Reference SAM template for deploying HyperFrames distributed rendering on AWS. One Lambda function, three roles (Plan / RenderChunk / Assemble), choreographed by a Step Functions standard workflow with a Map state for parallel chunk rendering.

See packages/aws-lambda/README.md for the Lambda handler architecture.

• AWS account with IAM permissions to deploy CloudFormation stacks containing Lambda, Step Functions, S3, IAM, and CloudWatch resources.
• sam CLI installed (≥ 1.100).
• bun installed (≥ 1.3) to build the handler ZIP.

# 1. Build the handler ZIP that `template.yaml`'s CodeUri points at.
bun install                                       # at repo root
bun run --cwd packages/aws-lambda build:zip

# 2. Deploy. First time: `--guided` to set stack name + region.
cd examples/aws-lambda
sam deploy --guided --resolve-s3

--resolve-s3 lets SAM pick (or create) a per-account bucket to host the uploaded ZIP. After the first deploy, subsequent updates can omit --guided and --resolve-s3 — SAM remembers your choices in samconfig.toml.

Upload your project as a zip to the render bucket, then start a Step Functions execution:

STACK_NAME=hyperframes-render          # whatever you picked at deploy
RENDER_BUCKET=$(aws cloudformation describe-stacks \
  --stack-name "$STACK_NAME" \
  --query 'Stacks[0].Outputs[?OutputKey==`RenderBucketName`].OutputValue' \
  --output text)
STATE_MACHINE_ARN=$(aws cloudformation describe-stacks \
  --stack-name "$STACK_NAME" \
  --query 'Stacks[0].Outputs[?OutputKey==`RenderStateMachineArn`].OutputValue' \
  --output text)

# Tar + upload the project directory. The handler uses `tar` (not
# `unzip`, which Lambda's base image doesn't ship), so the on-the-wire
# archive format is `.tar.gz`.
tar -czf my-project.tar.gz -C ./my-project .
aws s3 cp my-project.tar.gz "s3://${RENDER_BUCKET}/projects/my-project.tar.gz"

# Start the execution. The input JSON tells the state machine where to
# read inputs and write outputs.
aws stepfunctions start-execution \
  --state-machine-arn "$STATE_MACHINE_ARN" \
  --input "$(cat <<EOF
{
  "ProjectS3Uri": "s3://${RENDER_BUCKET}/projects/my-project.tar.gz",
  "PlanOutputS3Prefix": "s3://${RENDER_BUCKET}/renders/$(date +%s)/",
  "OutputS3Uri": "s3://${RENDER_BUCKET}/output.mp4",
  "PlanProtocol": "v2",
  "Config": {
    "fps": 30,
    "width": 1920,
    "height": 1080,
    "format": "mp4",
    "chunkSize": 240,
    "maxParallelChunks": 8,
    "runtimeCap": "lambda"
  }
}
EOF
)"

The Step Functions execution kicks off Plan, fans out RenderChunk via the Map state, and finally Assemble. Final mp4 lands at OutputS3Uri. Plan v2 is recommended for new integrations. PlanProtocol may be "v1" or "v2"; absent still defaults to v1 for backwards compatibility. V2 uses separate manifest and content-addressed artifact locators throughout the workflow and never places a v2 object in PlanS3Uri.

You can test the Lambda handler without deploying anything via SAM local:

# Build the ZIP first.
bun run --cwd packages/aws-lambda build:zip

# Launch a local Lambda runtime emulator and run a sample plan event.
cd examples/aws-lambda
sam validate
sam local invoke RenderFunction --event sample-events/plan.json

The sample-events/ directory ships small JSON payloads for each of the three actions. They reference fake S3 URIs — useful for sanity-checking the handler's dispatch logic; not for full end-to-end testing (real S3 calls require credentials and a project zip to actually exist).

For full end-to-end validation against real AWS — the gate that proves the architecture works on a deployed Lambda — use the local smoke script:

# Defaults use the fixture's meta.json minPsnr (30 dB for mp4-h264-sdr).
./scripts/smoke.sh

# Customised:
./scripts/smoke.sh \
  --fixture mp4-h264-sdr \
  --chunk-counts 2,4,8,16 \
  --plan-protocol both \
  --psnr-threshold 40 \
  --reserved-concurrency 8

# Keep the stack alive for inspection afterward:
./scripts/smoke.sh --keep-stack

# Show all flags including cost notes:
./scripts/smoke.sh --help

The script builds the handler ZIP, deploys this template under a per-run stack name, renders the fixture at each chunk count via the Step Functions state machine, PSNR-compares against the in-process baseline (which is git-LFS tracked under packages/producer/tests/distributed/<fixture>/output/), captures per-execution Step Functions history, and tears the stack down. Use --plan-protocol both to run v1 and v2 through the same deployed Lambda package and baseline. Each v1/v2 pair is also gated directly on per-chunk hashes from Step Functions history, normalized decoded RGBA frame hashes, decoded 48 kHz stereo s16le PCM hashes and byte counts, normalized stream metadata, and duration. Encoded MP4 SHA equality is reported but is informational unless --require-encoded-sha-equal is set. The script assigns unique function/state-machine names, uses a dedicated temporary SAM artifact bucket, and removes render objects, retained buckets, the implicit Lambda log group, and deployment artifacts on teardown. Suspended-version buckets are purged in 1,000-entry batches, including concrete versions, null versions, and delete markers. It then verifies that the stack, both buckets, Lambda, state-machine, and both log groups are absent; an otherwise-successful run fails if cleanup cannot be proven.

Wall-clock methodology caveat (eval.sh only). eval.sh reports a local-vs-Lambda "speedup" column. The local timing includes bun + tsx + harness scaffolding (not just renderer-internal time); the Lambda timing measures Step Functions execution only. This biases the speedup against Lambda on tiny fixtures and in favour of Lambda on larger ones. Treat the number as "end-to-end CLI experience," not as a renderer-vs-renderer benchmark. Cold-start variance is ±5-10s per chunk; run with --iterations 3+ to report medians.

Cost per pass. Each eval.sh invocation runs SAM deploy (~$0.01 in CFN operations) plus N fixtures × ITERATIONS × CHUNK_COUNT Lambda invocations at MemorySize (default 10 GiB) × per-chunk wall clock. With defaults (4 fixtures, 1 iteration, chunk-count 4) the Lambda spend is roughly $0.10-$0.20 per pass before S3 transfer. Lower --reserved-concurrency for cost-conscious accounts; higher --iterations improves median stability at proportional cost.

Outputs land under <repo-root>/lambda-smoke-artifacts/:

• results.json — planProtocol × chunkCount × wallClockMs × psnrAvgDb
• semantic-comparisons.json — direct v1/v2 semantic gate results
• renders/<protocol>-N<N>-output.mp4 — each rendered variant
• renders/<protocol>-N<N>-history.json — full Step Functions execution history
• renders/v1-v2-N<N>.* — normalized frame hashes, ffprobe metadata, and comparison JSON

Prerequisites: aws (v2), sam (≥ 1.100), bun (≥ 1.3), ffmpeg, jq, zip. AWS credentials come from the standard resolution chain (env vars → ~/.aws/credentials → SSO → IMDS). Pin a specific profile with --profile <name> or AWS_PROFILE=<name>.

sam delete --stack-name hyperframes-render

S3 buckets are Retained on delete to protect rendered artifacts. Empty + delete the bucket manually after sam delete if you want to fully tear down.

A 60-second 1080p30 composition at default chunkSize=240 (8 chunks) typically costs ~$0.04 in Lambda time + ~$0.001 in Step Functions. The eval script under scripts/eval.sh produces real per-fixture cost numbers when you run it against your own AWS account.

• "Chrome failed to launch" — the ZIP was likely built with the wrong --source. Match ChromeSource to the build flag.
• "PLAN_HASH_MISMATCH" — non-retryable. The plan tarball was written by a different version of the producer than the chunk worker is running. Re-plan from scratch.
• "BROWSER_GPU_NOT_SOFTWARE" — Chromium fell back to a hardware GL backend. Should not happen in Lambda (no GPU); file an issue.
• CloudWatch alarm firing on runaway-chunk-invocations — check the state machine execution history for an unintended Map fan-out, or raise the threshold if your workload genuinely exceeds it.

• CDK construct shipping the same topology programmatically — follow-up.
• hyperframes lambda deploy / render / progress / destroy CLI — follow-up.
• Migration guide — follow-up.
• Lambda RIE local smoke harness mode — follow-up.