> ## 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. # Quickstart > Launch a barebone GPU-accelerated workload This quickstart deploys a barebone GPU-accelerated workload, a Python container that prints the result of `nvidia-smi`. It illustrates the basic components of a single run and how you can deploy one. ## What you will do

* Launch a GPU-accelerated workload * Set up a runtime for your model * Run a simple command: `nvidia-smi` ## Installation and setup After creating a free account on [VESSL](https://app.vessl.ai), install our Python package and get an API authentication. Set your primary **Organization** and **Project** for your account and let's get going. ```bash theme={null} pip install --upgrade vessl vessl configure ``` If you encounter the error `ModuleNotFoundError: No module named 'packaging'`, please run the command `pip install packaging`. ## Writing the YAML file Launching a workload on VESSL begins with writing a YAML file. Our quickstart YAML is in four parts: * Compute resource -- typically in terms of GPUs -- this is defined under `resources` * Runtime environment that points to a Docker Image -- this is defined under `image` * Run commands executed inside the workload as defined under `run` Let's start by creating `quickstart.yaml` and define the key-value pairs one by one. `resources` defines the hardware specs you will use for your run. Here's an example that uses our managed cloud to launch an A10 instance. You can see the full list of compute options and their string values for `preset` under **Resources**. Later, you will be able to add and launch workloads on your private cloud or on-premises clusters simply by changing the value for `cluster`. ```yaml theme={null} resources: cluster: vessl-oci-sanjose preset: gpu-a10-small ``` VESSL uses Docker images to define a runtime. We provide a set of base images as a starting point but you can also bring your own custom Docker images by referencing hosted images on **Docker Hub** or **Red Hat Quay**. You can find the full list of images and the dependencies for the latest build [here](https://quay.io/repository/vessl-ai/kernels?tab=tags\&tag=py39-202310120824). Here, we'll use the latest go-to PyTorch container from **NVIDIA NGC**. ```yaml theme={null} resources: cluster: vessl-oci-sanjose preset: gpu-a10-small image: quay.io/vessl-ai/torch:2.3.1-cuda12.1 ``` Now that we defined the specifications of the compute resource and the runtime environment for our workload, let's run `nvidia-smi`. We can do this by defining a pair of `workdir` and `command` under `run`. ```yaml theme={null} resources: cluster: vessl-oci-sanjose preset: gpu-a10-small image: quay.io/vessl-ai/torch:2.3.1-cuda12.1 run: - workdir: /root command: nvidia-smi ``` Finally, let's polish up by giving our **Run** a name and description. Here's the completed `quickstart.yaml`: ```yaml theme={null} name: Quickstart description: Run "nvidia-smi" resources: cluster: vessl-oci-sanjose preset: gpu-a10-small image: quay.io/vessl-ai/torch:2.3.1-cuda12.1 run: - workdir: /root command: nvidia-smi ``` ## Running the run Now that we have a completed YAML, we can run the workload with `vessl run`. `vessl run create -f quickstart.yaml`

Running the command will verify your YAML and show you the current status of the run. Click the output link in your terminal to see full details and a realtime logs of the **Run** on the web, including the result of the run command.

## Behind the scenes When you `vessl run`, VESSL performs the following as defined in `quickstart.yaml`: 1. Launch an empty Python container on the cloud with 1 NVIDIA A10 GPU. 2. Configure runtime with CUDA compute-capable PyTorch 22.09. 3. Execute `nvidia-smi` and print the result. ## Using our web interface You can repeat the same process on the web. Head over to your **Organization**, select a project, and create a **New run**.