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Add user guide on how to deploy to EKS #2510

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Add user guide on how to deploy to EKS
IsaacYangSLA Apr 16, 2024
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1 change: 1 addition & 0 deletions docs/real_world_fl.rst
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Expand Up @@ -30,5 +30,6 @@ to see the capabilities of the system and how it can be operated.
real_world_fl/job
real_world_fl/workspace
real_world_fl/cloud_deployment
real_world_fl/kubernetes
real_world_fl/notes_on_large_models
user_guide/security/identity_security
364 changes: 364 additions & 0 deletions docs/real_world_fl/kubernetes.rst
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.. _eks_deployment:

############################################
Amazon Elastic Kubernetes Service Deployment
############################################
In this document, we will describe how to run the entire NVIDIA FLARE inside one Amazon Elastic Kubernetes Service (EKS). For information
how to run NVIDIA FLARE inside microk8s (local kubernetes cluster), please refer to :ref:`_helm_chart`. That document describes how to
provision one NVIDIA FLARE system, configure your microk8s cluter, deploy the servers, the overseer and the clients to that cluster, and
control and submit jobs to that NVIDIA FLARE from admin console.


Start the EKS
=============
We assume that you have one AWS account which allows you to start one EKS. We also assume you have eksctl, aws and kubectl installed in your local machine.
Note that the versions of those CLI may affect the operations. We suggest keep them updated.

The first thing is to start the EKS with eksctl. The following is a sample yaml file, ``cluter.yaml``, to create EKS with one command.
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.. code-block:: yaml

apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig

metadata:
name: nvflare-cluster
region: us-west-2
tags:
project: nvflare

nodeGroups:
- name: worker-node
instanceType: t3.large
desiredCapacity: 2

.. code-block:: shell

eksctl create cluster -f cluster.yaml

After this, you will have one cluster with two `t3.large` EC2 nodes.


Provision
=========

With NVIDIA FLARE installed in your local machine, you can create one set of startup kits easily with ``nvflare provision``. If there is a project.yml file
in your current working directory, ``nvflare provision`` will create a workspace directory. If that project.yml file does not exist, ``nvflare provsiion`` will
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create a sample project.yml for you. For simplicity, we suggest you remove/rename any existing project.yml and workspace directory. Then provision the
set of startup kits from scratch. When selecting the sampel project.yml during provisioning time, select non-HA one as most clusters support HA easily.

After provisioning, you will have a workspace/example_project/prod_00 folder, which includes server, site-1, site-2 and admin@nvidia.com folders.
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Persistent Volume
=================

EKS provides several ways to create persistent volumes. Before you can use create the volume,
you will need to create one OIDC provider, add one service account and attach a pollicy to two roles, the node instance group and that service account.

.. code-block:: shell

eksctl utils associate-iam-oidc-provider --region=us-west-2 --cluster=nvflare-cluster --approve

.. code-block:: shell

eksctl create iamserviceaccount \
--region us-west-2 \
--name ebs-csi-controller-sa \
--namespace kube-system \
--cluster nvflare-cluster \
--attach-policy-arn arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy \
--approve \
--role-only \
--role-name AmazonEKS_EBS_CSI_DriverRole


.. code-block:: shell

eksctl create addon --name aws-ebs-csi-driver \
--cluster nvflare-cluster \
--service-account-role-arn arn:aws:iam::$(aws sts get-caller-identity --query Account --output text):role/AmazonEKS_EBS_CSI_DriverRole \
--force

The following is the policy json file that you have to attach to the roles.

.. code-block:: json

{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "Poicly4EKS",
"Effect": "Allow",
"Action": [
"ec2:DetachVolume",
"ec2:AttachVolume",
"ec2:DeleteVolume",
"ec2:DescribeInstances",
"ec2:DescribeTags",
"ec2:DeleteTags",
"ec2:CreateTags",
"ec2:DescribeVolumes",
"ec2:CreateVolume"
],
"Resource": [
"*"
]
}
]
}

The following yaml file will utilize EKS gp2 StorageClass to allocate 5GiByte space. You
can run ``kubectl apply -f volume.yaml`` to make the volume available.

.. code-block:: yaml

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: nvflare-pv-claim
labels:
app: nvflare
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 5Gi
storageClassName: gp2

After that, your EKS persistent volme should be waiting for the first claim.


Start Helper Pod
================

Now you will need to copy your startup kits to your EKS cluster. Those startup kits will copied into the volume you just created.
In order to access the volume, we deploy a helper pod which mounts that persistent volume and use kubectl cp to copy files from your
local machine to the cluster.

The following is the helper pod yaml file.

.. code-block:: yaml

apiVersion: apps/v1
kind: Deployment
metadata:
labels:
run: bb8
name: bb8
spec:
replicas: 1
selector:
matchLabels:
run: bb8
template:
metadata:
labels:
run: bb8
spec:
containers:
- args:
- sleep
- "50000"
image: busybox
name: bb8
volumeMounts:
- name: nvfl
mountPath: /workspace/nvfl/
volumes:
- name: nvfl
persistentVolumeClaim:
claimName: nvflare-pv-claim


All pods can be deployed with ``kubectl apply -f`` so we just need the following command.

.. code-block:: shell

kubectl apply -f bb8.yaml

Your helper pod should be up and running very soon. Now copy the startup kits to the cluster with
.. code-block:: shell
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kubectl cp workspace/example_project/prod_00/server <helper-pod>:/workspace/nvfl/

And the same for site-1, site-2, admin@nvidia.com.

This will make the entire startup kits available at the nvflare-pv-claim of the cluster so that NVIDIA FLARE system
can mount that nvflare-pv-claim and access the startup kits.
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Start Server Pod
================

The NVIDIA FLARE server consists of two portions for Kubernetes clusters. As you might know,
the server needs computation to handle model updates, aggregations and other operations. It also needs to provide a service for clients and admins
to connect. Therefore, the followings are two separate yaml files that work together to create the NVIDIA FLARE server in EKS.

.. code-block:: yaml

apiVersion: apps/v1
kind: Deployment
metadata:
labels:
run: nvflare
name: nvflare
spec:
replicas: 1
selector:
matchLabels:
run: nvflare
template:
metadata:
labels:
run: nvflare
spec:
containers:
- args:
- -u
- -m
- nvflare.private.fed.app.server.server_train
- -m
- /workspace/nvfl/server
- -s
- fed_server.json
- --set
- secure_train=true
- config_folder=config
- org=nvidia
command:
- /usr/local/bin/python3
image: nvflare/nvflare:2.4.0
imagePullPolicy: Always
name: nvflare
volumeMounts:
- name: nvfl
mountPath: /workspace/nvfl/
volumes:
- name: nvfl
persistentVolumeClaim:
claimName: nvflare-pv-claim


.. code-block:: yaml

apiVersion: v1
kind: Service
metadata:
labels:
run: server
name: server
spec:
ports:
- port: 8002
protocol: TCP
targetPort: 8002
name: flport
- port: 8003
protocol: TCP
targetPort: 8003
name: adminport
selector:
run: nvflare


Note that the pod will use nvflare/nvflare:2.4.0 container image. If you build your own for additional dependencies, you will need to update
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the yaml file.

Start Client Pods
=================

For the client pods, we only need one yaml file for eacch client. The following is the deployment yaml file for site-1.

.. code-block:: yaml

apiVersion: apps/v1
kind: Deployment
metadata:
labels:
run: site1
name: site1
spec:
replicas: 1
selector:
matchLabels:
run: site1
template:
metadata:
labels:
run: site1
spec:
containers:
- args:
- -u
- -m
- nvflare.private.fed.app.client.client_train
- -m
- /workspace/nvfl/site-1
- -s
- fed_client.json
- --set
- secure_train=true
- uid=site-1
- config_folder=config
- org=nvidia
command:
- /usr/local/bin/python3
image: nvflare/nvflare:2.4.0
imagePullPolicy: Always
name: site1
volumeMounts:
- name: nvfl
mountPath: /workspace/nvfl/
volumes:
- name: nvfl
persistentVolumeClaim:
claimName: nvflare-pv-claim

Once the client is up and running, you can check the server log with ``kubectl logs`` and the log should show the clients registered.

Start and Connect to Admin Pods
===============================

We can also run the admin console inside the EKS cluster to submit jobs to the NVIDIA FLARE running in the EKS cluster. Start the admin pod
with the following yaml file.

.. code-block:: yaml

apiVersion: apps/v1
kind: Deployment
metadata:
labels:
run: admin
name: admin
spec:
replicas: 1
selector:
matchLabels:
run: admin
template:
metadata:
labels:
run: admin
spec:
containers:
- args:
- "50000"
command:
- /usr/bin/sleep
image: nvflare/nvflare:2.4.0
imagePullPolicy: Always
name: admin
volumeMounts:
- name: nvfl
mountPath: /workspace/nvfl/
volumes:
- name: nvfl
persistentVolumeClaim:
claimName: nvflare-pv-claim

Once the admin pod is running, you can enter the pod with ``kubectl exec`` , cd to ``/workspace/nvfl/admin@nvidia.com/startup`` and run ``fl_admin.sh``.


Note that you need to copy the job from your local machine to the EKS cluster so that the ``transfer`` directory of admin@nvidia.com contains the jobs
you would like to run in that EKS cluster.

6 changes: 6 additions & 0 deletions docs/real_world_fl/overview.rst
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Expand Up @@ -159,6 +159,12 @@ See how to deploy to Azure and AWS clouds can be found in :ref:`cloud_deployment

Deploy to Google Cloud will be made available in a future release.

Kubernetes Deployment
=====================
As mentioned above, you can run NVIDIA FLARE in the public cloud. If you prefer to deploy NVIDIA FLARE in Amazon Elastic Kubernetes Service (EKS),
you can find the deployment guide in :ref:`eks_deployment`.


Starting Federated Learning Servers
=============================================
The FL Server will coordinate the federated learning training and be the main hub all clients and admin
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