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Enterprise Layer Intelligence
Categories/AI Hosting & Inference/Clockwork Systems, Inc.
Clockwork Systems, Inc.

Clockwork Systems, Inc.

Founded 2018

Keep distributed machine learning workloads running through GPU and network hardware failures

Pricing model

Demo

You can use Clockwork to keep distributed artificial intelligence training and inference jobs running when network switches, links, or GPUs fail. The software detects network flaps and immediately reroutes traffic across available network interface cards without stopping your workload. If a GPU dies, it migrates running processes to healthy hardware over RDMA so you do not lose progress or need to restart from old checkpoints. It also monitors your entire network fabric, pinpointing degraded cables, misconfigurations, and silent hardware issues that slow down massive compute clusters.

What Clockwork Systems, Inc. does

Reroute network traffic automatically when network links flapMigrate training jobs live from failing GPUs to spare nodesCapture full multi-node cluster snapshots without code modificationsAudit cluster fabric links and switches before training runs beginPinpoint exact cables, NICs, and switches causing workload slowdownsMeasure one-way network packet delays across GPU cluster fabricsSynchronize hardware timestamps across distributed clusters with sub-microsecond precisionAutomatic network traffic rerouting across healthy NICs during link flapsLive GPU worker migration over RDMA without losing iteration progressMulti-node distributed training snapshots completed in under 20 secondsSub-microsecond clock synchronization across cluster fabric switchesOut-of-band directional delay probing to detect micro-congestionAutomated pre-flight cluster audits to validate links and nodes before jobs startDrop-in NCCL plugin installation requiring zero model code alterations

Pricing breakdown

Pricing model: Book a demo

Frequently asked

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Clockwork, GPU fault tolerance, distributed training, AI cluster management, LinkPass, TorchPass, FleetLens, network fabric observability, GPU live migration, RDMA network monitoring, NCCL plugin, deep learning resilience, cluster goodput, model checkpointing, AI infrastructure