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Cloud Run + Workflows adapter for HyperFrames distributed rendering — request handler, client-side SDK, and Terraform module.","maintainers":[{"name":"vancejs","email":"vance@heygen.com"},{"name":"miguel.sierra","email":"miguel.sierra@heygen.com"},{"name":"jrusso1020","email":"jdrusso1020@gmail.com"}],"readme":"# @hyperframes/gcp-cloud-run\n\nGoogle Cloud Run + Cloud Workflows adapter for HyperFrames distributed\nrendering. The OSS render primitives (`plan` → `renderChunk` × N →\n`assemble`) are pure functions over local file paths; this package is the\ndeployment, orchestration, and storage glue that runs them on Google Cloud —\nthe GCP counterpart to [`@hyperframes/aws-lambda`](../aws-lambda).\n\nTwo surfaces, one package:\n\n- **Server-side handler** (`./server`) — a Cloud Run HTTP service that\n  dispatches `plan` / `renderChunk` / `assemble` on the request body's\n  `Action` field, bridging GCS ↔ the container's filesystem around each OSS\n  primitive. This is what the bundled `Dockerfile` runs.\n- **Client-side SDK** (`./sdk`) — `renderToCloudRun`, `getRenderProgress`,\n  `deploySite`, `validateDistributedRenderConfig`, and `computeRenderCost`.\n  Call these from a Node process (CI, CLI, app backend) to drive a deployed\n  stack without writing GCS / Workflows boilerplate.\n\nThe package is **not** a dependency of `@hyperframes/producer`; install it\nseparately.\n\n## Architecture\n\n```\nGCS bucket  ←→  Cloud Run service (plan / renderChunk / assemble)\n                     ▲\n                     │ OIDC-authenticated http.post, one per step\n                     │\n                Cloud Workflows  (Plan → parallel RenderChunk → Assemble)\n```\n\n- **Plan** downloads the project tarball and publishes either a legacy v1\n  planDir tarball or a v2 manifest plus content-addressed artifacts.\n- **RenderChunk** runs in a parallel `for` loop in the workflow, fanned out\n  up to the plan's chunk count. Each invocation renders one chunk and uploads\n  it.\n- **Assemble** downloads every chunk + audio, stitches the final\n  deliverable, and uploads it.\n\nEvery step is a `POST` to the same Cloud Run URL with a different `Action`.\nThe workflow accumulates each step's small result body and returns\n`{ Plan, Chunks, Assemble }` so `getRenderProgress` can read frame totals and\nper-step durations on success.\n\n### Plan transport selection\n\nPlan v2 is the default for new renders. When `planProtocol` is omitted,\n`renderToCloudRun` sends an explicit `PlanProtocol: \"v2\"` so the SDK and\nthe deployed workflow agree:\n\n```ts\nawait renderToCloudRun({\n  // ...project, bucket, workflow, service, and config...\n});\n```\n\nV2 uses separate manifest and content-addressed artifact locators throughout\nthe workflow. Unknown protocols and integrity failures fail closed; a render\nnever mixes v1 and v2 artifacts.\n\nThe monolithic v1 transport remains available as deprecated compatibility by\npassing `planProtocol: \"v1\"` explicitly.\n\n#### Upgrade order\n\nRedeploy the Cloud Run image and Cloud Workflows definition from the same new\npackage version before upgrading an application that calls\n`renderToCloudRun`. Pause new renders and drain active workflow executions\nduring the infrastructure update. Older workflows can default omission to v1\nor lack the v2 branch, while the new SDK sends explicit v2. If infrastructure\ncannot be redeployed first, keep the previous SDK version or pass\n`planProtocol: \"v1\"` explicitly until the Terraform/workflow redeploy is\ncomplete.\n\n## Chrome runtime\n\nUnlike the Lambda adapter — which fights a 250 MB ZIP ceiling and\ndecompresses `@sparticuz/chromium` into `/tmp` at runtime — Cloud Run runs a\ncontainer image. The `Dockerfile` installs the same pinned\n`chrome-headless-shell` build and font set the production renderer uses, at a\nfixed path, and exports `HYPERFRAMES_CHROME_PATH`. CDP-level `BeginFrame`\nsupport is a binary/runtime capability, so the image build launches that\nexact executable and requires an enable + warm-up + PNG-returning\n`HeadlessExperimental.beginFrame` probe to pass. The end-to-end smoke also\nrequires every chunk to report effective `CaptureMode: \"beginframe\"`, which\ncatches runtime fallback separately from build-time packaging. There is no\nruntime decompression step and no packaging ceiling.\n\n## Deploying\n\nThe `terraform/` module provisions everything: the GCS render bucket, the\nCloud Run service, the Cloud Workflows definition, two least-privilege\nservice accounts (the service reads/writes the bucket; the workflow invokes\nthe service), and a runaway-request alert.\n\n```bash\n# 1. Build + push the image (Cloud Build or local docker).\ngcloud builds submit . \\\n  --tag REGION-docker.pkg.dev/PROJECT/REPO/hyperframes-render:TAG\n\n# 2. Apply the module.\nterraform -chdir=node_modules/@hyperframes/gcp-cloud-run/terraform init\nterraform -chdir=node_modules/@hyperframes/gcp-cloud-run/terraform apply \\\n  -var project_id=PROJECT \\\n  -var region=us-central1 \\\n  -var image=REGION-docker.pkg.dev/PROJECT/REPO/hyperframes-render:TAG\n```\n\nTerraform outputs `render_bucket_name`, `service_url`, `workflow_name`, and\n`region` — pass them straight into the SDK.\n\n## Using the SDK\n\n```ts\nimport { renderToCloudRun, getRenderProgress } from \"@hyperframes/gcp-cloud-run/sdk\";\n\nconst handle = await renderToCloudRun({\n  projectDir: \"./my-composition\",\n  config: { fps: 30, width: 1920, height: 1080, format: \"mp4\" },\n  bucketName: \"hyperframes-render-my-project\", // from terraform output\n  projectId: \"my-project\",\n  location: \"us-central1\",\n  workflowId: \"hyperframes-render\",\n  serviceUrl: \"https://hyperframes-render-abc.us-central1.run.app\",\n});\n\n// Poll until done.\nlet progress = await getRenderProgress({ executionName: handle.executionName });\nwhile (progress.status === \"running\") {\n  await new Promise((r) => setTimeout(r, 5000));\n  progress = await getRenderProgress({ executionName: handle.executionName });\n}\nconsole.log(progress.status, progress.outputFile, progress.costs.displayCost);\n```\n\n`deploySite` is called implicitly when you pass `projectDir`; call it\nyourself to pre-upload once and reuse the `siteHandle` across many renders\n(e.g. personalised template batches).\n\n## Running tests\n\n```bash\nbun test          # unit tests over an in-memory GCS double — no network\nbun run typecheck\n```\n\nThe live end-to-end smoke (build image → terraform apply → render a fixture\nthrough the workflow → PSNR-compare → destroy) lives at\n`examples/gcp-cloud-run/scripts/smoke.sh` and needs a GCP project with\nbilling enabled.\n\n## What's still ahead\n\n- **Mid-flight per-chunk progress.** `getRenderProgress` reports coarse\n  `running` progress and exact numbers on success. Reading the Cloud\n  Workflows step-entries API would give per-chunk progress while the render\n  is in flight; tracked as a follow-up.\n- **Cloud Run Jobs / Firebase Functions variants.** This first version\n  targets Cloud Run services + Workflows (the closest analog to Lambda +\n  Step Functions). The same handler runs unchanged under Cloud Run Jobs;\n  only the orchestration trigger differs.\n","readmeFilename":"README.md"}