> ## Documentation Index > Fetch the complete documentation index at: https://docs.kinetica.com/llms.txt > Use this file to discover all available pages before exploring further. # JupyterLab Tutorial > Learn how to set up and use Jupyter Lab with Kinetica ## Introduction JupyterLab is an integrated environment that can streamline the development of Python code and Machine Learning (ML) models in Kinetica. Jupyter notebooks integrate code execution, debugging, documentation, and visualization in a single document for consumption by multiple audiences. With JupyterLab, Jupyter notebooks can easily operate on a live Kinetica instance.

The development process is streamlined because sections of code (or cells) can be run iteratively while updating results and graphs. JupyterLab can be accessed from a web browser and supports a Python console with tab completions, tooltips, and visual output. One of the difficulties of using Jupyter notebooks with Kinetica in the past had been that environments needed to be installed with all the necessary dependencies. In this tutorial, the process will be simplified with a Docker image that integrates the components, so they can run locally on any Intel-based machine. The Docker image integrates the following major components: * JupyterLab * Kinetica 6.2 * CentOS 7 * Python 3.6 The Python environment has the necessary modules for: * Interaction with Kinetica using ODBC or the native API * Creating and executing Kinetica UDF’s * Execution of ML Models on Kinetica (e.g. Pandas, PyTorch, TensorFlow) The Kinetica Intel build does not give GPU-accelerated performance and should be used for development purposes only. ## Prerequisites This tutorial requires the following: * [Docker](#docker) * Kinetica Developer Edition ### Docker Docker can be downloaded from the Docker store: * **Mac**: [https://store.docker.com/editions/community/docker-ce-desktop-mac](https://store.docker.com/editions/community/docker-ce-desktop-mac) * **Linux**: [https://docs.docker.com/install/linux/docker-ce/centos/](https://docs.docker.com/install/linux/docker-ce/centos/) After installing, select the **Advanced** preferences and allocate at least 6GB of memory for the VM as shown below.

## Install All the required code is available in the kinetica-jupyterlab Git repository: * [https://github.com/kineticadb/kinetica-jupyterlab](https://github.com/kineticadb/kinetica-jupyterlab) You can use git clone to fetch a local copy. ```bash theme={null} git clone https://github.com/kineticadb/kinetica-jupyterlab cd kinetica-jupyterlab ``` ### Setting Permissions The two volumes that will be mounted in Docker, docker/share & notebooks, need to be writable by the image's `gpudb` user, which has a `uid` of `1000`. With some operating systems *(osxfs)*, this is managed automatically by Docker. For others, the directories will need to be given permission directly. From the kinetica-jupyterlab directory, run the following: ```bash theme={null} chown -R 1000 docker/share notebooks ``` ### Entering the License Key The database is configured to start automatically, but for this to succeed, a license key must be configured. Edit docker/share/conf/gpudb.conf, uncomment the line with `license_key`, and add the key: ``` # The license key to authorize running. license_key = ``` If the key is invalid, the container will fail to start. ### Pulling the Image This section will demonstrate how to use docker-compose to pull the kinetica/kinetica-jupyterlab image from DockerHub. From the kinetica-jupyterlab directory, go into the docker directory and run docker-compose pull. ```bash theme={null} cd docker docker-compose pull ``` The docker image command can be used to verify the download. ```bash theme={null} docker image list ``` Sample output: ``` REPOSITORY TAG IMAGE ID CREATED SIZE kinetica/kinetica-jupyterlab 6.2 e9702b6e31fb 28 minutes ago 7.35GB centos 7 49f7960eb7e4 7 weeks ago 200MB ``` ## Managing the Container This tutorial uses docker-compose to manage the parameters of the container. This can simplify the configuration process, as all of the settings are in the docker-compose.yml file. ### Starting Run the below docker-compose up command to start the image. The combined log output of Kinetica and JupyterLab will be displayed in the console. This console needs to be open for as long as the container is running. From the kinetica-jupyterlab/docker directory: ```bash theme={null} docker-compose up ``` Sample output: ``` Creating network "docker_default" with the default driver Creating gpudb-jupyterlab-6.x ... done Attaching to gpudb-jupyterlab-6.x [...] gpudb-jupyterlab-6.x | 2018-07-25 23:45:04.516 INFO (2494,5923,r0/gpudb_sl_shtbl ) d0a1758a319b Utils/GaiaHTTPUtils.h:161 - JobId:1011; call_gaia_internal endpoint: /show/table completed in: 0.00193 s ``` ### Using To use a Unix shell in the container, open up a separate console session and run the following from the kinetica-jupyterlab/docker directory: ```bash theme={null} docker-compose exec gpudb /bin/bash su - gpudb ``` ### Stopping To stop the container, use the docker-compose down command. From the kinetica-jupyterlab/docker directory: ```bash theme={null} docker-compose down ``` Sample output: ``` Stopping gpudb-jupyterlab-6.x ... done Removing gpudb-jupyterlab-6.x ... done Removing network docker_default ``` ## Exploring the Environment To access to GAdmin, open URL [http://localhost:8080](http://localhost:8080) and log in as an admin user *(default username/password is* `admin`/`admin` *)*. To access JupyterLab, open URL [http://localhost:8888](http://localhost:8888) and enter password `kinetica`. When logged in, the file browser will appear on the left.

Navigate to the Examples folder, and open notebook ex\_kapi\_io.ipynb. This notebook demonstrates basic interactions between Pandas dataframes and Kinetica via the functions in the KIJO module.

Select **Kernel -> Restart Kernel** and **Run All Cells...** to clear the outputs. Then select the first cell and click the **Play** button to run each cell. Each notebook has a separate Python process or kernel that remembers the variables that were created when cells were executed. Cells can be modified and re-executed without starting from the beginning. A Python console can also be attached to the same kernel as a notebook. Right click on a cell and select **New Console For Notebook**. Enter one of the variables executed from the notebook (e.g. `_test_df`) and then press **Shift+Enter** to see the contents in the console.