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How to verify that VPC traffic to S3 is going through your S3 gateway?

Gateway endpoints for Amazon S3 are a must-have whenever your EC2 instances send and receive traffic from S3, because they allow the traffic to stay within the AWS network, hence better security, bandwidth, throughput, and costs. They can easily be created, and added to your VPC route tables.

But how do you verify that traffic is indeed going through the S3 gateway, and not crossing the outer internet?

Using traceroute, you can probe the routes and see whether you are directly hitting the S3 servers (i.e. no intermediate gateway). In this example, the instance is running from a VPC located in us-east-1:

Terminal window
$ traceroute -n -T -p 443 s3.us-east-1.amazonaws.com
traceroute to s3.us-east-1.amazonaws.com (52.216.215.72), 30 hops max, 60 byte packets
 1  * * *
 2  * * *
 3  * * *
 4  * * *
 5  * * *
 6  52.216.215.72  0.890 ms  0.916 ms  0.892 ms
Terminal window
$ traceroute -n -T -p 443 s3.amazonaws.com
traceroute to s3.amazonaws.com (52.217.139.232), 30 hops max, 60 byte packets
 1  * * *
 2  * * *
 3  * * *
 4  * * *
 5  * * *
 6  52.217.139.232  0.268 ms  0.275 ms  0.252 ms

Both outputs produce the expected result, i.e. no intermediary gateway. This is what would happen if you were accessing a bucket located in the us-east-1 region.

Let’s see what happens if we try to access an S3 endpoint located in another zone:

Terminal window
$ traceroute -n -T -p 443 s3.eu-west-1.amazonaws.com
traceroute to s3.eu-west-1.amazonaws.com (52.218.25.211), 30 hops max, 60 byte packets
 1  * * *
 2  240.4.88.37  0.275 ms 240.0.52.64  0.265 ms 240.4.88.39  0.215 ms
 3  240.4.88.49  0.205 ms 240.4.88.53  0.231 ms 240.4.88.51  0.206 ms
 4  100.100.8.118  1.369 ms 100.100.6.96  0.648 ms 240.0.52.57  0.233 ms
 5  240.0.228.5  0.326 ms * *
 6  240.0.32.16  0.371 ms 240.0.48.30  0.362 ms *
 7  * 240.0.228.31  0.251 ms *
 8  * * *
 9  * * 240.0.32.27  0.392 ms
10  * * *
11  * 242.0.154.49  1.321 ms *
12  * * 52.93.28.131  1.491 ms
13  * * 100.100.6.108  1.286 ms
14  100.92.212.7  67.909 ms 52.218.25.211  67.356 ms  67.929 ms

As you can see, the route is completely different, and as expected does not hit straight to the S3 endpoint.

TL;DR: make sure your route tables are correct, and only point to S3 buckets located in the same region.

GitHub Action runner images (AMI) for AWS EC2

As part of the RunsOn service, we automatically maintain and publish replicas of the official GitHub runner images ↗ images as AWS-formatted images (AMIs) in this repository: https://github.com/runs-on/runner-images-for-aws ↗.

New images are automatically released every 2 weeks, and are slightly trimmed to remove outdated software, or (mostly useless) caches.

Supported images

  • ubuntu22-full-x64
  • ubuntu22-full-arm64
  • ubuntu24-full-x64
  • ubuntu24-full-arm64

Supported regions

  • North Virginia (us-east-1)
  • Ohio (us-east-2)
  • Oregon (us-west-2)
  • Ireland (eu-west-1)
  • London (eu-west-2)
  • Paris (eu-west-3)
  • Frankfurt (eu-central-1)
  • Mumbai (ap-south-1)
  • Tokyo (ap-northeast-1)
  • Singapore (ap-southeast-1)
  • Sydney (ap-southeast-2)

Find the AMI

For the x86_64 image, search for:

  • name: runs-on-v2.2-<IMAGE_ID>-*
  • owner: 135269210855

For instance, for the ubuntu22-full-x64 image, search for:

  • name: runs-on-v2.2-ubuntu22-full-x64-*
  • owner: 135269210855

Notes

  • SSH daemon is disabled by default, so be sure to enable it in a user-data script if needed.

You can find more details on https://github.com/runs-on/runner-images-for-aws ↗.

Use GitHub App manifests to symplify your GitHub App registration flow

Clicking through the GitHub UI interface to register a GitHub App with the correct permissions and settings is tedious, and error-prone.

I recently discovered that you can automate all this with GitHub App manifests ↗, which contain all the required details about your app. You can then submit it to GitHub and dynamically exchange the manifest with the App ID, Webhook Secret, and Private key.

On the client front, you simply need a way to POST the manifest to GitHub, using a state parameter that you generate:

<form action="https://github.com/settings/apps/new?state=abc123" method="post">
 Register a GitHub App Manifest: <input type="text" name="manifest" id="manifest"><br>
 <input type="submit" value="Submit">
</form>


<script>
 input = document.getElementById("manifest")
 input.value = JSON.stringify({
   "name": "Octoapp",
   "url": "https://www.example.com",
   "hook_attributes": {
     "url": "https://example.com/github/events",
   },
   "redirect_url": "https://example.com/redirect",
   "callback_urls": [
     "https://example.com/callback"
   ],
   "public": true,
   "default_permissions": {
     "issues": "write",
     "checks": "write"
   },
   "default_events": [
     "issues",
     "issue_comment",
     "check_suite",
     "check_run"
   ]
 })
</script>

Upon submission, GitHub will redirect back to your site with a temporary code, that you can then exchange for the app secrets.

For RunsOn GitHub Action runners, I am using this feature so that you only need 2 clicks after installation to get your private RunsOn app registered into your own GitHub organization with all the correct settings.

With Probot, you can describe your GitHub App manifest in YAML, so this would look like:

# The list of events the GitHub App subscribes to.
default_events:
 - workflow_job
 - meta


default_permissions:
  # required for registering runners
  administration: write
  # required to access config file
  single_file: write
  # required to access collaborators and repository metadata
  metadata: read
  # Organization members and teams.
  # https://developer.github.com/v3/apps/permissions/#permission-on-members
  members: read
  # required to access workflow runs
  actions: read


single_file_paths:
  - ./.github/runs-on.yml


name: runs-on


# Set to true when your GitHub App is available to the public or false when it is only accessible to the owner of the app.
public: false

For more details, you can have a look at the Probot doc ↗ about this.

How to setup docker with NVIDIA GPU support on Ubuntu 22

This took a bit of a search for me, so here it is in case it’s useful:

Terminal window
cd /tmp/
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get install -y cuda-drivers-545 nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Then you should be able to run the following docker container with gpu enabled:

Terminal window
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

And get the following output:

Fri Dec  8 14:49:32 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.23.08              Driver Version: 545.23.08    CUDA Version: 12.3     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla T4                       On  | 00000000:00:1E.0 Off |                    0 |
| N/A   29C    P0              25W /  70W |      5MiB / 15360MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+


+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

Automatically cleanup outdated AMIs in all AWS regions

Here is a script you can use to automatically cleanup AMIs older than 60 days (configurable), while keeping the 2 most recent AMIs in each region. This helps to remove outdated images, as well as reducing storage costs for your AMIs.

Particularly useful in the case of runs-on.com, where we regularly rebuild base images whenever GitHub releases a new version of the image runner.

The bin/cleanup script (simply adjust the filters as needed):

#!/bin/bash
# Deregisters old AMIs and deletes associated snapshots, in all regions


set -e
set -o pipefail


APPLICATION="RunsOn"
REGIONS="$(aws ec2 describe-regions --query "Regions[].RegionName" --output text)"
# Number of days to keep AMIs
DAYS_TO_KEEP=${DAYS_TO_KEEP:=60}
# Define the age threshold in seconds (60 days)
AGE_THRESHOLD=$((DAYS_TO_KEEP*24*3600))
# Get the current timestamp in seconds since epoch
CURRENT_TIMESTAMP=$(date +%s)


for region in ${REGIONS[@]}; do
    echo "---- Region: ${region} ---"
    # List all your AMIs and extract relevant information using the AWS CLI
    image_count=$(aws ec2 describe-images --owner self --filters "Name=tag:application, Values=${APPLICATION}" --query 'length(Images)' --region "$region" --output text)
    echo "     Total AMIs in this region: ${image_count}"


    if [ "$image_count" -lt 2 ]; then
      echo "     Less than 2 AMIs found, skipping"
      continue
    fi


    aws ec2 describe-images --owner self --region "${region}" --filters "Name=tag:application, Values=${APPLICATION}" --query 'Images[*].[Name,ImageId,CreationDate]' --output text | \
      while read -r name image_id creation_date; do
        # Parse the creation date into seconds since epoch
        image_timestamp=$(date -d "$creation_date" +%s)


        # Calculate the age of the AMI in seconds
        age=$((CURRENT_TIMESTAMP - image_timestamp))


        # Check if the AMI is older than the threshold
        if [ $age -gt $AGE_THRESHOLD ]; then
          echo "     ! Deregistering AMI: ${image_id} (${name}) created on $creation_date"
          snapshot_id=$(aws ec2 describe-images --image-ids "$image_id" --query "Images[].BlockDeviceMappings[].Ebs.SnapshotId" --region "${region}" --output text)
          if [ "$DRY_RUN" = "true" ]; then
            echo "     DRY_RUN is set to true, skipping deregistering AMI ${image_id} and deleting snapshot ${snapshot_id}"
            continue
          fi
          aws ec2 deregister-image --image-id "$image_id" --region "${region}"
          echo "     ! Deleting snapshot ${snapshot_id} for AMI ${image_id}"
          aws ec2 delete-snapshot --snapshot-id "${snapshot_id}" --region "${region}"
        fi
      done
done

Example output:

---- Region: ap-southeast-2 ---
     Total AMIs in this region: 0
---- Region: eu-central-1 ---
     Total AMIs in this region: 5
     Deregistering AMI: ami-0576e83d0a0f89fbe (runner-ubuntu2204-1699888130) created on 2023-11-13T16:53:48.000Z
     Deleting snapshot snap-07db95a7f230d3f76 for AMI ami-0576e83d0a0f89fbe
     Deregistering AMI: ami-004d4d18e6db2f812 (runner-ubuntu-22-1699873337) created on 2023-11-13T12:40:48.000Z
     Deleting snapshot snap-0500b0e3fb95ab36a for AMI ami-004d4d18e6db2f812
     Deregistering AMI: ami-0e6239eae649effcd (runner-ubuntu22-20231115.7-1700233930) created on 2023-11-17T17:01:58.000Z
     Deleting snapshot snap-05e795e4c6fe9e66f for AMI ami-0e6239eae649effcd
     Deregistering AMI: ami-0dd7f6b263a3ce28c (runner-ubuntu22-20231115-1700156105) created on 2023-11-16T19:24:38.000Z
     Deleting snapshot snap-02c1aef800c429b76 for AMI ami-0dd7f6b263a3ce28c
---- Region: us-east-1 ---
     Total AMIs in this region: 4
     Deregistering AMI: ami-0b56f2d6af0d58ce0 (runner-ubuntu2204-1699888130) created on 2023-11-13T15:54:22.000Z
     Deleting snapshot snap-0f2e8759bea8f3937 for AMI ami-0b56f2d6af0d58ce0
     Deregistering AMI: ami-04266841492472a95 (runner-ubuntu22-20231115.7-1700233930) created on 2023-11-17T16:02:34.000Z
     Deleting snapshot snap-0f0fcf9c6406c3ad9 for AMI ami-04266841492472a95
     Deregistering AMI: ami-0738c7108915044fe (runner-ubuntu22-20231115-1700156105) created on 2023-11-16T18:21:40.000Z
     Deleting snapshot snap-03f16588f59ed7cea for AMI ami-0738c7108915044fe
     ...

Example GitHub Action workflow file to schedule a cleanup every night:

name: Cleanup


concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}


on:
  workflow_dispatch:
  schedule:
    - cron: '0 2 * * *'


jobs:
  check:
    timeout-minutes: 30
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v4
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-east-1
      - run: bin/cleanup