")
run.log_hyperparameters({"regularization" : 0.8})
# ... model training code goes here
run.log_metric('accuracy', 0.83)
That's it! Navigate to http://localhost:3000 to find the ModelDB Web UI and check out the models you just logged.
For information on debugging the Docker-based ModelDB installation, check here.
Other ways to install ModelDB are:
Official documentation for ModelDB can be found here.
For Getting Started guides, Tutorials, and API reference check out our docs.
To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.
For help, questions, contribution discussions and release announcements, please join us on Slack.
At a high level the architecture of ModelDB in a Kubernetes cluster or a Docker application looks as below:
Volumes : The relational database and the artifact store in backend need volumes attached to enable persistent storage.
Each module in the architecture diagram has a designated folder in this repository, and has their own README covering in depth documentation and contribution guidelines.
Other supporting material for deployment and documentation is at:
As seen from the Architecture ModelDB provides a full stack solution to tracking, versioning and auditing machine learning models. We are open to contributions to any of the modules in form of Pull Requests.
The main skill sets for each module are as below:
Java
development or are interested in database design using technologies like Hibernate
and Liquibase
please take a look at backend README for setup and development instructions.Python
or Scala
development or are interested in building examples notebooks on various ML frameworks logging data to Modeldb please take a look at client README.Node
,React
or Redux
based development please take a look at webapp READMEPlease reach out to us in slack for any assistance in getting started with the development set up or any other feedback.
ModelDB is licensed under Apache 2.0.
Thanks to our many contributors and users.