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Deploying to Azure Container Apps

note

This guide has been contributed by the community and has not yet been tested by the Remotion team.

This guide provides a walkthrough for deploying Remotion rendering service with basic render queuing on Azure Container Apps.

Prerequisites​

  • A working Remotion project (e.g., from npm create video) with provision to use Docker.
  • An Azure account.
  • A basic understanding of Docker and having it installed on your system is required.
  • The Azure CLI installed, authenticated (azure login) and a subscription is selected after authentication.
  • The Azure account is provisioned to use GPU on Azure Container Apps, if not, request here.
  • A docker hub account to host the docker image and authenticate (docker login).

Step 1: Pick a project​

For simplicity, we'll use remotion-gpu which is configured to leverage GPU acceleration. This project is based with Remotion render server template and uses the scene composition from gpu-scene project.

Step 2: Build and tag the docker image​

From the project directory, build the Docker image and push it to Docker Hub with your account, specifying an image name and version tag. The image-name is remotion-docker-gpu and version is 1.0.0.

bash
docker build -t your-dockerhub-username/remotion-docker-gpu:1.0.0 .

Step 3: Push the image to docker hub​

bash
docker push your-dockerhub-username/remotion-docker-gpu:1.0.0

Step 4: Set environment variables​

Set the environment variables for the azure cli

Set environment variables
bash
CONTAINER_IMAGE="docker.io/your-dockerhub-username/remotion-docker-gpu:1.0.0"
RESOURCE_GROUP="<RESOURCE_GROUP>"
ENVIRONMENT_NAME="<ENVIRONMENT_NAME>"
LOCATION="swedencentral"
CONTAINER_APP_NAME="<CONTAINER_APP_NAME>"
WORKLOAD_PROFILE_NAME="NC8as-T4"
WORKLOAD_PROFILE_TYPE="Consumption-GPU-NC8as-T4"

Variable representation​

  • CONTAINER_IMAGE represents the docker.io domain and docker image.
  • LOCATION represents the region where the GPU is allocated.
  • WORKLOAD_PROFILE_NAME and WORKLOAD_PROFILE_TYPE are constant variables from Azure documentation representing GPU family to use.

Step 5: Create the resource group​

bash
az group create \
  --name $RESOURCE_GROUP \
  --location $LOCATION \
  --query "properties.provisioningState"

Step 6: Register insights for monitoring​

bash
az provider register -n Microsoft.OperationalInsights --wait

Step 7: Create a Container Apps environment​

bash
az containerapp env create \
  --name $ENVIRONMENT_NAME \
  --resource-group $RESOURCE_GROUP \
  --location "$LOCATION" \
  --query "properties.provisioningState"

Step 8: Add a workload profile to your environment​

bash
az containerapp env workload-profile add \
  --name $ENVIRONMENT_NAME \
  --resource-group $RESOURCE_GROUP \
  --workload-profile-name $WORKLOAD_PROFILE_NAME \
  --workload-profile-type $WORKLOAD_PROFILE_TYPE

Step 9: Deploy container app​

bash
az containerapp create \
  --name $CONTAINER_APP_NAME \
  --resource-group $RESOURCE_GROUP \
  --environment $ENVIRONMENT_NAME \
  --image $CONTAINER_IMAGE \
  --target-port 3000 \
  --ingress external \
  --cpu 8.0 \
  --memory 56.0Gi \
  --workload-profile-name $WORKLOAD_PROFILE_NAME \
  --query properties.configuration.ingress.fqdn

Azure CLI will deploy the image to Azure Container Apps and when completed will provide a URL which accepts HTTP requests.

Deployment Output (example)
bash
Container app created. Access your app at https://remotion-gpu.grayocean-24741fc9.australiaeast.azurecontainerapps.io/
"remotion-gpu.grayocean-24741fc9.australiaeast.azurecontainerapps.io"
note

The target-port is the port that the Node.js server listens on for incoming requests. Steps 4 to 9 are based on the Azure Container App documentation.

Step 10: Trigger a Render​

Send a POST request to the service's /renders endpoint. Replace YOUR_SERVICE_URL with the URL from the deployment output, https://remotion-gpu.grayocean-24741fc9.australiaeast.azurecontainerapps.io/.

bash
curl -X POST {YOUR_SERVICE_URL}/renders \
-H "Content-Type: application/json" \
-d '{}'

Response​

If successful, the API will respond with JSON containing the jobId which can be used to get the status of render.

Example Response
json
{
  "jobId": "955338f6-2607-48bd-bedb-6d4c98f7b4dc"
}

Step 11: Get render status​

Send a GET request to get the render status, replace YOUR_SERVICE_URL with the URL from the deployment output and the jobId from the render request output.

bash
curl  {YOUR_SERVICE_URL}/renders/{jobId}

Response​

Example Response
json
{
  "status": "completed",
  "videoUrl": "/renders/955338f6-2607-48bd-bedb-6d4c98f7b4dc.mp4",
  "data": {"titleText": "Hello, world!"}
}

If status is completed, combine YOUR_SERVICE_URL and the videoUrl response to generate the download link (https://remotion-gpu.grayocean-24741fc9.australiaeast.azurecontainerapps.io/renders/955338f6-2607-48bd-bedb-6d4c98f7b4dc.mp4).

Step 12: (Optional) Delete the resource group​

bash
az group delete --name $RESOURCE_GROUP

This will delete all the resources inside the resource group including the application container.

warning

Cost: This implementation does not include cost management. Refer to Azure pricing documentation for usage estimates. Use at your own risk. Performance: Generated videos may appear blurry. GPU acceleration is configured in the project config, but actual GPU utilization has not been confirmed.

See also​