AI Metrics

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 dashboard shown above is from a simulated network 1,000 switches, each with 48 ports access ports connected to a host. Activity occurs in a 256mS on / off cycle to emulate an AI learning run. 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 AI Metrics application in a production environment and integrate the metrics with back end Prometheus / Grafana dashboards. Please try AI Metrics out and share your comments so that the set of metrics can be refined and extended to address operational requirements.

docker run -p 8008:8008 -p 6343:6343/udp sflow/ai-metrics

User Docker to run the pre-built sflow/ai-metrics image and access the web interface on port 8008.

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 and NVIDIA Cumulus Linux 5.11 for AI / ML 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 guage to get specific details.

Congratulations! The configuration is now complete and you should see charts at top of article in AI Metric application Traffic tab.

The AI Metrics application exports the metrics shown in Prometheus scrape format, see Help tab for details. The Docker image also includes the Prometheus application that allows flow metrics to be created and extracted, see Flow metrics with Prometheus and Grafana.

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.