AI network performance monitoring using containerlab

AI Metrics is available on GitHub. The application provides performance metrics for AI/ML RoCEv2 network traffic, for example, large scale CUDA compute tasks using NVIDIA Collective Communication Library (NCCL) operations for inter-GPU communications: AllReduce, Broadcast, Reduce, AllGather, and ReduceScatter.

The screen capture is from a containerlab topology that emulates a AI compute cluster connected by a leaf and spine network. The metrics include:

• Total Traffic Total traffic entering fabric
• Operations Total RoCEv2 operations broken out by type
• Core Link Traffic Histogram of load on fabric links
• Edge Link Traffic Histogram of load on access ports
• RDMA Operations Total RDMA operations
• RDMA Bytes Average RDMA operation size
• Credits Average number of credits in RoCEv2 acknowledgements
• Period Detected period of compute / exchange activity on fabric (in this case just over 0.5 seconds)
• Congestion Total ECN / CNP congestion messages
• Errors Total ingress / egress errors
• Discards Total ingress / egress discards
• Drop Reasons Packet drop reasons

Note: Clicking on peaks in the charts shows values at that time.

This article gives step-by-step instructions to run the demonstration.

git clone https://github.com/sflow-rt/containerlab.git

Download the sflow-rt/containerlab project from GitHub.

git clone https://github.com/sflow-rt/containerlab.git
cd containerlab
./run-clab

Run the above commands to download the sflow-rt/containerlab GitHub project and run Containerlab on a system with Docker installed. Docker Desktop is a conventient way to run the labs on a laptop.

containerlab deploy -t rocev2.yml

Start the 3 stage leaf and spine emulation.

The initial launch may take a couple of minutes as the container images are downloaded for the first time. Once the images are downloaded, the topology deploys in a few seconds.

./topo.py clab-rocev2

Run the command above to send the topology to the AI Metrics application and connect to http://localhost:8008/app/ai-metrics/html/ to access the dashboard shown at the top of this article.

docker exec -it clab-rocev2-h1 hping3 exec rocev2.tcl 172.16.2.2 10000 500 100

Run the command above to simulate RoCEv2 traffic between h1 and h2 during a training run. The run consists of 100 cycles, each cycle involves exchange of 10000 packets followed by a 500 millisecond pause (similuating GPU compute time). You should immediately see charts updating in the dashboard.

Clicking on the wave symbol in the top right of the Period chart pulls up a fast updating Periodicity chart that shows how sub-second variability in the traffic due to the exchange / compute cycles is used to compute the period shown in the dashboard, in this case a period of just under 0.7 seconds.

Connect to the included sflow-rt/trace-flow application, http://localhost:8008/app/trace-flow/html/

suffix:stack:.:1=ibbt

Enter the filter above and press Submit. The instant a RoCEv2 flow is generated, its path should be shown. See Defining Flows for information on traffic filters.

docker exec -it clab-rocev2-leaf1 vtysh

The routers in this topology run the open source FRRouting daemon used in data center switches with NVIDIA Cumulus Linux or SONiC network operating systems. Type the command above to access the CLI on the leaf1 switch.

leaf1# show running-config

For example, type the above command in the leaf1 CLI to see the running configuration.

Building configuration...

Current configuration:
!
frr version 10.2.3_git
frr defaults datacenter
hostname leaf1
log stdout
!
interface eth3
 ip address 172.16.1.1/24
 ipv6 address 2001:172:16:1::1/64
exit
!
router bgp 65001
 bgp bestpath as-path multipath-relax
 bgp bestpath compare-routerid
 neighbor fabric peer-group
 neighbor fabric remote-as external
 neighbor fabric description Internal Fabric Network
 neighbor fabric capability extended-nexthop
 neighbor eth1 interface peer-group fabric
 neighbor eth2 interface peer-group fabric
 !
 address-family ipv4 unicast
  redistribute connected route-map HOST_ROUTES
 exit-address-family
 !
 address-family ipv6 unicast
  redistribute connected route-map HOST_ROUTES
  neighbor fabric activate
 exit-address-family
exit
!
route-map HOST_ROUTES permit 10
 match interface eth3
exit
!
ip nht resolve-via-default
!
end

The switch is using BGP to establish equal cost multi-path (ECMP) routes across the fabric - this is very similar to a configuration you would expect to find in a production network. Containerlab provides a great environment to experiment with topologies and configuration options before putting them in production.

The results from this containerlab project are very similar to those observed in production networks, see Comparing AI / ML activity from two production networks. Follow the instruction in AI Metrics with Prometheus and Grafana to deploy this solution to monitor production GPU cluster traffic.

AI Metrics with Grafana Cloud

The Grafana AI Metrics dashboard shown above tracks performance metrics for AI/ML RoCEv2 network traffic, for example, large scale CUDA compute tasks using NVIDIA Collective Communication Library (NCCL) operations for inter-GPU communications: AllReduce, Broadcast, Reduce, AllGather, and ReduceScatter.

The metrics include:

• Total Traffic Total traffic entering fabric
• Operations Total RoCEv2 operations broken out by type
• Core Link Traffic Histogram of load on fabric links
• Edge Link Traffic Histogram of load on access ports
• RDMA Operations Total RDMA operations
• RDMA Bytes Average RDMA operation size
• Credits Average number of credits in RoCEv2 acknowledgements
• Period Detected period of compute / exchange activity on fabric (in this case just over 0.5 seconds)
• Congestion Total ECN / CNP congestion messages
• Errors Total ingress / egress errors
• Discards Total ingress / egress discards
• Drop Reasons Packet drop reasons

AI Metrics with Prometheus and Grafana describes how to stand up an analytics stack with Prometheus and Grafana to track performance metrics for an AI/ML GPU cluster. This article shows how to integrate with Prometheus and Grafana hosted in the cloud, Grafana Cloud, instead of running the services locally.

Note: Grafana Cloud has a free service tier that can be used to test this example.

Save the following compose.yml file on a system running Docker.

configs:
  config.alloy:
    content: |
      prometheus.scrape "prometheus" {
        targets = [{
          __address__ = "sflow-rt:8008",
        }]
        forward_to   = [prometheus.remote_write.grafanacloud.receiver]
        metrics_path = "/app/ai-metrics/scripts/metrics.js/prometheus/txt"
        scrape_interval = "10s"
      }

      prometheus.remote_write "grafanacloud" {
        endpoint {
          url  = "https://<Your Grafana Cloud Prometheus Instance>/api/prom/push"
          basic_auth {
            username = "<Your Grafana.com User ID>"
            password = "<Your Grafana.com API Token>"
          }
        }
      }

networks:

  monitoring:
    driver: bridge

services:

  sflow-rt:
    image: sflow/ai-metrics
    container_name: sflow-rt
    restart: unless-stopped
    ports:
      - '6343:6343/udp'
      - '8008:8008'
    networks:
      - monitoring

  alloy:
    image: grafana/alloy
    container_name: alloy
    restart: unless-stopped
    configs:
      - source: config.alloy
        target: /etc/alloy/config.alloy
    depends_on:
      - sflow-rt
    networks:
      - monitoring

Find the settings needed to upload metrics to Prometheus by clicking on the Send Metrics button in your Grafana Cloud account.

Edit the highlighted prometheus.remote_write endpoint settings (url, username and password) to match those provided by Grafana Cloud.

docker compose up -d

Run the command above to start streaming metrics to Grafana Cloud. Click on the Grafana Launch button to access Grafana and add the AI Metrics dashboard (ID: 23255).

Enable sFlow on all switches in the cluster (leaf and spine) using the recommeded settings. Enable sFlow dropped packet notifications to populate the drop reasons metric, see Dropped packet notifications with Arista Networks, NVIDIA Cumulus Linux 5.11 for AI / ML and Dropped packet notifications with Cisco 8000 Series Routers for examples.

Note: Tuning Performance describes how to optimize settings for very large clusters.

Industry standard sFlow telemetry is uniquely suited monitoring AI workloads. The sFlow agents leverage instrumentation built into switch ASICs to stream randomly sampled packet headers and metadata in real-time. Sampling provides a scaleable method of monitoring the large numbers of 400G/800G links found in AI fabrics. Export of packet headers allows the sFlow collector to decode the InfiniBand Base Transport headers to extract operations and RDMA metrics. The Dropped Packet extension uses Mirror-on-Drop (MoD) / What Just Happened (WJH) capabilities in the ASIC to include packet header, location, and reason for EVERY dropped packet in the fabric.

Talk to your switch vendor about their plans to support the Transit delay and queueing extension. This extension provides visibility into queue depth and switch transit delay using instrumentation built into the ASIC.

A network topology is required to generate the analytics, see Topology for a description of the JSON file and instructions for generating topologies from Graphvis DOT format, NVIDIA NetQ, Arista eAPI, and NetBox.

Use the Topology Status dashboard to verify that the topology is consistent with the sFlow telemetry and fully monitored. The Locate tab can be used to locate network addresses to access switch ports.

Note: If any gauges indicate an error, click on the gauge to get specific details.

Congratulations! The configuration is now complete and you should see charts above in AI Metric application Traffic tab. In addition, the AI Metrics Grafana dashboard at the top of this page should start to populate with data.

Flow metrics with Prometheus and Grafana and Dropped packet metrics with Prometheus and Grafana describe how to define additional flow-based metrics to incorporate in Grafana dashboards.

Getting Started provides an introductions to sFlow-RT, describes how to browse metrics and traffic flows using tools included in the Docker image, and links to information on creating applications using sFlow-RT APIs.