Idle allocation
GPUs remain reserved while the job waits on startup, data, a deadlock, or an empty workload.
For AI labs & ML teams
Lower the cost of every successful training run
RidgeScope finds the GPU-hours lost to idle jobs, slow ranks, data and storage bottlenecks, distributed hangs, and runs that finish without producing a useful model.
Where training budgets disappear01 02 03 04
Four kinds of GPU time you should not pay twice for
Slow allocation
Checkpoint I/O, launch gaps, weak input pipelines, or one straggler stretch every expensive step.
Stalled allocation
Ranks spin inside a collective after a peer dies, making engine utilization look healthy.
Completed-but-wasted
The run exits successfully but fails to learn, resumes from scratch, or stops before meaningful training.
From allocation to action
No SDK. No query language. No source-code review.
RidgeScope reads the evidence your Slurm training already produces and turns it into a cited investigation your ML and infrastructure engineers can evaluate together.
See the fleet
Know which jobs are computing, waiting, idle, unhealthy, or finished with questionable value.
Open the run
Launch an investigation from Mission Control without collecting a manual bundle of dashboards and logs.
Read the verdict
Get what the data shows, what it implies, how to confirm the cause, and the recommended next action.
Compare the rerun
Use prior healthy runs from the same workload as evidence when a trustworthy baseline exists.
Silent waste, made visible101 saves / 100 steps Exit code 0 81% idle
The run can finish and still waste the allocation
Checkpointing dominated the run
The run learned and completed, but save cadence consumed the wall clock and collapsed useful compute.
The model learned nothing
Infrastructure stayed green while the loss rose and plateaued, exposing a fully spent but unusable run.
A debug flag serialized kernels
Training advanced, but launch blocking left large gaps that ordinary failure monitoring would ignore.
