Update FPGA examples (#1685)

* Update FPGA Operator usage instructions

Signed-off-by: Elias Koromilas <elias.koromilas@gmail.com>

* Update FPGA example experiments

Signed-off-by: Elias Koromilas <elias.koromilas@gmail.com>

* Add FPGA experiment reviewers

Signed-off-by: Elias Koromilas <elias.koromilas@gmail.com>

* Update examples/v1beta1/fpga/OWNERS

Co-authored-by: Andrey Velichkevich <andrey.velichkevich@gmail.com>

Co-authored-by: Andrey Velichkevich <andrey.velichkevich@gmail.com>

examples/v1beta1/fpga/OWNERS

@@ -0,0 +1,3 @@
+approvers:
+  - eliaskoromilas
+  - jstamel

examples/v1beta1/fpga/README.md

@@ -7,55 +7,43 @@ If you want to read more about provisioning FPGA resources and deploying
 accelerated applications (e.g. Kubeflow Pipelines) on any Kubernetes cluster,
 visit the [InAccel](https://docs.inaccel.com) documentation.
 
-## EKS*-optimized AMI with FPGA support
+## Simplifying FPGA management in EKS* (Elastic Kubernetes Serice)
 
 **For development and testing purposes you can still [deploy Kubeflow Katib
 using Minikube](https://kubeflow.org/docs/started/workstation/minikube-linux) in
 a single AMI instance. In production environments, Amazon's managed Kubernetes
 service ([EKS](https://aws.amazon.com/eks)) is recommended.*
 
-The InAccel EKS-optimized FPGA AMI is built on top of the standard [Amazon
-EKS-optimized Linux AMI](https://aws.amazon.com/marketplace/pp/B07GRMYQR5), and
-is configured to serve as an optional image for Amazon EKS worker nodes to
-support FPGA based workloads.
+The InAccel FPGA Operator allows administrators of Kubernetes clusters to manage
+FPGA nodes just like CPU nodes in the cluster. Instead of provisioning a special
+OS image for FPGA nodes, administrators can rely on a standard OS image for both
+CPU and FPGA nodes and then rely on the FPGA Operator to provision the required
+software components for FPGAs.
 
-In addition to the standard Amazon EKS-optimized AMI configuration, the FPGA AMI
-includes the following:
-
-* Xilinx FPGA drivers
-* InAccel runtime (as the default runtime)
-
-The AMI IDs for the latest InAccel EKS-optimized FPGA AMI are shown in the
-following table.
-
-| AWS Region                          | Kubernetes version 1.17.7 | Kubernetes version 1.16.12 |
-| ----------------------------------- | ------------------------- | -------------------------- |
-| US East (N. Virginia) (`us-east-1`) | `ami-0c4e0b85781a9dde3`   | `ami-0519d9b242546530a`    |
-
-> **Note**: The EKS-optimized FPGA AMI also supports non-FPGA instance types.
+Note that the FPGA Operator is specifically useful for scenarios where the
+Kubernetes cluster needs to scale quickly - for example provisioning additional
+FPGA nodes on the cloud and managing the lifecycle of the underlying software
+components.
 
 ## Enabling FPGA based workloads
 
 The following section describes how to run a workload on an FPGA based instance
-with the InAccel EKS-optimized FPGA AMI.
+with the InAccel FPGA Operator.
 
 After your FPGA worker nodes join your cluster, you must apply the [InAccel FPGA
-Operator](https://hub.helm.sh/charts/inaccel/fpga-operator) for Kubernetes, as a
-Helm app on your cluster, with the following command.
+Operator](https://artifacthub.io/packages/helm/inaccel/fpga-operator) for
+Kubernetes, as a Helm app on your cluster, with the following command.
 
 ```sh
 helm repo add inaccel https://setup.inaccel.com/helm
 
-helm install inaccel inaccel/fpga-operator --set license=...
+helm install inaccel inaccel/fpga-operator
 ```
 
-Get your free Community Edition [license](https://inaccel.com/license), if it's
-the first time that you use InAccel toolset.
-
 You can verify that your nodes have available FPGAs with the following command:
 
 ```sh
-kubectl get nodes -o custom-columns=NAME:metadata.name,FPGAS:.status.capacity.xilinx/aws-vu9p-f1
+kubectl get nodes -o custom-columns=NAME:metadata.name,FPGAS:.status.capacity.xilinx/aws-vu9p-f1,SHELL:.metadata.labels.xilinx/aws-vu9p-f1
 ```
 
 ## Experiment

examples/v1beta1/fpga/xgboost-example.yaml

@@ -49,6 +49,12 @@ spec:
       kind: Job
       spec:
         template:
+          metadata:
+            labels:
+              inaccel/fpga: enabled
+            annotations:
+              inaccel/cli: |
+                bitstream install --mode others https://store.inaccel.com/artifactory/bitstreams/xilinx/aws-vu9p-f1/shell-v04261818_201920.2/aws/com/inaccel/xgboost/0.1/2exact
           spec:
             containers:
               - name: training-container
@@ -60,7 +66,6 @@ spec:
                   - "--name=SVHN"
                   - "--test-size=0.35"
                   - "--tree-method=fpga_exact"
-                  - "--bitstream=https://store.inaccel.com/artifactory/bitstreams/xilinx/aws-vu9p-f1/dynamic_5.0/com/inaccel/xgboost/0.1/2exact"
                   - "--max-depth=10"
                   - "--alpha=${trialParameters.alpha}"
                   - "--eta=${trialParameters.eta}"