[localhost:8889/lab?token=my-token](http://localhost:8889/lab?token=my-token).

To stop the container, in the terminal press `ctrl`+`c`.

To access an existing notebook on your system, you can use a
[bind mount](/storage/bind-mounts/). Open a terminal and
change directory to where your existing notebook is. Then,
run the following command based on your operating system.

{{< tabs >}}
{{< tab name="Mac / Linux" >}}

```console
$ docker run --rm -p 8889:8888 -v "$(pwd):/home/jovyan/work" quay.io/jupyter/base-notebook start-notebook.py --NotebookApp.token='my-token'
```

{{< /tab >}}
{{< tab name="Windows (Command Prompt)" >}}

```console
$ docker run --rm -p 8889:8888 -v "%cd%":/home/jovyan/work quay.io/jupyter/base-notebook start-notebook.py --NotebookApp.token='my-token'
```

{{< /tab >}}
{{< tab name="Windows (PowerShell)" >}}

```console
$ docker run --rm -p 8889:8888 -v "$(pwd):/home/jovyan/work" quay.io/jupyter/base-notebook start-notebook.py --NotebookApp.token='my-token'
```

{{< /tab >}}
{{< tab name="Windows (Git Bash)" >}}

```console
$ docker run --rm -p 8889:8888 -v "/$(pwd):/home/jovyan/work" quay.io/jupyter/base-notebook start-notebook.py --NotebookApp.token='my-token'
```

{{< /tab >}}
{{< /tabs >}}

The `-v` option tells Docker to mount your current working directory to
`/home/jovyan/work` inside the container. By default, the Jupyter image's root
directory is `/home/jovyan` and you can only access or save notebooks to that
directory in the container.

Now you can access [localhost:8889/lab?token=my-token](http://localhost:8889/lab?token=my-token) and open notebooks contained in the bind mounted directory.

To stop the container, in the terminal press `ctrl`+`c`.

Docker also has volumes, which are the preferred mechanism for persisting
data generated by and used by Docker containers. While bind mounts are dependent
on the directory structure and OS of the host machine, volumes are completely
managed by Docker.

## Save and access notebooks

When you remove a container, all data in that container is deleted. To save
notebooks outside of the container, you can use a [volume](/engine/storage/volumes/).

### Run a JupyterLab container with a volume

To start the container with a volume, open a terminal and run the following command

```console
$ docker run --rm -p 8889:8888 -v jupyter-data:/home/jovyan/work quay.io/jupyter/base-notebook start-notebook.py --NotebookApp.token='my-token'
```

The `-v` option tells Docker to create a volume named `jupyter-data` and mount it in the container at `/home/jovyan/work`.

To access the container, in a web browser navigate to
[localhost:8889/lab?token=my-token](http://localhost:8889/lab?token=my-token).
Notebooks can now be saved to the volume and will accessible even when
the container is deleted.

### Save a notebook to the volume

For this example, you'll use the [Iris Dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html) example from scikit-learn.

1. Open a web browser and access your JupyterLab container at [localhost:8889/lab?token=my-token](http://localhost:8889/lab?token=my-token).

2. In the **Launcher**, under **Notebook**, select **Python 3**.

3. In the notebook, specify the following to install the necessary packages.

   ```console
   !pip install matplotlib scikit-learn
   ```

4. Select the play button to run the code.

5. In the notebook, specify the following code.

   ```python
   from sklearn import datasets

   iris = datasets.load_iris()
   import matplotlib.pyplot as plt

   _, ax = plt.subplots()
   scatter = ax.scatter(iris.data[:, 0], iris.data[:, 1], c=iris.target)
   ax.set(xlabel=iris.feature_names[0], ylabel=iris.feature_names[1])
   _ = ax.legend(
      scatter.legend_elements()[0], iris.target_names, loc="lower right", title="Classes"
   )
   ```

6. Select the play button to run the code. You should see a scatter plot of the
   Iris dataset.

7. In the top menu, select **File** and then **Save Notebook**.

8. Specify a name in the `work` directory to save the notebook to the volume.