ta stores you want to migrate, then upgrade ZenML.Decide the ZenML deployment model that you want to follow for your projects. See the ZenML deployment documentation for available deployment scenarios. If you decide on using a local or remote ZenML server to manage your pipelines, make sure that you first connect your client to it by running zenml connect.

Use the zenml pipeline runs migrate CLI command to migrate your old pipeline runs:

If you want to migrate from a local SQLite metadata store, you only need to pass the path to the metadata store to the command, e.g.:

zenml pipeline runs migrate PATH/TO/LOCAL/STORE/metadata.db

If you would like to migrate any other store, you will need to set --database_type=mysql and provide the MySQL host, username, and password in addition to the database, e.g.:

zenml pipeline runs migrate DATABASE_NAME \

--database_type=mysql \

--mysql_host=URL/TO/MYSQL \

--mysql_username=MYSQL_USERNAME \

--mysql_password=MYSQL_PASSWORD

💾 The New Way (CLI Command Cheat Sheet)

Deploy the server

zenml deploy --aws (maybe don’t do this :) since it spins up infrastructure on AWS…)

Spin up a local ZenML Server

zenml up

Connect to a pre-existing server

zenml connect (pass in URL / etc, or zenml connect --config + yaml file)

List your deployed server details

zenml status

The ZenML Dashboard is now available

The new ZenML Dashboard is now bundled into the ZenML Python package and can be launched directly from Python. The source code lives in the ZenML Dashboard repository.

To launch it locally, simply run zenml up on your machine and follow the instructions:

$ zenml up

Deploying a local ZenML server with name 'local'.

Connecting ZenML to the 'local' local ZenML server (http://127.0.0.1:8237).

Updated the global store configuration.

Connected ZenML to the 'local' local ZenML server (http://127.0.0.1:8237).

The local ZenML dashboard is available at 'http://127.0.0.1:8237'. You can

connect to it using the 'default' username and an empty password.