AWS Lambda

HyperFrames ships a first-class AWS Lambda deployment: one Lambda function fronts a Step Functions standard workflow that fans renders out across many parallel chunk workers, with intermediate artifacts in S3. End-to-end is three commands once your AWS credentials are configured.

hyperframes lambda deploy
hyperframes lambda render ./my-project --width 1920 --height 1080 --wait
hyperframes lambda destroy

Architecture

┌──────────────────────────────────────────────────────────────────┐
│ Step Functions state machine                                     │
│   Plan → Map(N) RenderChunk → Assemble                           │
└──────────────────────────────────────────────────────────────────┘
                              │ dispatches by event.Action
                              ▼
┌──────────────────────────────────────────────────────────────────┐
│ One Lambda function (packages/aws-lambda/dist/handler.zip)       │
│   handler.mjs                                                    │
│     ├─ Action="plan"        → @hyperframes/producer/distributed  │
│     ├─ Action="renderChunk" → @hyperframes/producer/distributed  │
│     └─ Action="assemble"    → @hyperframes/producer/distributed  │
│   bin/ffmpeg                — ffmpeg-static                      │
│   node_modules/@sparticuz/chromium/ — Lambda-optimised Chromium  │
└──────────────────────────────────────────────────────────────────┘
                              │ pure functions over local paths
                              ▼
┌──────────────────────────────────────────────────────────────────┐
│ S3 bucket — plan tarball + per-chunk outputs + final mp4         │
└──────────────────────────────────────────────────────────────────┘

The Lambda handler is a thin dispatch: parse the Step Functions event, download inputs from S3 into /tmp, call the OSS primitive from @hyperframes/producer/distributed, upload outputs back, return a small JSON result. Everything heavy — capture, encode, audio mix — happens inside the OSS primitives.

Prerequisites

Tool	Why	Install
AWS credentials	The CLI and the deploy step both call AWS APIs.	Env vars, `~/.aws/credentials`, SSO, or IMDS — any chain `boto3` would resolve.
AWS SAM CLI	`hyperframes lambda deploy/destroy` shells out to `sam deploy`/`sam delete`.	Install guide
`bun`	Used to build `packages/aws-lambda/dist/handler.zip` at deploy time.	`npm install -g bun` or bun.sh
HyperFrames repo checkout	`lambda deploy` builds the Lambda handler ZIP from source. Adopters who deploy outside a checkout can set `HYPERFRAMES_REPO_ROOT` to point at one.	`git clone https://github.com/heygen-com/hyperframes`

Three deployment paths

Path 1 — `hyperframes lambda` CLI (recommended)

The CLI is a thin wrapper around the SAM template + the @hyperframes/aws-lambda SDK. For most adopters this is the right starting point.

hyperframes lambda deploy \
  --stack-name=hyperframes-prod \
  --region=us-east-1 \
  --concurrency=8 \
  --memory=10240

The default --concurrency=8 is deliberately conservative for first-time users. The Lambda Map state’s default would let an unbounded number of chunks fan out in parallel; 8 caps your worst-case spend on a runaway render at roughly 8 × (15 min × 10 GB × $0.0000167/GB-s) ≈ $1.20. Raise it after you’ve sized your typical render’s chunk count. After deploy, render anything with:

hyperframes lambda render ./my-project --width 1920 --height 1080 --wait

The --wait flag blocks and streams per-chunk progress + accrued cost; drop it to fire-and-forget, then poll with hyperframes lambda progress <renderId> on your own cadence. See the CLI reference for full flag documentation.

Pre-staging a project with `sites create`

Re-rendering the same project tree on every lambda render call re-tars and re-uploads it each time. For tight inner loops (CI smoke jobs, prompt iteration in a demo flow), pre-stage the project once and reuse the upload:

hyperframes lambda sites create ./my-project
# → Site ID: a1b2c3d4e5f6g7h8 (content-addressed)

hyperframes lambda render ./my-project --site-id=a1b2c3d4e5f6g7h8 \
  --width 1920 --height 1080 --wait

The siteId is content-addressed via a SHA-256 of the project tree; re-running sites create on an unchanged tree skips the upload via a HeadObject short-circuit. Pass the same --site-id to as many lambda render calls as you like — they all reuse the one S3 PUT.

Path 2 — Direct SAM deploy

If you want to read the CloudFormation before you deploy, or you need to customise the topology (extra alarms, SNS subscribers, KMS keys, …), invoke SAM directly against the template at examples/aws-lambda/template.yaml:

cd packages/aws-lambda
bun run build:zip                     # produces dist/handler.zip
cd ../../examples/aws-lambda
sam deploy \
  --stack-name=hyperframes-prod \
  --region=us-east-1 \
  --resolve-s3 \
  --capabilities CAPABILITY_IAM \
  --no-confirm-changeset \
  --parameter-overrides ChromeSource=sparticuz ReservedConcurrency=8

The template emits three CloudFormation outputs you’ll need to invoke renders:

RenderBucketName — S3 bucket for plan tarballs + per-chunk outputs + final renders.
RenderStateMachineArn — the Step Functions standard workflow that orchestrates Plan → Map → Assemble.
RenderFunctionArn — the single Lambda function the state machine dispatches against.

The SAM template’s own default for ReservedConcurrency is -1 (unreserved, account-default). The Path 1 CLI overrides it to 8 to keep first-time spend bounded; if you drop ReservedConcurrency from --parameter-overrides here, you get the unreserved default. Set it explicitly unless you’ve already sized your typical render’s fan-out.

Path 3 — CDK construct

For users already running CDK, the @hyperframes/aws-lambda package exports a HyperframesRenderStack L2 construct that emits the same topology as the SAM template:

import { App, CfnOutput, Stack } from "aws-cdk-lib";
import { HyperframesRenderStack } from "@hyperframes/aws-lambda/cdk";

const app = new App();
const stack = new Stack(app, "MyApp");
const render = new HyperframesRenderStack(stack, "Render", {
  projectName: "hyperframes",
  lambdaMemoryMb: 10240,
  reservedConcurrency: 8,
  chromeSource: "sparticuz",
});

new CfnOutput(stack, "RenderBucketName", { value: render.bucket.bucketName });
new CfnOutput(stack, "StateMachineArn", { value: render.stateMachine.stateMachineArn });

aws-cdk-lib and constructs are declared as optional peer dependencies of @hyperframes/aws-lambda, so consumers who only need the SDK don’t pay the CDK import cost. The construct exposes .bucket, .renderFunction, and .stateMachine so you can wire dashboards, SNS topics, or other AWS resources alongside it without re-deriving ARNs.

IAM permissions

The CLI ships a built-in IAM bootstrap to avoid the “User is not authorized to perform iam:CreateRole” first-deploy trap:

# Print an inline policy doc to attach to the IAM user that runs the CLI.
hyperframes lambda policies user

# Print { TrustRelationship, InlinePolicy } for a CloudFormation service role.
hyperframes lambda policies role --principal=cloudformation

# Validate a checked-in policy still covers the CLI's needs (exit non-zero on missing).
hyperframes lambda policies validate ./infra/iam/hyperframes-deploy.json

The generated documents grant Resource: "*" for the CLI’s required action set. After your first successful deploy you can narrow Resource to the deployed ARNs — predictable per the CloudFormation outputs above. Adopters running the CLI in CI typically check the policy doc into source control and run policies validate as a pre-deploy step to catch drift.

Cost shape

Lambda renders are billed by GB-seconds (Lambda billed duration × configured memory) plus a tiny per-state-transition fee for Step Functions standard workflows. hyperframes lambda progress exposes the running tally:

hyperframes lambda progress my-render-id
# Status:    SUCCEEDED
# Progress:  100%
# Frames:    480 / 480
# Lambdas:   5
# Cost:      $0.0214 (Lambda $0.0210 + SFN $0.0004)
# Output:    s3://hyperframes-renders/.../output.mp4

The cost number is best-effort: Lambda billed duration comes from the handler’s own DurationMs return value (which SFN history surfaces in the success payload) and S3 transfer is not included. The math is in packages/aws-lambda/src/sdk/costAccounting.ts if you want to verify; CLI-shown values match what AWS Billing reports within rounding noise.

Troubleshooting

`sam deploy` fails with “Stack already exists”

Pass the same --stack-name you used the first time. SAM is idempotent — re-running on an existing stack resolves to a no-op or an in-place update.

`User is not authorized to perform iam:CreateRole`

The IAM credential running lambda deploy doesn’t have permission to create the service role CloudFormation needs. Run hyperframes lambda policies user and attach the printed policy to your IAM user (or take the policies role output and have your admin create a deploy role).

`Lambda function failed: PLAN_HASH_MISMATCH`

Step Functions invoked a renderChunk with a plan hash that didn’t match the planDir on S3. Almost always means the producer version differs between the local plan() build and the deployed Lambda ZIP. Re-run hyperframes lambda deploy (which rebuilds the ZIP) and re-render.

`Lambda function failed: BROWSER_GPU_NOT_SOFTWARE`

The handler launched Chromium but the runtime probe found a non-SwiftShader GL backend. Hardware GL is non-deterministic across chunk boundaries, so distributed renders refuse it at the runtime-image / launch-flags layer (not at the composition layer). Rebuild the handler ZIP and redeploy:

bun run --cwd packages/aws-lambda build:zip
hyperframes lambda deploy --stack-name=<your-stack>

The build pipeline pins @sparticuz/chromium + the Chrome flags (--use-gl=swiftshader --use-angle=swiftshader) so a fresh deploy almost always resolves this. If it persists, your stack’s Lambda function is pointing at a stale handler ZIP from a previous deploy — lambda deploy always rebuilds, so re-running unsticks it.

Render seems stuck at `RUNNING`

Most often a Lambda cold-start chain on a many-chunk render. The Map state’s reserved concurrency caps how many chunks can run in parallel — if you set --concurrency=4 and your render has 16 chunks, the state machine processes them in batches of 4. hyperframes lambda progress <id> shows how many invocations are in flight. If progress doesn’t advance for >10 minutes, check the Step Functions execution in the AWS console — failed Lambda invocations include the typed error name (FONT_FETCH_FAILED, FFMPEG_VERSION_MISMATCH, etc.) which short-circuits the state machine.

Tearing down doesn’t reclaim S3 storage

The render bucket is created with CloudFormation Retain on delete — hyperframes lambda destroy (or sam delete) tears the function + state machine down but the bucket survives. This is intentional: it protects final-rendered MP4s from being lost when you re-deploy. To fully reclaim storage, empty + delete the bucket via the AWS console / aws s3 rb.

What’s NOT in the v1 surface

Webhooks on completion. Not in v1 — poll with hyperframes lambda progress or watch the Step Functions execution. A --webhook flag with an SNS topic is on the Phase 6c backlog.
compositions discovery verb. Coming separately (PR 6.10 on the plan); for now, point lambda render at the project directory containing your index.html.
Multi-region. Each --region is an independent stack. There is no built-in cross-region failover.
HDR. Distributed mode is SDR-only. HDR mp4 with bsf signaling is on the v1.5 backlog.

Packages

Deploy

Architecture

Prerequisites

Three deployment paths

Path 1 — `hyperframes lambda` CLI (recommended)

Pre-staging a project with `sites create`

Path 2 — Direct SAM deploy

Path 3 — CDK construct

IAM permissions

Cost shape

Troubleshooting

`sam deploy` fails with “Stack already exists”

`User is not authorized to perform iam:CreateRole`

`Lambda function failed: PLAN_HASH_MISMATCH`

`Lambda function failed: BROWSER_GPU_NOT_SOFTWARE`

Render seems stuck at `RUNNING`

Tearing down doesn’t reclaim S3 storage

What’s NOT in the v1 surface

Packages

Deploy

Documentation Index

​Architecture

​Prerequisites

​Three deployment paths

​Path 1 — hyperframes lambda CLI (recommended)

​Pre-staging a project with sites create

​Path 2 — Direct SAM deploy

​Path 3 — CDK construct

​IAM permissions

​Cost shape

​Troubleshooting

​sam deploy fails with “Stack already exists”

​User is not authorized to perform iam:CreateRole

​Lambda function failed: PLAN_HASH_MISMATCH

​Lambda function failed: BROWSER_GPU_NOT_SOFTWARE

​Render seems stuck at RUNNING

​Tearing down doesn’t reclaim S3 storage

​What’s NOT in the v1 surface

Architecture

Prerequisites

Three deployment paths

Path 1 — `hyperframes lambda` CLI (recommended)

Pre-staging a project with `sites create`

Path 2 — Direct SAM deploy

Path 3 — CDK construct

IAM permissions

Cost shape

Troubleshooting

`sam deploy` fails with “Stack already exists”

`User is not authorized to perform iam:CreateRole`

`Lambda function failed: PLAN_HASH_MISMATCH`

`Lambda function failed: BROWSER_GPU_NOT_SOFTWARE`

Render seems stuck at `RUNNING`

Tearing down doesn’t reclaim S3 storage

What’s NOT in the v1 surface