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https://github.com/project-codeflare/instaslice
InstaSlice facilitates the use of Dynamic Resource Allocation (DRA) on Kubernetes clusters for GPU sharing
https://github.com/project-codeflare/instaslice
Last synced: about 1 month ago
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InstaSlice facilitates the use of Dynamic Resource Allocation (DRA) on Kubernetes clusters for GPU sharing
- Host: GitHub
- URL: https://github.com/project-codeflare/instaslice
- Owner: project-codeflare
- Created: 2024-03-12T13:09:35.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-02T17:06:57.000Z (5 months ago)
- Last Synced: 2024-08-02T19:47:30.857Z (5 months ago)
- Language: Go
- Homepage:
- Size: 137 KB
- Stars: 17
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Note - Kubecon EU 2024 code (DRA code) is now available in the legacy branch
# InstaSlice
Experimental InstaSlice works with GPU operator to create mig slices on demand.
## Getting Started
### Prerequisites
- [Go](https://go.dev/doc/install) v1.22.0+
- [Docker](https://docs.docker.com/get-docker/) v17.03+
- [Docker buildx plugin](https://github.com/docker/buildx) for building cross-platform images.
- [kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl) v1.11.3+.
- Access to a [KinD](https://kind.sigs.k8s.io/docs/user/quick-start/) cluster.
### Install KinD cluster with GPU operator
- Make sure the GPUs on the host have MIG enabled
```sh
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA A100-PCIE-40GB Off | 00000000:0E:00.0 Off | On |
| N/A 36C P0 33W / 250W | 0MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA A100-PCIE-40GB Off | 00000000:0F:00.0 Off | On |
| N/A 40C P0 32W / 250W | 0MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| MIG devices: |
+------------------+----------------------------------+-----------+-----------------------+
| GPU GI CI MIG | Memory-Usage | Vol| Shared |
| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |
| | | ECC| |
|==================+==================================+===========+=======================|
| No MIG devices found |
+-----------------------------------------------------------------------------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
```
- Run the below script
```sh
sh ./deploy/setup.sh
```
NOTE: Please check if all the pods in GPU operator are completed or Running before moving to the next step.
```sh
(base) openstack@netsres62:~/asmalvan/instaslice2$ kubectl get pods -n gpu-operator
NAME READY STATUS RESTARTS AGE
gpu-feature-discovery-578q8 1/1 Running 0 102s
gpu-operator-1714053627-node-feature-discovery-gc-9b857c99phlnn 1/1 Running 0 7m21s
gpu-operator-1714053627-node-feature-discovery-master-6df78zgsz 1/1 Running 0 7m21s
gpu-operator-1714053627-node-feature-discovery-worker-47tpx 1/1 Running 0 7m19s
gpu-operator-54b8bfbfd8-rmzbd 1/1 Running 0 7m21s
nvidia-container-toolkit-daemonset-wkc5h 1/1 Running 0 6m21s
nvidia-cuda-validator-cn8lg 0/1 Completed 0 88s
nvidia-dcgm-exporter-h75xg 1/1 Running 0 102s
nvidia-device-plugin-daemonset-452dk 1/1 Running 0 101s
nvidia-mig-manager-htt7z 1/1 Running 0 2m21s
nvidia-operator-validator-kh6jf 1/1 Running 0 102s
```
- After all the pods are Running/Completed, run nvidia-smi on the host and check if MIG slices appear on the all the GPUs of the host.
```sh
(base) openstack@netsres62:~/asmalvan/instaslice2$ nvidia-smi
Thu Apr 25 10:08:24 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA A100-PCIE-40GB Off | 00000000:0E:00.0 Off | On |
| N/A 45C P0 71W / 250W | 87MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA A100-PCIE-40GB Off | 00000000:0F:00.0 Off | On |
| N/A 49C P0 69W / 250W | 87MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| MIG devices: |
+------------------+----------------------------------+-----------+-----------------------+
| GPU GI CI MIG | Memory-Usage | Vol| Shared |
| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |
| | | ECC| |
|==================+==================================+===========+=======================|
| 0 2 0 0 | 37MiB / 19968MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 0 3 0 1 | 25MiB / 9856MiB | 28 0 | 2 0 1 0 0 |
| | 0MiB / 16383MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 0 9 0 2 | 12MiB / 4864MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 0 10 0 3 | 12MiB / 4864MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 2 0 0 | 37MiB / 19968MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 3 0 1 | 25MiB / 9856MiB | 28 0 | 2 0 1 0 0 |
| | 0MiB / 16383MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 9 0 2 | 12MiB / 4864MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 10 0 3 | 12MiB / 4864MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
(base) openstack@netsres62:~/asmalvan/instaslice2$
```
- Delete mig slices using the commmand
```sh
sudo nvidia-smi mig -dci && sudo nvidia-smi mig -dgi
uccessfully destroyed compute instance ID 0 from GPU 0 GPU instance ID 9
Successfully destroyed compute instance ID 0 from GPU 0 GPU instance ID 10
Successfully destroyed compute instance ID 0 from GPU 0 GPU instance ID 3
Successfully destroyed compute instance ID 0 from GPU 0 GPU instance ID 2
Successfully destroyed compute instance ID 0 from GPU 1 GPU instance ID 9
Successfully destroyed compute instance ID 0 from GPU 1 GPU instance ID 10
Successfully destroyed compute instance ID 0 from GPU 1 GPU instance ID 3
Successfully destroyed compute instance ID 0 from GPU 1 GPU instance ID 2
Successfully destroyed GPU instance ID 9 from GPU 0
Successfully destroyed GPU instance ID 10 from GPU 0
Successfully destroyed GPU instance ID 3 from GPU 0
Successfully destroyed GPU instance ID 2 from GPU 0
Successfully destroyed GPU instance ID 9 from GPU 1
Successfully destroyed GPU instance ID 10 from GPU 1
Successfully destroyed GPU instance ID 3 from GPU 1
Successfully destroyed GPU instance ID 2 from GPU 1
```
- Create placeholder slice to make k8s-device-plugin happy using the command
```sh
sudo nvidia-smi mig -cgi 3g.20gb -C
Successfully created GPU instance ID 2 on GPU 0 using profile MIG 3g.20gb (ID 9)
Successfully created compute instance ID 0 on GPU 0 GPU instance ID 2 using profile MIG 3g.20gb (ID 2)
Successfully created GPU instance ID 2 on GPU 1 using profile MIG 3g.20gb (ID 9)
Successfully created compute instance ID 0 on GPU 1 GPU instance ID 2 using profile MIG 3g.20gb (ID 2)
```
- Run the below command to patch device plugin with configmap created by the setup script. For OpenShift replace clusterpolicies.nvidia.com/cluster-policy to clusterpolicies.nvidia.com/gpu-cluster-policy and namespace to nvidia-gpu-operator
```sh
(base) openstack@netsres62:~/asmalvan/instaslice2$ kubectl patch clusterpolicies.nvidia.com/cluster-policy -n gpu-operator --type merge -p '{"spec": {"devicePlugin": {"config": {"name": "test"}}}}'
```
You are now all set to dynamically create slices on the cluster using InstaSlice.
### Running the controller
- Refer to section `To Deploy on the cluster`
### Submitting the workload
- Submit a sample workload using the command
```sh
kubectl apply -f ./samples/test-pod.yaml
pod/cuda-vectoradd-5 created
```
- check the status of the workload using commands
```sh
kubectl get pods
NAME READY STATUS RESTARTS AGE
cuda-vectoradd-5 1/1 Running 0 15s
kubectl logs cuda-vectoradd-5
GPU 0: NVIDIA A100-PCIE-40GB (UUID: GPU-31cfe05c-ed13-cd17-d7aa-c63db5108c24)
MIG 1g.5gb Device 0: (UUID: MIG-c5720b34-e550-5278-90e6-d99a979aafd1)
[Vector addition of 50000 elements]
Copy input data from the host memory to the CUDA device
CUDA kernel launch with 196 blocks of 256 threads
Copy output data from the CUDA device to the host memory
Test PASSED
Done
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA A100-PCIE-40GB Off | 00000000:0E:00.0 Off | On |
| N/A 52C P0 75W / 250W | 50MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA A100-PCIE-40GB Off | 00000000:0F:00.0 Off | On |
| N/A 60C P0 75W / 250W | 37MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| MIG devices: |
+------------------+----------------------------------+-----------+-----------------------+
| GPU GI CI MIG | Memory-Usage | Vol| Shared |
| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |
| | | ECC| |
|==================+==================================+===========+=======================|
| 0 2 0 0 | 37MiB / 19968MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 0 10 0 1 | 12MiB / 4864MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 2 0 0 | 37MiB / 19968MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
```
### Deleting the workload
- Delete the pod and see the newly created MIG slice deleted
```sh
kubectl delete pod cuda-vectoradd-5
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA A100-PCIE-40GB Off | 00000000:0E:00.0 Off | On |
| N/A 53C P0 75W / 250W | 37MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA A100-PCIE-40GB Off | 00000000:0F:00.0 Off | On |
| N/A 60C P0 75W / 250W | 37MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| MIG devices: |
+------------------+----------------------------------+-----------+-----------------------+
| GPU GI CI MIG | Memory-Usage | Vol| Shared |
| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |
| | | ECC| |
|==================+==================================+===========+=======================|
| 0 2 0 0 | 37MiB / 19968MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 2 0 0 | 37MiB / 19968MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
```
### To Deploy on the cluster
**All in one command**
make docker-build && make docker-push && make deploy
Cross-platform or multi-arch images can be built and pushed using
`make docker-buildx`. When using Docker as your container tool, make
sure to create a builder instance. Refer to
[Multi-platform images](https://docs.docker.com/build/building/multi-platform/)
for documentation on building mutli-platform images with Docker.
You can change the destination platform(s) by
setting `PLATFORMS`, e.g.
```sh
PLATFORMS=linux/arm64,linux/amd64 make docker-buildx
```
**Build and push your image to the location specified by `IMG`:**
```sh
make docker-build docker-push IMG=/instaslice:tag
```
**NOTE:** This image ought to be published in the personal registry you specified.
And it is required to have access to pull the image from the working environment.
Make sure you have the proper permission to the registry if the above commands don’t work.
**Install the CRDs into the cluster:**
```sh
make install
```
**Deploy the Manager to the cluster with the image specified by `IMG`:**
```sh
make deploy IMG=/instaslice:tag
```
> **NOTE**: If you encounter RBAC errors, you may need to grant yourself cluster-admin
privileges or be logged in as admin.
**Create instances of your solution**
You can apply the samples (examples) from the config/sample:
```sh
kubectl apply -k config/samples/
```
>**NOTE**: Ensure that the samples has default values to test it out.
### To Uninstall
**Delete the instances (CRs) from the cluster:**
```sh
kubectl delete -k config/samples/
```
**Delete the APIs(CRDs) from the cluster:**
```sh
make uninstall
```
**UnDeploy the controller from the cluster:**
```sh
make undeploy
```
## Project Distribution
Following are the steps to build the installer and distribute this project to users.
1. Build the installer for the image built and published in the registry:
```sh
make build-installer IMG=/instaslice:tag
```
NOTE: The makefile target mentioned above generates an 'install.yaml'
file in the dist directory. This file contains all the resources built
with Kustomize, which are necessary to install this project without
its dependencies.
2. Using the installer
Users can just run kubectl apply -f to install the project, i.e.:
```sh
kubectl apply -f https://raw.githubusercontent.com//instaslice//dist/install.yaml
```
## Contributing
// TODO(user): Add detailed information on how you would like others to contribute to this project
**NOTE:** Run `make help` for more information on all potential `make` targets
More information can be found via the [Kubebuilder Documentation](https://book.kubebuilder.io/introduction.html)
## License
Copyright 2024.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.