# Compute-specific configuration
<a name="jupyterlab-compute-configure"></a>

 Amazon SageMaker Unified Studio provides a set of Jupyter magic commands. Magic commands, or magics, enhance the functionality of the IPython environment. For more information about the magics that Amazon SageMaker Unified Studio provides, run %help in a notebook. 

 Compute-specific configurations can be configured by %%configure Jupyter magic. The %%configure magic takes a json-formatted dictionary. To use %%configure magic, please specify the compute name in the argument -n. Include —f will restart the session to forcefully apply the new configuration, otherwise this configuration will apply only when next session starts. 

## Configure an EMR Spark session
<a name="jupyterlab-configure-emr-session"></a>

 When working with EMR on EC2 or EMR Serverless, %%configure command can be used to configure the Spark session creation parameters. Using conf settings, you can configure any Spark configuration that's mentioned in the configuration documentation for Apache Spark. 

```
%%configure -n compute_name -f 
{ 
    "conf": { 
        "spark.sql.shuffle.partitions": "36"
     }
}
```

## Configure a Glue interactive session
<a name="jupyterlab-configure-glue-session"></a>

Use the `--` prefix for run arguments specified for Glue. 

```
%%configure -n project.spark.compatibility -f
{
   "––enable-auto-scaling": "true"
   "--enable-glue-datacatalog": "false"
}
```

For more information on job parameters, see Job parameters.

You can update Spark configuration via %%configure when working with Glue with --conf in configure magic. You can configure any Spark configuration that's mentioned in the configuration documentation for Apache Spark. 

```
%%configure -n project.spark.compatibility -f 
{ 
    "--conf": "spark.sql.shuffle.partitions=36" 
}
```