Add design document to 'dynamic-mig' feature (#725)

* update documents

Signed-off-by: limengxuan <[email protected]>

Author: archlitchi

docs/develop/dynamic-mig.md

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+# NVIDIA GPU MPS and MIG dynamic slice plugin
+
+## Special Thanks
+
+This feature will not be implemented without the help of @sailorvii.
+
+## Introduction
+
+The NVIDIA GPU build-in sharing method includes: time-slice, MPS and MIG. The context switch for time slice sharing would waste some time, so we chose the MPS and MIG. The GPU MIG profile is variable, the user could acquire the MIG device in the profile definition, but current implementation only defines the dedicated profile before the user requirement. That limits the usage of MIG. We want to develop an automatic slice plugin and create the slice when the user require it.
+For the scheduling method, node-level binpack and spread will be supported. Referring to the binpack plugin, we consider the CPU, Mem, GPU memory and other user-defined resource.
+HAMi is done by using [hami-core](https://github.com/Project-HAMi/HAMi-core), which is a cuda-hacking library. But mig is also widely used across the world. A unified API for dynamic-mig and hami-core is needed.
+
+## Targets
+
+- CPU, Mem, and GPU combined schedule
+- GPU dynamic slice: Hami-core and MIG
+- Support node-level binpack and spread by GPU memory, CPU and Mem
+- A unified vGPU Pool different virtualization technics
+- Tasks can choose to use MIG, use HAMi-core, or use both.
+
+### Config maps
+- hami-scheduler-device-configMap
+This configmap defines the plugin configurations including resourceName, and MIG geometries, and node-level configurations.
+
+```yaml
+apiVersion: v1
+data:
+  device-config.yaml: |
+    nvidia:
+      resourceCountName: nvidia.com/gpu
+      resourceMemoryName: nvidia.com/gpumem
+      resourceCoreName: nvidia.com/gpucores
+      knownMigGeometries:
+      - models: [ "A30" ]
+        allowedGeometries:
+          - 
+            - name: 1g.6gb
+              memory: 6144
+              count: 4
+          - 
+            - name: 2g.12gb
+              memory: 12288
+              count: 2
+          - 
+            - name: 4g.24gb
+              memory: 24576
+              count: 1
+      - models: [ "A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB", "A100-SXM4-40GB" ]
+        allowedGeometries:
+          - 
+            - name: 1g.5gb
+              memory: 5120
+              count: 7
+          - 
+            - name: 2g.10gb
+              memory: 10240
+              count: 3
+            - name: 1g.5gb
+              memory: 5120
+              count: 1
+          - 
+            - name: 3g.20gb
+              memory: 20480
+              count: 2
+          - 
+            - name: 7g.40gb
+              memory: 40960
+              count: 1
+      - models: [ "A100-SXM4-80GB", "A100-80GB-PCIe", "A100-PCIE-80GB"]
+        allowedGeometries:
+          - 
+            - name: 1g.10gb
+              memory: 10240
+              count: 7
+          - 
+            - name: 2g.20gb
+              memory: 20480
+              count: 3
+            - name: 1g.10gb
+              memory: 10240
+              count: 1
+          - 
+            - name: 3g.40gb
+              memory: 40960
+              count: 2
+          - 
+            - name: 7g.79gb
+              memory: 80896
+              count: 1
+      nodeconfig: 
+          - name: nodeA
+            operatingmode: hami-core
+          - name: nodeB
+            operatingmode: mig
+```
+
+## Structure
+
+<img src="./imgs/hami-dynamic-mig-structure.png" width = "400" /> 
+
+## Examples
+
+Dynamic mig is compatable with hami tasks, as the example below: 
+Just Setting `nvidia.com/gpu` and `nvidia.com/gpumem`.
+
+```yaml
+apiVersion: v1
+kind: Pod
+metadata:
+  name: gpu-pod1
+spec:
+  containers:
+    - name: ubuntu-container1
+      image: ubuntu:20.04
+      command: ["bash", "-c", "sleep 86400"]
+      resources:
+        limits:
+          nvidia.com/gpu: 2 # requesting 2 vGPUs
+          nvidia.com/gpumem: 8000 # Each vGPU contains 8000m device memory （Optional,Integer)
+```
+
+A task can decide only to use `mig` or `hami-core` by setting `annotations.nvidia.com/vgpu-mode` to corresponding value, as the example below shows:
+
+```yaml
+apiVersion: v1
+kind: Pod
+metadata:
+  name: gpu-pod1
+  annotations:
+    nvidia.com/vgpu-mode: "mig"
+spec:
+  containers:
+    - name: ubuntu-container1
+      image: ubuntu:20.04
+      command: ["bash", "-c", "sleep 86400"]
+      resources:
+        limits:
+          nvidia.com/gpu: 2 # requesting 2 vGPUs
+          nvidia.com/gpumem: 8000 # Each vGPU contains 8000m device memory （Optional,Integer
+```
+
+## Procedures
+
+The Procedure of a vGPU task which uses dynamic-mig is shown below:
+
+<img src="./imgs/hami-dynamic-mig-procedure.png" width = "800" /> 
+
+Note that after submited a task, deviceshare plugin will iterate over templates defined in configMap `hami-scheduler-device`, and find the first available template to fit. You can always change the content of that configMap, and restart vc-scheduler to customize.
+
+If you submit the example on an empty A100-PCIE-40GB node, then it will select a GPU and chosse MIG template below:
+
+```yaml
+  2g.10gb : 3
+  1g.5gb : 1
+```
+
+Then start the container with 2g.10gb instances * 2
+