wehub-resource-sync
57 lines
1.7 KiB
Markdown
57 lines
1.7 KiB
Markdown
# UI Preview
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Deploy a live preview of the MLflow UI as a [Databricks App](https://docs.databricks.com/aws/en/dev-tools/databricks-apps/) when a PR modifies the frontend (`mlflow/server/js/`).
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## How it works
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1. Add the `ui-preview` label to a PR with UI changes
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2. The [UI Preview workflow](../workflows/ui-preview.yml) builds the frontend and deploys it to a Databricks App
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3. A comment with the preview URL is posted on the PR
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4. The app is automatically deleted when the PR is closed
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## Access
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Preview apps are only accessible to core maintainers with workspace access.
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## API access
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To query or add data to a preview app, set the following environment variables:
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```bash
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export DATABRICKS_HOST="https://..."
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export DATABRICKS_CLIENT_ID="..."
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export DATABRICKS_CLIENT_SECRET="..."
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export APP_URL="..."
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```
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Then, obtain an access token:
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```bash
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export TOKEN=$(curl -s -X POST "$DATABRICKS_HOST/oidc/v1/token" \
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-d "grant_type=client_credentials&client_id=$DATABRICKS_CLIENT_ID&client_secret=$DATABRICKS_CLIENT_SECRET&scope=all-apis" \
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| jq -r '.access_token')
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```
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Once the token is obtained, run the following command to verify it works:
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```bash
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curl -s "$APP_URL/api/2.0/mlflow/experiments/search" \
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-X POST -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
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-d '{"max_results": 10}' | jq .
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```
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You can also use the MLflow Python client:
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```bash
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export MLFLOW_TRACKING_URI="$APP_URL"
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export MLFLOW_TRACKING_TOKEN="$TOKEN"
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```
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```python
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import mlflow
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mlflow.search_experiments(max_results=10)
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```
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See [Connect to Databricks Apps](https://docs.databricks.com/aws/en/dev-tools/databricks-apps/connect-local) for more details on authentication.
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