Troubleshooting VScode GPU Node Resource Issues

Hey guys, let's dive into a real head-scratcher – the VScode GPU node resource issue. This is a common problem that can really put a damper on your workflow. I'll break down the issue, the investigation steps, and what we can do to fix it. This is crucial, especially if you're relying on GPU-enabled VScode for your projects. Let's make sure those resources are always available when you need them!

Understanding the Core Problem: Resource Unavailability

The Issue Unveiled

On July 10, 2025, a bunch of users reported that they couldn't deploy the GPU-enabled VScode. The first thing we did was check the user's POD events, and this is what we saw:

Warning  FailedScheduling   27m (x3 over 37m)    default-scheduler   0/89 nodes are available: 3 Insufficient memory, 86 node(s) didn't match Pod's node affinity/selector. preemption: 0/89 nodes are available: 3 Insufficient memory, 86 Preemption is not helpful for scheduling.

Essentially, the system couldn't find any nodes with enough resources to run the VScode instance. The error messages point to insufficient memory and nodes that didn't match the POD's requirements. This is a classic case of resource contention.

Digging into the Cluster Autoscaler Logs

We then took a look at the cluster autoscaler logs, and things got a little clearer. The logs revealed that AWS was having trouble provisioning new nodes:

I1007 10:07:11.841915       1 orchestrator.go:111] Upcoming 0 nodes
W1007 10:07:11.842072       1 orchestrator.go:603] Node group eks-prod20240821165852296500000001-bec8baf0-3f3d-1e84-4893-a3010e1a3b54 is not ready for scaleup - backoff with status: {true {OutOfResource placeholder-cannot-be-fulfilled AWS cannot provision any more instances for this node group}}  

This is a crucial piece of information. The autoscaler was trying to scale up the cluster to accommodate the demand, but AWS couldn't provide the necessary resources. This is often due to various limitations, such as insufficient capacity in the selected availability zones or account-level resource limits. This means that your GPU-enabled VScode deployments were blocked because there weren't enough underlying resources available.

The Impact: Limited Resources

For a significant part of the day, the cluster had only two out of a possible ten nodes running. This was a direct result of AWS's inability to provision additional nodes. This drastically limited the number of users who could use the GPU-enabled VScode simultaneously. The situation was made worse by a recent update.

The Culprit: New VScode Release

It appears that the problem was further intensified by the recent release of Visual Studio Code 2.28.0 (GPU, 2vCPU, 24GB RAM). This new release now requires 24GB of RAM, which is double the previous requirement of 12GB. This increase meant that nodes with 62GB RAM could only serve two users at a time, significantly increasing the pressure on the available resources.

Investigating the Root Causes and Solutions

Investigate the following:

Now, let's dig into some specific areas that need investigation to solve this. The goal is to ensure users can consistently deploy GPU-enabled VScode instances.

1. Is the AWS cannot provision any more instances for this node group a regular occurrence or a one-off and how to resolve? This is crucial. Is this a common issue, or did something trigger it? If it's frequent, we need to understand why AWS is unable to provide the resources. Potential solutions include:
   • Checking AWS Service Limits: Ensure that your AWS account isn't hitting any limits on the number of instances or the types of instances it can provision. You can request an increase in these limits if needed.
   • Availability Zone Capacity: The issue might be related to the capacity in the specific Availability Zones your cluster uses. Try spreading your nodes across multiple zones to increase the chances of finding available resources.
   • Node Group Configuration: Review your node group configuration, including the instance types and the desired capacity. Make sure that the instance types are available in your chosen Availability Zones.
   • Monitoring and Alerting: Implement monitoring and alerting to identify the AWS cannot provision any more instances condition early. This will allow you to react quickly.
2. Is the sizing of the nodes correct in light of the standard GPU release requiring 24GB RAM? With the new RAM requirements, the existing node sizes might be insufficient. We need to consider:
   • Node Size Optimization: Evaluate whether the current node sizes are optimal for the workload. Are you over-provisioning resources, or are you running out of memory? Consider using instance types with more RAM, such as those optimized for memory-intensive workloads.
   • Resource Requests and Limits: Ensure that your VScode deployments have appropriate resource requests and limits. This helps the scheduler make informed decisions about node placement and prevents individual pods from consuming excessive resources.
   • Node Utilization: Monitor the resource utilization of your nodes (CPU, memory, GPU). If the nodes are consistently underutilized, you might be able to reduce their size or adjust the number of nodes in your cluster.
3. Are any of the users allocated this restricted release no longer requiring it? This is about optimizing the resources allocated. Some users might not actively need the resources. If users don't need the new release, we should consider:
   • User Segmentation: Identify users who are actively using the GPU-enabled VScode and those who are not. Consider creating different node pools for users based on their resource requirements.
   • Resource Reclamation: If a user is no longer actively using their VScode instance, consider implementing mechanisms to reclaim the resources. This can involve automatically terminating idle instances or providing users with the ability to release their resources when they're done.
   • Communication: Communicate with users about the resource constraints and encourage them to release resources when they are not actively using them.

Proposal and Action Items

Actions and Considerations

Without any specific proposal, here are some general suggestions for resolution. These could be applied in practice:

• Increase Node Capacity and Evaluate Instance Types: The core of the issue seems to be a lack of available resources. The immediate response is to either increase the node capacity (if possible) or consider more powerful instances.
• Review resource requests and limits: Ensure all pods have valid resource requests. This ensures that Kubernetes can schedule them correctly.
• Implement monitoring: Monitor the cluster resources closely. Alert on any unusual patterns.
• Automated Scaling: Review and optimize the Cluster Autoscaler configuration to ensure that the cluster can automatically scale up and down based on demand.

Definition of Done

Let's make sure we have a clear definition of