guides/get-started/overview.md

---
title: Overview
description: Launch and scale AI workloads without the hassle of managing infrastructure
icon: "hand-wave"
version: EN
---

## VESSL AI -- Purpose-built cloud for AI

VESSL AI provides a unified interface for training and deploying AI models on the cloud. Simply define your GPU resource and pinpoint to your code & dataset. VESSL AI does the orchestration & heavy lifting for you:
1. Create a GPU-accelerated container with the right Docker Image.
2. Mount your code and dataset from GitHub, Hugging Face, Amazon S3, and more.
3. Launcs the workload on our fully managed GPU cloud.

<CardGroup cols={2}>
  <Card title="One any cloud, at any scale" href="#">
    Instantly scale workloads across multiple clouds. 
    <br/>
    <img className="rounded-md" src="/images/get-started/overview-cloud.png" />
  </Card>

  <Card title="Streamlined interface" href="#">
    Launch any AI workloads with a unified YAML definition.
    <br/>
    <img className="rounded-md" src="/images/get-started/overview-yaml.png" />
  </Card>
  
  <Card title="End-to-end coverage" href="#">
    A single platform for fine-tuning to deployment.
    <br/>
    <img className="rounded-md" src="/images/get-started/overview-pipeline.png" />
  </Card>

  <Card title="A centralized compute platform" href="#">
    Optimize GPU usage and save up to 80% in cloud.
    <br/>
    <img className="rounded-md" src="/images/get-started/overview-gpu.png" />
  </Card>
</CardGroup>

## What can you do?

- Run compute-intensive AI workloads remotely within seconds.
- Fine-tune LLMs with distributed training and auto-failover with zero-to-minimum setup. 
- Scale training and inference workloads horizontally.
- Deploy an interactive web applicaiton for your model.
- Serve your AI models as web endpoints.

## How to get started

Head over to [vessl.ai](https://vessl.ai) and sign up for a free account. No `docker build` or `kubectl get`.
1. Create your account at [vessl.ai](https://vessl.ai) and get $30 in free GPU credits.
2. Install our Python package — `pip install vessl`.
3. Follow our [Quickstart](/get-started/quickstart) guide or try out our example models at [VESSL Hub](https://vessl.ai/hub). 

## How does it work?

VESSL AI abstracts the obscure infrastructure and complex backends inherent to launching AI workloads into a simple YAML file, so you don't have to mess with AWS, Kubernetes, Docker, or more. Here's an example that launches a web app for Stable Diffusion.

```yaml
resources:
  cluster: vessl-gcp-oregon
  preset: gpu-l4-small
image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
import:
  /code/:
    git:
      url: https://github.com/vessl-ai/hub-model
      ref: main
  /model/: hf://huggingface.co/VESSL/SSD-1B
run:
  - command: |-
      pip install -r requirements.txt
      streamlit run ssd_1b_streamlit.py --server.port=80
    workdir: /code/SSD-1B
interactive:
  max_runtime: 24h
  jupyter:
    idle_timeout: 120m
ports:
  - name: streamlit
    type: http
    port: 80

```

With every YAML file, you are creating a VESSL Run. VESSL Run is an atomic unit of VESSL AI, a single unit of Kubernetes-backed AI workload. You can use our YAML definition as you progress throughout the AI lifecycle from checkpointing models for fine-tuning to exposing ports for inference.

## What's next?

See VESSL AI in action with our examples Runs and pre-configured open-source models.

<CardGroup cols={2}>
  <Card title="Quickstart – Hello, world!"  href="get-started/quickstart">
    Fine-tune Llama2-7B with a code instructions dataset. 
  </Card>

  <Card title="GPU-accelerated notebook" href="get-started/gpu-notebook">
    Launch a GPU-accelerated Streamlit app of Mistral 7B.
  </Card>
  
  <Card title="SSD-1B Playground" href="get-started/stable-diffusion">
    Interactive playground of a lighter and faster version for Stable Diffusion XL.
  </Card>

  <Card title="Llama2-7B Fine-tuning" href="get-started/llama2">
    Translate audio snippets into text on a Streamlit playground.
  </Card>
</CardGroup>