How can I set the number of CPU and GPU cores for each Ray worker node in a Databricks cluster?
To set the number of CPU and GPU cores for each Ray worker node in a Databricks cluster, you can use the `num_cpus_worker_node` and `num_gpus_worker_node` configuration parameters. Databricks recommends setting `num_cpus_worker_node` to the number of CPU cores per Spark worker node and `num_gpus_worker_node` to the number of GPUs per Spark worker node. This will ensure that each Spark worker node launches one Ray worker node that fully utilizes the resources of the Spark worker node.

Here's an example of how to set these configuration parameters in a Databricks cluster:

1. Go to the Databricks workspace and create a new cluster or select an existing cluster.
2. Click on the "Edit" button to modify the cluster configuration.
3. In the "Spark Config" section, add the following configuration parameters:
```python
spark.task.resource.gpu.amount 0
spark.executor.memory 4g
num_cpus_worker_node <number of CPU cores per Spark worker node>
num_gpus_worker_node <number of GPUs per Spark worker node>
```
Replace `<number of CPU cores per Spark worker node>` and `<number of GPUs per Spark worker node>` with the actual number of CPU and GPU cores for your Spark worker nodes.

4. Click on the "Confirm" button to save the changes.

By setting these configuration parameters, you can ensure that each Ray worker node has the appropriate number of CPU and GPU cores for your Databricks cluster.