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Convia edited this page Mar 2, 2026 · 2 revisions

Argus

You might need Argus if…

  • Your GPUs are busy but inference is still slow
  • Adding more nodes sometimes makes performance worse
  • The same experiment gives different results
  • No profiler shows a clear bottleneck
  • Latency spikes appear without configuration changes

If these look familiar, the issue may not be compute utilization.

It may be execution behavior.

Argus is an execution observation protocol for distributed AI systems.

It does not optimize models and does not benchmark hardware.

Argus helps you check whether a system behaves consistently when the same workload is repeated.

What problem is Argus trying to solve?

In distributed AI workloads, systems often appear healthy:

  • GPUs are utilized
  • CPU usage looks normal
  • Memory is sufficient
  • No crashes occur

Yet performance becomes inconsistent.

You may observe:

  • latency spikes without configuration changes
  • scaling more nodes making performance worse
  • experiments that cannot be reproduced
  • intermittent retries or timeouts
  • tail latency dominating total runtime

Traditional monitoring tools measure resource usage.

Argus observes execution behavior.

What Argus produces

Instead of a single performance number, Argus generates a reproducible observation record:

  • execution metrics (metrics.json)
  • structured report (report.md)
  • environment metadata (run_meta.json)

The goal is not to prove performance improvement.

The goal is to determine whether execution behavior is stable or unstable across repeated runs.

When should you use Argus?

Argus may be useful if:

  • the system is slow but utilization is high
  • scaling nodes does not improve performance
  • identical runs produce different results
  • debugging shows no obvious bottleneck
  • performance issues are intermittent rather than constant

Argus is not a profiler and not a monitoring dashboard.

It is a protocol to observe whether execution behavior itself is reproducible.

What Argus does NOT do

Argus does not:

  • optimize performance
  • accelerate models
  • replace profilers
  • evaluate model quality

Argus only records and reports execution behavior.

Start here

  • See When to use Argus
  • See What Argus measures
  • See How to read the report
  • See Limitations

First time here? Start with When to use Argus.

Repository: https://github.com/tongro2025/Argus

Argus Wiki

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