UI Preview

Deploy a live preview of the MLflow UI as a Databricks App when a PR modifies the frontend (mlflow/server/js/).

How it works

Add the ui-preview label to a PR with UI changes
The UI Preview workflow builds the frontend and deploys it to a Databricks App
A comment with the preview URL is posted on the PR
The app is automatically deleted when the PR is closed

Access

Preview apps are only accessible to core maintainers with workspace access.

API access

To query or add data to a preview app, set the following environment variables:

export DATABRICKS_HOST="https://..."
export DATABRICKS_CLIENT_ID="..."
export DATABRICKS_CLIENT_SECRET="..."
export APP_URL="..."

Then, obtain an access token:

export TOKEN=$(curl -s -X POST "$DATABRICKS_HOST/oidc/v1/token" \
  -d "grant_type=client_credentials&client_id=$DATABRICKS_CLIENT_ID&client_secret=$DATABRICKS_CLIENT_SECRET&scope=all-apis" \
  | jq -r '.access_token')

Once the token is obtained, run the following command to verify it works:

curl -s "$APP_URL/api/2.0/mlflow/experiments/search" \
  -X POST -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d '{"max_results": 10}' | jq .

You can also use the MLflow Python client:

export MLFLOW_TRACKING_URI="$APP_URL"
export MLFLOW_TRACKING_TOKEN="$TOKEN"

import mlflow

mlflow.search_experiments(max_results=10)

See Connect to Databricks Apps for more details on authentication.