Design for sharing GPU across multiple APIs

[RobertLucian]

Description

An API can only be given entire GPU units. Add support for fractional values for the GPU resource. Here's an example:

# cortex.yaml
- name: <string>  # API name (required)
  # ...
  compute:
    gpu: 300m

Motivation

Better GPU resource utilization across multiple APIs. This can reduce the overall costs for the user. If an API is supposed to run very rarely and doesn't need much inference capacity, then giving 100m of the GPU resource might be desirable - the current alternative is to give an entire GPU to the API, and this can be expensive/wasteful.

Additional context

There are 2 ways to address this:

1. At the driver level (i.e. device plugin for the GPU). This is the preferred method.
2. At the pod level, by having a single pod per instance which is responsible for handling the prediction requests of all API replicas of all APIs residing on that instance. This may incur significant performance penalties. The single pod also represents the single-point-of-failure, so this may be undesirable.

Open questions

• If a pod uses more GPU than requested is there a way to evict it?

Useful links for the first approach (where the device plugin handles all of this):

1. https://github.com/kubernetes/kubernetes/issues/52757
2. https://github.com/AliyunContainerService/gpushare-scheduler-extender
3. https://github.com/sakjain92/Fractional-GPUs
4. https://github.com/Deepomatic/shared-gpu-nvidia-k8s-device-plugin
5. https://github.com/tkestack/gpu-manager

[creatorrr]

What's the status of this, @RobertLucian ? We could really use this for our workloads. Would love to help as well

[RobertLucian]

@creatorrr we've tabled this for now. That's because we didn't find a good reliable solution to this and some of the projects that would support this also appear to be stale.

From what I can recall, there were issues with memory isolation between different vGPUs. @miguelvr might be able to give more context as he did some research on one or two of these projects.

That being said, if you want to support this, you probably want to start with:

1. https://github.com/cortexlabs/cortex/blob/master/CONTRIBUTING.md
2. This is the Nvidia device plugin: https://github.com/cortexlabs/cortex/blob/master/manager/manifests/nvidia.yaml
3. This is where we install it on a new cluster: https://github.com/cortexlabs/cortex/blob/master/manager/install.sh#L65-L68
4. You might have to edit the labels on the instances: https://github.com/cortexlabs/cortex/blob/6ca17086cf7019d2ba8e233f3beb5ab6e1ab073f/manager/generate_eks.py#L119-L120
5. Check out for all the resource requests/limits "nvidia.com/gpu" in https://github.com/cortexlabs/cortex/blob/be39ba721efd4f0cd5aafaa9efdb7b6922f5db0c/pkg/workloads/k8s.go.
6. Make sure the taints/affinities are correctly applied.

[creatorrr]

Thanks! I'll try to work on this over the weekend. In the meantime, sharing notes from my research:

• You're right, a lot of the projects mentioned in the description are stale
• AliyunContainerService/gpushare-scheduler-extender requires replacing the nvidia-device-plugin and I am not sure that's a good idea
• Deepomatic/shared-gpu-nvidia-k8s-device-plugin is a very limited and hacky workaround
• nvidia seems to be working on this and there is even an open PR for partial support but it doesn't look like it's landing anytime soon
• until then, I am looking at tkestack/gpu-manager and NTHU-LSALAB/KubeShare as possible candidates

[naxty]

@creatorrr did you find a solution yet? Can you share anything? I'm having a deep interest in this feature as well 👍