> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/reference/inference/inference-cli/server.md).

# Control the Inference Server

The `inference server` command provides a control layer around the HTTP server that exposes Inference.

{% hint style="success" %}
To see the details of the command, run:

```bash
inference server --help
```

A help guide is also available for each sub-command:

```bash
inference server start --help
```

{% endhint %}

## `inference server start`

Starts a local Inference Server. It optionally takes a port number (default is 9001) and only starts the Docker container if there is not already a container running on that port.

If you would rather run your server on a virtual machine in Google Cloud or Amazon Cloud, see [Deploy to Cloud](/reference/inference/inference-cli/cloud.md).

Before you begin, ensure that you have Docker installed on your machine. Docker provides a containerized environment, allowing the Inference Server to run in a consistent and isolated manner, regardless of the host system. If you haven't installed Docker yet, you can get it from [Docker's official website](https://www.docker.com/get-started).

The CLI automatically detects the device you are running on and pulls the appropriate Docker image.

```bash
inference server start --port 9001 [-e {optional_path_to_file_with_env_variables}]
```

The parameter `--env-file` (or `-e`) is the optional path to a `.env` file that is loaded into your Inference Server when the values of internal parameters need to be adjusted. Any value passed explicitly as a command parameter is considered more important and shadows the value defined in the `.env` file under the same target variable name.

### Volume mounts

Use the `--volume` (or `-v`) flag to mount a host directory into the container. This is useful for persisting files written by Workflows (for example via the `local_file_sink` block) to your local machine.

```bash
inference server start --volume /host/path:/container/path
```

You can mount multiple volumes by repeating the flag:

```bash
inference server start --volume /host/data:/data --volume /host/models:/models:ro
```

The optional `:ro` suffix mounts the volume as read-only. If omitted, the volume is mounted read-write.

### Development mode

Use the `--dev` flag to start the Inference Server in development mode. Development mode enables the Inference Server's built-in notebook environment for easy testing and development.

```bash
inference server start --dev
```

Visit `localhost:9001` in your browser to see the Inference landing page, which links to resources and examples. From the landing page, select "Jump Into an Inference Enabled Notebook" to open the built-in JupyterLab environment in a new tab. It comes preloaded with example notebooks and all of the dependencies needed to run Inference.

### Tunnel

Use the `--tunnel` flag to start the Inference Server with a tunnel that exposes inference to external requests on a TLS-enabled endpoint.

The randomly generated address is shown in the server start output:

```
Tunnel to local inference running on https://somethingrandom-ip-192-168-0-1.roboflow.run
```

## `inference server status`

Checks the status of the local Inference Server.

```bash
inference server status
```

## `inference server stop`

Stops the Inference Server.

```bash
inference server stop
```