> ## Documentation Index
> Fetch the complete documentation index at: https://hyperframes-codex-docs-human-first-refactor.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Google Cloud Run

> Deploy distributed HyperFrames rendering to Cloud Run, Cloud Workflows, and Google Cloud Storage.

Use this path when renders must run in your Google Cloud account. A Cloud
Workflow plans the render, sends chunks to Cloud Run in parallel, and assembles
the result in Google Cloud Storage.

```text theme={null}
Cloud Workflow: Plan → Render chunks in parallel → Assemble
                         ↓
                  Cloud Run service
                         ↓
                         GCS
```

For a hosted render without infrastructure ownership, use
[HyperFrames Cloud](/deploy/cloud). For a small preview application and render
endpoint, use a [hosted template](/guides/deploy).

## Prerequisites

* A Google Cloud project with billing enabled
* `gcloud` authenticated for that project
* Terraform 1.5 or newer
* Cloud Build access, or an existing compatible container image
* `@hyperframes/gcp-cloud-run` installed alongside the CLI

The CLI can build the renderer automatically when it runs from a HyperFrames
repository checkout. Outside the repository, pass an image built from
`packages/gcp-cloud-run/Dockerfile` with `--image`.

## Deploy

```bash theme={null}
hyperframes cloudrun deploy --project my-gcp-project
```

The command enables the required Google Cloud APIs, builds and pushes the
renderer when needed, applies the bundled Terraform module, and stores the
resulting bucket, service URL, and workflow name in `~/.hyperframes/`.

The default region is `us-central1`. Machine and scaling controls include
`--region`, `--cpu`, `--memory`, `--max-instances`, and `--timeout`.

## Render

Width and height describe the authored canvas. Use `--output-resolution` when
the encoded result should be supersampled without changing the layout.

```bash theme={null}
hyperframes cloudrun render ./my-project \
  --width 1920 \
  --height 1080 \
  --wait
```

The command accepts the same template variables used by local and AWS renders:

```bash theme={null}
hyperframes cloudrun render ./card-template \
  --width 1920 \
  --height 1080 \
  --variables '{"name":"Ada"}' \
  --wait
```

Supported distributed outputs are MP4, MOV, WebM, and PNG sequences. MP4 can
use H.264 or H.265. Distributed rendering is currently SDR-only.

## Reuse an upload

Projects are content-addressed. Upload an unchanged project once, then reuse
its site ID for later renders:

```bash theme={null}
hyperframes cloudrun sites create ./my-project
```

Use `render-batch` with a JSONL file when one template needs many sets of
variables:

```bash theme={null}
hyperframes cloudrun render-batch ./card-template \
  --batch ./recipients.jsonl \
  --width 1920 \
  --height 1080 \
  --max-concurrent 10
```

Each JSONL row contains an output key and optional variables:

```json theme={null}
{"outputKey":"renders/ada.mp4","variables":{"name":"Ada"}}
```

## Progress and teardown

Without `--wait`, a render returns its workflow execution name immediately.

```bash theme={null}
hyperframes cloudrun progress <execution-name>
hyperframes cloudrun destroy --project my-gcp-project
```

`destroy` removes the Terraform-managed stack and its render bucket. Download
anything that must be retained before running it.

## Programmatic use

`@hyperframes/gcp-cloud-run/sdk` exposes `deploySite`, `renderToCloudRun`, and
`getRenderProgress` for Node backends. The package also exports the Terraform
module and HTTP handler used by the deployed service.

See the [GCP package reference](/packages/gcp-cloud-run) for the SDK contract
and the complete infrastructure shape.