> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lazycloud.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Examples

> Complete applications built on the LazyCloud SDK: GPU inference, model training, an ASGI service, a coding agent, and parallel data processing.

Each workflow is a standalone uv project to download. The source and assets
ship with the SDK, so you do not need repository access.

Each app defines its images, compute, and storage mounts in Python. Running or
deploying the app applies those definitions, so mounted volumes need no
separate CLI step to create them. Examples that need credentials include a
Python setup step for them.

<CardGroup cols={2}>
  <Card title="OpenAI-compatible LLM service" icon="messages" href="/examples/openai-compatible-llm">
    Serve a language model behind an authenticated OpenAI-compatible API on a GPU.
  </Card>

  <Card title="Train a YOLO object detector" icon="camera" href="/examples/train-yolo-object-detector">
    Train on a GPU, keep the checkpoint in a volume, and predict from a second function.
  </Card>

  <Card title="Document processing with FastAPI" icon="file-magnifying-glass" href="/examples/document-processing-asgi">
    Upload a PDF or image in your browser and download the extracted text.
  </Card>

  <Card title="Sandboxed coding agent" icon="terminal" href="/examples/sandboxed-coding-agent">
    Keep model credentials in a function while generated code runs in a network-blocked sandbox.
  </Card>

  <Card title="Parallel Parquet processing on S3" icon="table" href="/examples/parallel-parquet-s3">
    Fan partition work out through a function and write the summary back to your bucket.
  </Card>
</CardGroup>

## Set up the examples

Install the CLI with uv, then choose a project:

```bash theme={null}
uv tool install lazycloud-client
lazycloud example list
lazycloud example download yolo-training
cd yolo-training
uv sync
```

Each download includes a README and dependencies pinned to the installed SDK.
Run the guide's commands from the downloaded directory, and use `uv run` for
Python scripts and CLI workflows. Commit the generated `uv.lock` and use
`uv sync --locked` in CI. GPU libraries install in remote images, so you do not
need a local GPU.

```bash theme={null}
uv run lazycloud login
```

If you use several workspaces, select one with `uv run lazycloud workspace use <name>`.
Before running a GPU example, check your credit balance and GPU access in
Settings, then Billing. The storage examples keep their files after a run so
you can reuse the outputs. Follow each guide's cleanup steps when you finish.

The catalog also includes `quickstart`, `artifacts`, and `all-workloads`.
Their READMEs cover run commands and cleanup. Use
`lazycloud example download all` to write every project into its own directory.