> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vessl.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Create a new service
Deploy your AI models efficiently using VESSL Service, which supports both **provisioned** and **serverless** deployment methods. This guide will walk you through the steps to create a new service using the **Command Line Interface (CLI)** and the **web console**, with how to enable Serverless Mode.
Before deploying a service, ensure that **VESSL Service is enabled** in the cluster settings. Refer to the **[Endpoint Configuration Guide](https://docs.vessl.ai/guides/clusters/endpoint)** for details on how to enable **VESSL Service** and configure endpoints properly.
## Deploy a new service using CLI
### Using YAML file
To deploy a service using the CLI, you'll first need to define your service configuration in a YAML file. This YAML-based configuration allows you to deploy services programmatically.
If you were using this feature as beta and want to migrate to new version, please refer to [migration guide](/guides/serve/migration).
#### Example YAML configuration:
```yaml theme={null}
name: vessl-test-service
message: vessl-yaml-revision
image: quay.io/vessl-ai/torch:2.3.1-cuda12.1-r5
resources:
cluster: vessl-oci-sanjose
preset: gpu-l4-small-spot
run:
- |
pip install vllm
python -m vllm.entrypoints.openai.api_server --model $MODEL_NAME --max-model-len 4096 --disable-log-requests --api-key token-123
env:
MODEL_NAME: mistralai/Mistral-7B-Instruct-v0.2
ports:
- port: 8000
service:
autoscaling:
max: 2
metric: cpu
min: 1
target: 50
expose: 8000
```
#### Steps to deploy using CLI:
1. **Create or edit your YAML file** to define the service configuration.
2. **Deploy the service** by running the following command in your terminal. Replace `[your-yaml-file].yaml` with the path to your YAML file.:
```bash theme={null}
vessl service create -f [your-yaml-file].yaml
```
If you want to deploy a **serverless mode**, make sure to append `--serverless` flag.
```bash theme={null}
vessl service create -f [your-serverless-yaml-file.yaml] --serverless
```
If deploying to a NodePort cluster, specify the --port option to expose the service on a specific port.
```bash theme={null}
vessl service create -f [your-yaml-file].yaml --port=30010
```
### Using VESSL Hub templates
[VESSL Hub](https://app.vessl.ai/hub) provides service templates for rapid service deployment.
#### Steps to deploy using VESSL Hub templates:
1. Get the key of the template you want to use from the VESSL Hub
```
vessl hub list -t service
```
example output:
```
Service Tasks:
llama-3-chat-api
gemma-2-chat-api
tgi-service
vllm-service
```
2. Start your service by `vessl service create --from-hub=[template-key]`.
Example:
```
vessl service create --from-hub=llama-3-chat-api
```
## Deploy a new service using web console
Deploying through the web console is user-friendly and suitable for those who prefer a graphical interface over command line operations. The interactive demo below will guide you to through the process.
### Provisioned Mode -- Steps to create a new service in the web console
* **Initialize this revision with:**: Select initialization method.
* **Template from VESSL hub**: Use a template from the VESSL Hub.
* **Recent revision configuration**: Select the configuration of the recent revisions.
* **YAML file**: Upload a YAML file or paste its content to initialize the revision.
* **Message**: Enter a message for the revision.
* **Resources**: Select the compute resources and container image you want to use for the Service.
* **Resource**: Select the compute resources you want to use for the Service.
* **Container image**: The Docker image to use for the Revision.
* **Task**:
* **Volumes**: Import or mount code, data
* **Command**: The command to run inside the container. This is similar to running a command in the terminal on your computer.
* **Port**: The port to expose from the container. For example, if you're using a BentoML model server, you'll want to expose port 3000 and use the HTTP protocol to access the service endpoint.
* **Monitoring**: Enable monitoring to track default system metrics from service workers.
* **Healthcheck**: Check API health using the specified port and path.
* **Autoscaling**: Set autoscaling strategy for the revision.
* **Target Metric**: The metric to use for autoscaling - cpu, memory, nvidia.com/GPU, requests.
* **Target Value**: The target value for the metric.
* **Min value**: The minimum number of replicas.
* **Max value**: The maximum number of replicas.
* **Variables**: Environment variables or secret variables to inject into the container.
### Serverless Mode -- Steps to create a new service in the web console:
* **Resources**: Select the compute resources and container image you want to use for the Service.
* **Resource**: Select the compute resources you want to use for the Service. Custom resource specs cannot be set in Serverless mode. If you need to use a custom resource spec, please contact our sales team.
* **Container image**: The Docker image to use for the Revision.
* **Task**:
* **Command**: The command to run inside the container. This is similar to running a command in the terminal on your computer.
* **Port**: The port to expose from the container. For example, if you're using a BentoML model server, you'll want to expose port 3000 and use the HTTP protocol to access the service endpoint. You can open only one port in Serverless mode.
* **Advanced Settings**: Set additional configurations.
* **Variables**: Environment variables or secret variables to inject into the container.
Get information from the Overview Dashboard.