# run the script to set up the sqlite and the mongodb databases that modeldb will use # this also starts mongodb # ***IMPORTANT NOTE: This clears any previous modeldb databases. In the following, refers to the directory into which you have cloned the modeldb repo and is 0.9.3 or 0.10.0 depending on your thrift version (check by running thrift -version). ModelDB is composed of three components: the ModelDB server, the ModelDB client libraries, and the ModelDB frontend. Pip install -r /client/python/requirements.txtįor Linux, you can also refer to this script. The following instructions are for 0.9.3 cd is the installation directory # ModelDB works with thrift 0.9.3 and 0.10.0. Sudo apt-get install -y mongodb-org # further instructions here: # install thrift. Sudo apt-get install nodejs # may need to symlink node to nodejs. Sudo apt-get install sbt # for spark.ml client only To run ModelDB with Docker, but without Docker Compose, see detailed instructions. If you have Docker Compose installed, you can bring up a ModelDB server with just a couple commands.
ModelDB frontend: See how ModelDB can visualize model data and resultsĢ017.02.08: ModelDB publicly available! Try it out and contribute.ModelDB client: See how ModelDB can be integrated into different ML workflows.See samples for spark.ml here and those for scikit-learn here. Incorporating ModelDB into a scikit-learn / spark.ml workflow is as simple as appending Sync or _sync to relevant methods in the respective libraries. The native clients can automatically extract relevant pieces of model data as the model is being built and sync them with ModelDB. Unlike the Light API, native clients do not require the user to explicitly provide model data to ModelDB. see here and here respectively).Īlternatively, ModelDB native clients for spark.ml and scikit-learn can be used to perform automatic, fine-grained logging. ModelDB's Light API can be used with any ML environment to sync model metrics and metadata or even entire config files by calling a few functions (e.g. ModelDB Configurable Graph Parameters How does it work? operations like filter by hyperparameter, group by datasets) and to build custom charts showing model performance. The frontend provides functionality to slice and dice this data along various attributes (e.g. The ModelDB frontend provides rich summaries and graphs showing model data. ModelDB native clients can be used for advanced support in spark.ml and scikit-learn. ModelDB can be used with any ML environment via the ModelDB Light API. It ingests models and associated metadata as models are being trained, stores model data in a structured format, and surfaces it through a web-frontend for rich querying. ModelDB is an end-to-end system to manage machine learning models. Incorporate ModelDB into your ML workflow.