Merge branch 'update-readme' into 'main'

Update readme with new config file format and cli

See merge request nvidia/cloud-native/vgpu-device-manager!10

README.md

@@ -2,54 +2,173 @@
 
 **Note:** This project is under active development and not yet designed for production use. Use at your own risk.
 
-The `NVIDIA vGPU Device Manager` manages vGPU devices on a GPU node in a Kubernetes cluster.
-It defines a schema for declaratively specifying the list of vGPU types one would like to create on the node.
-The vGPU Device Manager parses this schema and applies the desired config by creating vGPU devices following steps outlined in the
-[NVIDIA vGPU User Guide](https://docs.nvidia.com/grid/latest/grid-vgpu-user-guide/index.html#creating-vgpu-device-red-hat-el-kvm).
+NVIDIA Virtual GPU (vGPU) enables multiple virtual machines (VMs) to have simultaneous, direct access to a single physical GPU, using the same NVIDIA graphics drivers that are deployed on non-virtualized operating systems.
+By doing this, NVIDIA vGPU provides VMs with unparalleled graphics performance, compute performance, and application compatibility, together with the cost-effectiveness and scalability brought about by sharing a GPU among multiple workloads.
+Under the control of the NVIDIA Virtual GPU Manager running under the hypervisor, NVIDIA physical GPUs are capable of supporting multiple virtual GPU devices (vGPUs) that can be assigned directly to guest VMs.
+To learn more, refer to the [NVIDIA vGPU Software Documentation](https://docs.nvidia.com/grid/).
 
-As an example, consider the following configuration for a node with NVIDIA Tesla T4 GPUs.
+The `NVIDIA vGPU Device Manager` is a tool designed for system administrators to make working with vGPU devices easier.
+
+It allows administrators to ***declaratively*** define a set of possible vGPU device
+configurations they would like applied to all GPUs on a node. At runtime, they
+then point `nvidia-vgpu-dm` at one of these configurations, and
+`nvidia-vgpu-dm` takes care of applying it. In this way, the same
+configuration file can be spread across all nodes in a cluster, and a runtime
+flag (or environment variable) can be used to decide which of these
+configurations to actually apply to a node at any given time.
+
+As an example, consider the following configuration for a node with two NVIDIA Tesla T4 GPUs.
 
 ```
 version: v1
 vgpu-configs:
-  default:
-    - "T4-8Q"
-
   # NVIDIA Tesla T4, Q-Series
-  T4-16Q:
-    - "T4-16Q"
-  T4-8Q:
-    - "T4-8Q"
-  T4-4Q:
-    - "T4-4Q"
-  T4-2Q:
-    - "T4-2Q"
   T4-1Q:
-    - "T4-1Q"
+    - devices: all
+      vgpu-devices:
+        "T4-1Q": 16
+
+  T4-2Q:
+    - devices: all
+      vgpu-devices:
+        "T4-2Q": 8
+
+  T4-4Q:
+    - devices: all
+      vgpu-devices:
+        "T4-4Q": 4
+
+  T4-8Q:
+    - devices: all
+      vgpu-devices:
+        "T4-8Q": 2
+
+  T4-16Q:
+    - devices: all
+      vgpu-devices:
+        "T4-16Q": 1
 
   # Custom configurations
   T4-small:
-    - "T4-1Q"
-    - "T4-2Q"
+    - devices: [0]
+      vgpu-devices:
+        "T4-1Q": 16
+    - devices: [1]
+      vgpu-devices:
+        "T4-2Q": 8
+
   T4-medium:
-    - "T4-4Q"
-    - "T4-8Q"
+    - devices: [0]
+      vgpu-devices:
+        "T4-4Q": 4
+    - devices: [1]
+      vgpu-devices:
+        "T4-8Q": 2
+
   T4-large:
-    - "T4-8Q"
-    - "T4-16Q"
+    - devices: [0]
+      vgpu-devices:
+        "T4-8Q": 2
+    - devices: [1]
+      vgpu-devices:
+        "T4-16Q": 1
 ```
 
-Each of the sections under `vgpu-configs` is user-defined, with custom labels used to refer to them. For example, the `T4-8Q` label refers to the vGPU configuration that creates vGPU devices of type `T4-8Q` on all T4 GPUs on the node. Likewise, the `T4-1Q` label refers to the vGPU configuration that creates vGPU devices of type `T4-1Q` on all T4 GPUs on the node.
+Each of the sections under `vgpu-configs` is user-defined, with custom labels used to refer to them. For example, the `T4-8Q` label refers to the vGPU configuration that creates 2 vGPU devices of type `T4-8Q` on all T4 GPUs on the node. Likewise, the `T4-1Q` label refers to the vGPU configuration that creates 16 vGPU devices of type `T4-1Q` on all T4 GPUs on the node. Finally, the `T4-small` label defines a completely custom configuration which creates 16 `T4-1Q` vGPU devices on the first GPU and 8 `T4-2Q` vGPU devices on the second GPU.
 
-More than one vGPU type can be associated with a configuration. For example, the `T4-small` label specifies both the `T4-1Q` and `T4-2Q` vGPU types. If the node has multiple T4 cards, then vGPU devices of both types will be created on the node. More specifically, the vGPU Device Manager will select the vGPU types in a round robin fashion as it creates devices. vGPU devices of type `T4-1Q` get created on the first card, vGPU devices of type `T4-2Q` get created on the second card, vGPU devices of type `T4-1Q` get created on the third card, etc.
+Using the `nvidia-vgpu-dm` tool, the following commands can be run to apply each of these configs in turn:
+```
+$ nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-1Q
+$ nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-2Q
+$ nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-4Q
+$ nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-8Q
+$ nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-16Q
+$ nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-small
+$ nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-medium
+$ nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-large
+```
 
-## Prerequisites
+The currently applied configuration can then be asserted with:
+```
+$ nvidia-vgpu-dm assert -f examples/config-t4.yaml -c T4-large
+INFO[0000] Selected vGPU device configuration is currently applied
 
-- [NVIDIA vGPU Manager](https://docs.nvidia.com/grid/latest/grid-vgpu-user-guide/index.html#installing-configuring-grid-vgpu) is installed on the system.
+$ echo $?
+0
+
+$ nvidia-vgpu-dm assert -f examples/config-t4.yaml -c T4-16Q
+FATA[0000] Assertion failure: selected configuration not currently applied
+
+$ echo $?
+1
+```
+
+## Build `nvidia-vgpu-dm`
+
+```
+git clone https://gitlab.com/nvidia/cloud-native/vgpu-device-manager.git
+cd vgpu-device-manager
+make cmd-nvidia-vgpu-dm
+```
+
+This will generate a binary called `nvidia-vgpu-dm` in your current directory.
 
 ## Usage
 
-**Note:** Currently this project can only be deployed on Kubernetes, and the only supported way is through the [NVIDIA GPU Operator](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/overview.html). It is not meant to be run as a standalone component and no CLI utility exists. The instructions below are for deploying the vGPU Device Manager as a standalone DaemonSet, for development purposes.
+#### Prerequisites
+
+- [NVIDIA vGPU Manager](https://docs.nvidia.com/grid/latest/grid-vgpu-user-guide/index.html#installing-configuring-grid-vgpu) is installed on the system.
+
+#### Apply a specific vGPU device config from a configuration file
+```
+nvidia-vgpu-dm apply -f examples/config-t4.yaml -c T4-1Q
+```
+
+#### Apply a specific vGPU device config with debug output
+```
+nvidia-vgpu-dm -d apply -f examplpes/config-t4.yaml -c T4-1Q
+```
+
+#### Apply a one-off vGPU device configuration without a configuration file
+```
+cat <<EOF | nvidia-vgpu-dm apply -f -
+version: v1
+vgpu-configs:
+  T4-1Q:
+  - devices: all
+    vgpu-devices:
+      "T4-1Q": 16
+EOF
+```
+
+#### Assert a specific vGPU device configuration is currently applied
+```
+nvidia-vgpu-dm assert -f examples/config.yaml -c T4-1Q
+```
+
+#### Assert a one-off vGPU device configuration without a configuration file
+```
+cat <<EOF | nvidia-vgpu-dm assert -f -
+version: v1
+vgpu-configs:
+  T4-1Q:
+  - devices: all
+    vgpu-devices:
+      "T4-1Q": 16
+EOF
+```
+
+#### Assert only that the configuration file is valid and the selected config is present in it
+```
+nvidia-vgpu-dm assert -f exaples/config.yaml -c T4-1Q --valid-config
+```
+
+## Kubernetes Deployment
+
+The [NVIDIA vGPU Device Manager container](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/cloud-native/containers/vgpu-device-manager) manages vGPU devices on a GPU node in a Kubernetes cluster.
+The containerized deployment is only supported through the [NVIDIA GPU Operator](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/overview.html).
+It is not meant to be run as a standalone component.
+The instructions below are for deploying the vGPU Device Manager as a standalone DaemonSet, for development purposes.
 
 First, create a vGPU devices configuration file. The example file in `examples/` can be used as a starting point:
 

examples/config-t4.yaml

@@ -0,0 +1,52 @@
+version: v1
+vgpu-configs:
+  # NVIDIA Tesla T4, Q-Series
+  T4-1Q:
+    - devices: all
+      vgpu-devices:
+        "T4-1Q": 16
+
+  T4-2Q:
+    - devices: all
+      vgpu-devices:
+        "T4-2Q": 8
+
+  T4-4Q:
+    - devices: all
+      vgpu-devices:
+        "T4-4Q": 4
+
+  T4-8Q:
+    - devices: all
+      vgpu-devices:
+        "T4-8Q": 2
+
+  T4-16Q:
+    - devices: all
+      vgpu-devices:
+        "T4-16Q": 1
+
+  # Custom configurations
+  T4-small:
+    - devices: [0]
+      vgpu-devices:
+        "T4-1Q": 16
+    - devices: [1]
+      vgpu-devices:
+        "T4-2Q": 8
+
+  T4-medium:
+    - devices: [0]
+      vgpu-devices:
+        "T4-4Q": 4
+    - devices: [1]
+      vgpu-devices:
+        "T4-8Q": 2
+
+  T4-large:
+    - devices: [0]
+      vgpu-devices:
+        "T4-8Q": 2
+    - devices: [1]
+      vgpu-devices:
+        "T4-16Q": 1