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Add gpu sharing user guide #902

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165 changes: 165 additions & 0 deletions docs/user-guide/how_to_use_gpu_sharing.md
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# GPU Sharing User guide

## Environment setup

### Install volcano


#### 1. Install from source

Refer to [Install Guide](../../installer/README.md) to install volcano.

After installed, update the scheduler configuration:

```shell script
kubectl edit cm -n volcano-system volcano-scheduler-configmap
```

```yaml
kind: ConfigMap
apiVersion: v1
metadata:
name: volcano-scheduler-configmap
namespace: volcano-system
data:
volcano-scheduler.conf: |
actions: "enqueue, allocate, backfill"
tiers:
- plugins:
- name: priority
- name: gang
- name: conformance
- plugins:
- name: drf
- name: predicates
arguments:
predicate.GPUSharingEnable: true # enable gpu sharing
- name: proportion
- name: nodeorder
- name: binpack
```

#### 2. Install from release package.

Same as above, after installed, update the scheduler configuration in `volcano-scheduler-configmap` configmap.

### Install Volcano device plugin

Please refer to [volcano device plugin](https://github.com/volcano-sh/devices/blob/master/README.md#quick-start)

### Verify environment is ready

Check the node status, it is ok if `volcano.sh/gpu-memory` and `volcano.sh/gpu-number` are included in the allocatable resources.

```shell script
$ kubectl get node {node name} -oyaml
...
status:
addresses:
- address: 172.17.0.3
type: InternalIP
- address: volcano-control-plane
type: Hostname
allocatable:
cpu: "4"
ephemeral-storage: 123722704Ki
hugepages-1Gi: "0"
hugepages-2Mi: "0"
memory: 8174332Ki
pods: "110"
volcano.sh/gpu-memory: "89424"
volcano.sh/gpu-number: "8" # GPU resource
capacity:
cpu: "4"
ephemeral-storage: 123722704Ki
hugepages-1Gi: "0"
hugepages-2Mi: "0"
memory: 8174332Ki
pods: "110"
volcano.sh/gpu-memory: "89424"
volcano.sh/gpu-number: "8" # GPU resource
```

### Running GPU Sharing Jobs

NVIDIA GPUs can now be shared via container level resource requirements using the resource name `volcano.sh/gpu-memory`:
```shell script
$ cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
name: gpu-pod1
spec:
containers:
- name: cuda-container
image: nvidia/cuda:9.0-devel
command: ["sleep"]
args: ["100000"]
resources:
limits:
volcano.sh/gpu-memory: 1024 # requesting 1024MB GPU memory
EOF

$ cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
name: gpu-pod2
spec:
containers:
- name: cuda-container
image: nvidia/cuda:9.0-devel
command: ["sleep"]
args: ["100000"]
resources:
limits:
volcano.sh/gpu-memory: 1024 # requesting 1024MB GPU memory
EOF
```

If only the above pods are claiming gpu resource in a cluster, you can see the pods sharing one gpu card:

```shell script
$ kubectl exec -ti gpu-pod1 env
...
VOLCANO_GPU_TOTAL=11178
VOLCANO_GPU_ALLOCATED=1024
NVIDIA_VISIBLE_DEVICES=0
...

$ kubectl exec -ti gpu-pod1 env
...
VOLCANO_GPU_TOTAL=11178
VOLCANO_GPU_ALLOCATED=1024
NVIDIA_VISIBLE_DEVICES=0
...
```

### Understanding how GPU sharing works

The GPU sharing workflow is depicted as below:

![gpu_sharing](../images/gpu-share-flow.png)

1. create a pod with `volcano.sh/gpu-memory` resource request,

2. volcano scheduler predicates and allocate gpu resource for the pod. Adding the below annotation

```yaml
annotations:
volcano.sh/gpu-index: “0”
volcano.sh/predicate-time: “1593764466550835304”
```

3. kubelet watches the pod bound to itself, and call allocate API to set env before running the container.

```yaml
env:
NVIDIA_VISIBLE_DEVICES: “0” # GPU card index
VOLCANO_GPU_ALLOCATED: “1024” # GPU allocated
VOLCANO_GPU_TOTAL: “11178” # GPU memory of the card
```