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+# Databricks
+
+## DASK Rapids in Databricks MNMG Cluster
+
+You can launch Dask RAPIDS cluster on a single-node or multi-node GPU Databricks cluster
+
+```{warning}
+It is also possible to use [Spark RAPIDS](https://docs.nvidia.com/spark-rapids/user-guide/latest/getting-started/databricks.html) with Dask on the same Databricks cluster. To do this, the user
+must provide an init script that downloads the `rapids-4-spark-xxxx.jar`` plugin and then configure Spark to load this plugin.
+```
+
+### Init script
+
+Before creating the cluster, we will need to create an [initialization script](https://docs.databricks.com/en/init-scripts/index.html) to install Dask and the RAPIDS Accelerator for Apache Spark.
+
+Databricks recommends storing all cluster-scoped init scripts using workspace files. Each user has a Home directory configured under the `/Users` directory in the workspace. Navigate to your home directory in the UI and select **Create** > **File** from the menu, create an `init.sh` scripts with contents:
+
+```python
+#!/bin/bash
+
+set -e
+
+echo "DB_IS_DRIVER = $DB_IS_DRIVER"
+echo "DB_DRIVER_IP = $DB_DRIVER_IP"
+
+pip install "dask[complete]"
+
+if [[ $DB_IS_DRIVER = "TRUE" ]]; then
+  echo "This node is the Dask scheduler."
+  dask scheduler &
+else
+  echo "This node is a Dask worker."
+  echo "Connecting to Dask scheduler at $DB_DRIVER_IP:8786"
+  # Wait for the scheduler to start
+  while ! nc -z $DB_DRIVER_IP 8786; do
+    echo "Scheduler not available yet. Waiting..."
+    sleep 10
+  done
+  dask cuda worker tcp://$DB_DRIVER_IP:8786 &
+fi
+
+```
+
+NOTE: The above script will be packaged as a library to be imported instead.
+
+### Launch a Databricks cluster
+
+Navigate to the **All Purpose Compute** tab of the **Compute** section in Databricks and select **Create Compute**. Name your cluster and choose "Multi node" or "Single node".
+
+![Screenshot of the Databricks compute page](../images/databricks-create-compute.png)
+
+In order to launch a GPU node uncheck **Use Photon Acceleration**. Then choose Databricks ML GPU Runtime from the drop down. For instance,`13.3 LTS ML (GPU, Scala 2.12, Spark 3.4.1)`. Once you have done this you should be able to select one of the GPU-accelerated instance types for the Driver and Worker nodes.
+
+Optional to enable autoscale for worker nodes based on load.
+
+![Screenshot of selecting a g4dn.xlarge node type](../images/databricks-ML-runtime.png)
+
+Expand the **Advanced Options** section and open the **Init Scripts** tab and add the file path to the init script starting with `/Users/`.
+
+You can also configure cluster log delivery, which will write the init script logs to DBFS in a subdirectory called `dbfs:/cluster-logs/<cluster-id>/init_scripts/`. Refer to [docs](https://docs.databricks.com/en/init-scripts/logs.html) for more information.
+
+Once your cluster has started create a new notebook or open an existing one.
+
+### Test Rapids
+
+Connect to the dask client using the scheduler address and submit tasks.
+
+```python
+from dask.distributed import Client
+import os
+
+client = Client(f'{os.environ["SPARK_LOCAL_IP"]}:8786')
+```
+
+You can run also this workflow example (link) to get started on using Dask X Spark RAPIDS. Refer to blog (link) for more information.