Cases2 cases

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1. IL-0137 Training initialisation on a 60-node NVIDIA B200 cluster took more than 8 hours instead of minutes because fragmented small random I/O from all nodes saturated the metadata service of the VAST Data NFS storage

   Lablup Inc2026

   Symptoms

   The 60-node NVIDIA B200 training configuration for the Solar Open pre-training run showed no major problem during initial validation on 2 to 4 nodes. Once the job ran at its full size of 60 nodes, training initialisation, which reads from the VAST Data storage and should have finished within minutes, took more than 8 hours, and storage throughput stayed far below the theoretical limit.

   Throughput degradation

   Trigger

   Scaling the training job from 2 to 4 nodes in validation to the full 60-node configuration.

   Root cause

   Capacity exhaustion

   Component

   Shared network filesystem

   Contributing factor

   Unrepresentative stagingMonitoring blind spot

   Detection

   The problem showed up as training initialisation taking hours after the job was scaled to 60 nodes; application logs showed only the delayed initialisation.

   Remediation

   • Sharded the Arrow training data files by rank on the application side
   • Switched the storage to asynchronous deletion instead of synchronous rm/unlink
   • Tuned readahead

   Lessons

   Storage behaviour measured on 2 to 4 nodes did not predict behaviour on 60 nodes: a metadata bottleneck of this kind appears only at production scale, so small pre-tests cannot find it. Per-node network rates can look normal while the combined access pattern of all nodes overwhelms the storage metadata path. Finding the cause took metrics from the application, scheduler, network and storage on one shared timeline; monitoring by any single team was not enough. The report concludes that GPU capacity has to be provisioned together with a matching storage path.

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2. IL-0129 Throughput of the 384-GPU SmolLM3 pretraining run collapsed because shared WekaFS storage evicted dataset shards to S3, then kept dropping on local NVMe because the nanotron dataloader built an index that grew with the step count

   Hugging Face2025

   Symptoms

   Within hours of launching the full SmolLM3 pretraining run (3B parameters, 11T tokens, 384 H100 GPUs on 48 nodes), training throughput fell steeply, with repeated sharp drops. The team expected a steady 13,500 to 14,000 tokens per second, and none of the throughput ablations run beforehand on the same 48 nodes had shown drops.

   Throughput degradation

   Trigger

   Launching the full run with the whole ~24 TB dataset on shared WekaFS storage and the real step count for 11T tokens, both far larger than in the ablation runs.

   Root cause

   Capacity exhaustion

   Component

   Shared network filesystemTraining data loader

   Contributing factor

   Overlapping failuresUnrepresentative stagingUnverified assumption

   Detection

   The team watched training throughput in tokens per second and saw it fall within hours of launch; node-monitoring metrics then tied the large fall to spikes in disk read latency.

   Remediation

   • moved the training data from WekaFS to the local NVMe RAID scratch storage on each node
   • copied the dataset to replacement nodes from a healthy node with fpsync, cutting the copy from 3 hours to 1.5 hours
   • kept a spare node with the dataset preloaded in the Slurm reservation to swap in when a node failed
   • reproduced the drops on a single node and with step counts of about 3M and 32k to rule out hardware and isolate the step count
   • replaced nanotron's nanosets dataloader with the TokenizedBytes dataloader copied from the SmolLM2 setup, which removed the drops
   • pre-shuffled the tokenized sequences offline for each epoch with different seeds (about 1 hour) to fix the noisier loss from TokenizedBytes

   Lessons

   Ablations on data subsets and a short step horizon did not exercise either cause: a shared cache-and-evict filesystem near its capacity, and a dataloader index that grows with the step count. Reproducing the drops on one node separated hardware from software, and varying only the step count pointed at the dataloader. The team chose proven replacements over repairs under deadline pressure, taking an existing dataloader instead of fixing nanosets and offline shuffling instead of changing the dataloader, and warns that too many such shortcuts leave a patchwork system that is hard to maintain.

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