Chamber

Founded by Ivan Vrkic in 2024

Diagnose failed machine learning training jobs and reclaim idle GPUs across compute clusters

Pricing model

Demo

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Chamber to manage and monitor your machine learning infrastructure across cloud and on-premise setups. It deploys autonomous agents that watch training jobs, detect failures like out-of-memory errors, and automatically apply fixes to resume workloads from checkpoints. The tool inspects resource usage, packing idle GPUs during CPU-heavy tasks to cut down wasted compute. Teams can interact with an agent through Slack or the command line to check cluster health, inspect bottlenecks, and approve automated repairs without context switching across multiple dashboards.

What Chamber does

Deploy cluster agents using HelmDiagnose machine learning training errorsRerun failed experiments from safe checkpointsAllocate idle GPUs to pending workloadsMonitor cluster telemetry across multi-cloud environmentsTrigger automated remediation through Slack commandsAutomatically detects training failures like CUDA out-of-memory errorsRestarts failed jobs from the latest checkpoint with adjusted configurationsPacks workloads onto idle GPUs during heavy CPU computation cyclesAnswers infrastructure questions and runs remediation commands inside SlackMonitors GPU fleets across AWS, Google Cloud, Azure, and on-premise clustersEnforces blast-radius boundaries and rollback protections on automated fixesDeploys directly into private Kubernetes or Slurm clusters via Helm