> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Jupyter Notebook Management
> Work with JupyterLab in Wherobots Cloud, including executing notebooks, importing custom Python modules, monitoring Spark jobs, and exporting notebooks for job submission.
After starting a notebook instance, Wherobots Cloud opens a pre-configured JupyterLab environment where you can write and execute spatial queries, visualize geospatial data, and build analytics workflows.
For information on starting a runtime and creating a notebook instance, see [Notebook Instance Management](/develop/notebook-management/notebook-instance-management).
The introductory notebook appears when you first open a Wherobots Cloud Notebook. After that, you will see the JupyterLab **Launcher**.
## Before you start
The following is required to manage a Wherobots Notebook:
* An account within a Community, Professional, or Enterprise Edition Organization. For more information, see [Create an Account](/get-started/wherobots-cloud/create-account).
## Available kernels
Wherobots Cloud provides two kernels for your Jupyter Notebooks:
* **Python kernel (ipykernel)** -- for Python-based spatial workflows using Apache Sedona and WherobotsDB.
* **Scala kernel (Scala)** -- for Scala-based spatial workflows.
To create a new notebook with either kernel, click **File > New Launcher** in JupyterLab and select the kernel.
## Execute notebook cells
### Run all cells
To execute all code cells in a notebook, do the following:
1. Click **Run** in the JupyterLab toolbar.
2. Click **Run All Cells**.
When you first execute a WherobotsDB code cell, you may see the following warning:
```
WARN TaskSchedulerImpl: Initial job has not accepted any resources;
check your cluster UI to ensure that workers are registered and have sufficient resources
```
This is expected behavior. Executors take 1-5 minutes to start depending on the runtime size.
### Monitor jobs with the Spark Web UI
The Spark Web UI helps you monitor running jobs, analyze query performance, and identify bottlenecks. To access it, click **Sedona Spark** in JupyterLab and select the correct port number.
To retrieve the port number programmatically, run the following in a notebook cell:
```python theme={"system"}
# Get the Spark Web UI port number
spark_ui_port = sedona.sparkContext.uiWebUrl.split(":")[-1]
print(spark_ui_port)
```
For more information, see [Web UI](https://spark.apache.org/docs/latest/web-ui.html) in the Apache Spark documentation.
## Import custom Python modules
You can package custom Python modules as a zip file and import them into your notebook. This is useful for reusing shared utility functions across notebooks and job submissions.
### Create and upload a module
1. On your local machine, create a directory for your module:
```shell theme={"system"}
mkdir zipmoduletest
```
2. Create a Python file with your module code. For example, create `zipmoduletest/hellosedona.py`:
```python theme={"system"}
def hello(input):
return 'hello ' + str(input)
```
3. Add an empty `__init__.py` file to make the directory a valid Python package:
```shell theme={"system"}
touch zipmoduletest/__init__.py
```
4. Verify the directory structure:
```shell theme={"system"}
ls zipmoduletest
```
The output should show:
```
__init__.py hellosedona.py
```
5. Zip the module files:
```shell theme={"system"}
cd zipmoduletest && zip -r9 ../zipmoduletest.zip *
```
6. Upload `zipmoduletest.zip` to Wherobots Managed Storage or your integrated Amazon S3 bucket. For more information, see [Notebook and Data Storage](../storage-management/storage).
### Use the module in a notebook
After uploading, add the zip file to the Spark context and import your module:
```python theme={"system"}
# Add the zip file to the Spark context
sedona.sparkContext.addPyFile('s3:///path-to-file/zipmoduletest.zip')
# Import and use the custom module
from zipmoduletest.hellosedona import hello
result = hello("Sedona")
print(result)
```
Output:
```
hello Sedona
```
You can also use `addPyFile` to import custom Python modules in job submissions, not just notebooks.
## Access data from Amazon S3
For information on reading and writing data from an integrated Amazon S3 bucket, see [Access Integrated Storage in a Notebook](../storage-management/s3-storage-integration#access-integrated-storage-in-a-notebook).
To use new storage integrations or catalogs in your notebooks, you must start a new runtime. Notebooks can only access storage integrations or catalogs that were created before the runtime started.
## Open a notebook by path
To open a specific notebook file, do the following:
1. Click **File > Open from path**.
2. Enter the notebook path.
3. Click **Open**.
## Export notebooks
You can export notebooks as executable scripts for use with [WherobotsRunOperator](../run-operator) job submissions.
### Export a Python notebook
To export a Python notebook as an executable script, do the following:
1. Click **File** in the JupyterLab toolbar.
2. Hover over **Save and Export Notebook As...**
3. Select **Executable Script**.
The `.py` file will download to your machine.
### Export a Scala notebook
To export a Scala notebook as an executable script, do the following:
1. Click **File** in the JupyterLab toolbar.
2. Hover over **Save and Export Notebook As...**
3. Select **Executable Script**.
The `.scala` file will download to your machine.
The exported Scala file does not include a `main` class. To make it runnable, wrap your code (excluding `import` statements) in an `App` object:
```scala theme={"system"}
object MyApp extends App {
// Your exported code here (excluding import statements)
}
```
You cannot execute `.scala` files in a Python kernel. Use the Scala kernel to run Scala code.
You can import the Scala executable file into `sedona-maven-example/src/main/scala/com/wherobots/sedona/` for job submission.
### Build a JAR file from a Scala notebook
To package your Scala notebook as a JAR file for job submission, do the following:
1. Click **File > New Launcher** in JupyterLab.
2. Open **Terminal**.
3. Navigate to the `sedona-maven-example` directory and build the project:
```shell theme={"system"}
cd sedona-maven-example
mvn clean package
```
4. In the JupyterLab file browser, locate the `target` folder.
5. Right-click `sedonadb-example-0.0.1.jar` and select **Download**.
You can add custom dependencies to the `pom.xml` file located at `notebook-example/scala/sedona-maven-example`.
Once you have the JAR file, refer to [WherobotsRunOperator](../run-operator) to create a job.
## Session expiration
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