Cases13 cases

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1. IL-0070 Critical pods on core nodes evicted when Prometheus began scraping about 200 nodes after a dask-gateway scale-up

   2i2c2023

   Symptoms

   The hub pod of the prod JupyterHub on the leap cluster (leap.2i2c.org) restarted unexpectedly during a large node scale-up, and a user opened a support ticket. The ingress-nginx and proxy pods kept running.

   Pod evictionPods Pending

   Trigger

   A user's dask-gateway workload added about 100 dask-worker nodes within a minute, and Prometheus started scraping the metrics exporters on about 200 nodes.

   Root cause

   Capacity exhaustion

   Component

   PrometheusNode pool

   Contributing factor

   No headroomExcessive blast radiusMonitoring blind spot

   Detection

   A user support ticket (2i2c Freshdesk ticket 414) about the leap hub; the engineer then looked at Grafana and the cluster's nodes and events.

   Action items

   • Configure CPU and memory requests for the hub and proxy pods (#2127; at least 100m CPU was proposed)
   • Make the Grafana OOMKiller dashboard work (#2213)
   • Move LEAP cloud infrastructure to shared nodes (#2209)
   • Update resource requests and limits, mainly for ingress-nginx and Prometheus (#2324)
   • Decide whether Prometheus should scrape dask worker pods (#2366)

   Lessons

   Prometheus load grows with the number of nodes, so a burst of autoscaled nodes can push a nearly full core node over the edge and evict the pods that matter most. Core pods that request 10m of CPU get a tenth of the CPU share of busy neighbours that request 100m. Kubernetes events expire after an hour, so evidence of OOM kills needs a dashboard that keeps it.

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2. IL-0115 One of OpenAI's three production AKS clusters went offline through a human error, and rebuilding it took 10½ hours because an AKS Kubernetes upgrade broke the ingress and AKS cannot be downgraded

   OpenAI2022

   Symptoms

   At 18:56 PDT on 23 June 2022 (01:56 UTC on 24 June) several models went offline at once: some fine-tuned curie and davinci models, the Codex beta models code-davinci-001 and code-cushman-001, the legacy GPT models curie, babbage and ada, and all Embeddings models.

   Service outageHigh error rateIntermittent timeouts

   Trigger

   A destructive operation on one of the three production clusters, caused by human error that the existing safeguards did not catch, took the whole cluster offline.

   Root cause

   Operator error

   Component

   Ingress controllerModel hub

   Contributing factor

   No safeguard on destructive changeNo headroomDeferred upgradesOverlapping failures

   Remediation

   • re-allocated the models from the lost cluster to the remaining two clusters
   • rebuilt the VPN link with Azure support after losing the connection to AKS during cluster setup
   • built workarounds with Azure support after losing the connection to Azure Private Link
   • tore down the cluster and rebuilt it from scratch after the upgraded Kubernetes version broke the ingress
   • moved traffic back into the rebuilt cluster between 05:30 and 06:30 PDT

   Action items

   • Add checks and locks so that this and similar production-critical infrastructure cannot be modified by accident
   • Add better visibility into the operations being performed and make parts of the system more lexicographically distinct
   • Keep working with Azure on the AKS network connectivity issues so they do not disrupt new cluster operations
   • Update playbooks with the new cluster failure modes and change the setup steps for new clusters and infrastructure
   • Upgrade Kubernetes systematically and set aside time so the clusters do not fall behind again
   • Shard fine-tuned models across multiple clusters and work towards more automatic cross-cluster replication
   • Distribute and replicate Azure Blob Storage so fine-tuned models load faster and more reliably
   • Finish rolling out node-to-node caching to relieve Azure Blob Storage bottlenecks
   • Change how model weights are stored so they are more efficient and faster to load

   Lessons

   Losing one of three clusters exhausted the GPU headroom, and GPUs could not be added on short notice, so full recovery depended on bringing the same cluster back. Rebuilding it exposed a deferred Kubernetes upgrade: the newer version broke the ingress, and because AKS cannot downgrade there was no way back except a full rebuild. Recovering many large models at once is limited by the bandwidth of the storage they load from, and the caches that normally hide this do not help when everything moves to a different cluster.

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3. IL-0126 The OPT-175B training run restarted more than 40 times in two weeks as cloud GPU nodes failed with ECC errors, replacement nodes came back faulty, and checkpoint downloads hung

   Meta AI2021

   Symptoms

   Between the team's 17 November 2021 update and 3 December 2021, the 175B-parameter OPT training run went down and had to be restarted more than 40 times, for hardware, infrastructure and training stability reasons. A hardware fault could stop the run for hours at any time of day, and several did over the Thanksgiving break.

   Training job failureGPU unavailable

   Trigger

   Nodes in the cloud training cluster failed with ECC errors, and each failure took the training run down.

   Root cause

   Dependency failure

   Component

   GPU hardware and memoryNCCLInfiniBand fabricObject storage

   Contributing factor

   No headroomLimited debug accessNo runbook

   Detection

   During these two weeks the team added monitoring of train.log, GPU burn-in testing to detect memory errors, and automated InfiniBand testing.

   Remediation

   • replaced failed nodes, and later did it through a script
   • added GPU burn-in testing to detect memory errors
   • automated InfiniBand testing
   • added monitoring of train.log
   • debugged the cloud provider's Python API until checkpoint downloads from the blob store worked well enough

   Action items

   • Run an on-call rotation for the training run, with runbooks and tooling for the most common issues, and invite more people to join it

   Lessons

   At this scale cloud hardware failures are certain, and the team concluded that automating the handling of hardware failures is necessary. Without spare nodes and burn-in tests, each failure costs hours and a replacement can bring the same fault back. The team also concluded that cloud infrastructure instability is something to always prepare for, because the customer has no control over the underlying hardware. A checkpoint large enough to need hundreds of multi-gigabyte downloads makes the storage path part of recovery time.

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4. IL-0004 A new default pod priority let a new Cortex cluster preempt every production ingester in a cascade, taking Grafana Cloud Hosted Prometheus down

   Grafana Labs2019

   Symptoms

   On Friday 19 July 2019 the Grafana Cloud Hosted Prometheus service, which is built on the Cortex project, was down for 26 minutes. New error-budget-based Prometheus alerts paged the on-call engineer within 4 minutes of the start.

   Pod evictionHigh error rateService outageOOMKilled

   Trigger

   An engineer created a new dedicated Cortex cluster for a large customer. Its configuration did not include the new pod priorities, so all its pods got the default medium priority, and the Kubernetes cluster did not have enough free resources to fit it.

   Root cause

   Configuration change

   Component

   Pod priority and preemption

   Contributing factor

   Partial rolloutAutomation amplificationMissed in reviewNo headroomRetry storm

   Detection

   New error-budget-based Prometheus alerts paged the on-call engineer within 4 minutes of the start of the outage.

   Remediation

   • Scaled up the underlying Kubernetes cluster to fit both the new and the production Cortex clusters
   • Diagnosed and fixed the out-of-memory errors in the authenticating reverse proxies in front of Cortex

   Action items

   • Give the production ingesters the high priority class, which should have been done before medium became the default
   • Have more people review changes to configuration that is global to the cluster
   • Require a design document for every configuration change outside a namespace
   • Automate the sizing of the frontend authenticating reverse proxy so it does not run out of memory under overload
   • Investigate the default backoff and scaling behaviour of Prometheus to prevent a stampeding herd of retries

   Lessons

   Medium should not have become the default priority until the production ingesters had the high priority, and that designation should have reached production sooner. The change was considered too minor for a design document and was only discussed in a GitHub issue; cluster-global configuration needs more scrutiny. On the positive side, once Cortex had enough capacity it recovered by itself, and Grafana Loki helped confirm that the ingesters exited gracefully.

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