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Setting Up AWS Credentials & Bedrock

Prerequisites

  1. AWS Account
    • You can just have the root user for now, but we will set up an IAM user below

AWS Setup

  1. Set up access to AWS Bedrock LLM models 1a. Log into your AWS account 1b. Navigate to Amazon Bedrock and click Get started 1c. Look for Bedrock configurations and click on model access on the bottom left of the sidebar 1d. Make note of which region you are in on the top right corner

    • NOTE: us-east-1, in the setup of this repo, was selected for the availability of Claude-3-Haiku, which is the LLM model configured in this application (server/routes/index.js)
    • NOTE: For reference, the region you set up your secrets will need to be in the same region you requested access to AWS Bedrock 1e. Request for all model access or specifically for Anthropic Claude 3 Haiku

  2. Create an IAM user 2a. Navigate to the IAM Dashboard Service 2b. In the sidebar on the left, under Access management click on Users 2c. Click Create user, name your new user something you will remember (e.g. morpheus), and do NOT click the check box for Provide user access to the AWS Management Console!

    • Under Permissions options select Attach policies directly and search for the permission policy AmazonBedrockFullAccess and select that policy
    • Click Next and then click Create user 2d. Create an Access Key for the IAM user
    • Back on the Users page click on the new user you just created and click Create access key
    • For simplicity select Other -> Next -> Create access key, and now save your Access key and Secret access key somewhere secure

  3. Setup credentials in the repo 3a. In VS Code's file explorer navigate to the nextui directory 3b. Create a new file called .env.local within the nextui directory 3c. Setup your .env.local file to mirror this example:

    .env.local file

ACCESS_KEY_ID =<your access_key_id>
SECRET_ACCESS_KEY =<your secret_access_key>
OPENAI_API_KEY= <your openai api key>

  4. Switching to development mode to work with dummy data 4a. Navigate to nextui -> api -> aws-bedrock -> route.ts 4b. Within the export async function GET(req: Request) function, comment out the AWS Bedrock API call block 4c. Uncomment the dummy data block and save the file

OpenAI Setup

  1. Log In: Visit the OpenAI platform and either create a new account or log in if you already have one: https://openai.com/index/openai-api/
  2. API Key Page: Navigate to the API Keys section found in the Dashboard: https://platform.openai.com/api-keys
  3. Generate API Key: Click on "Create new secret key" to generate a new API key.
  4. Copy and Insert Your Key: Once the API key is generated, make sure to copy it securely. Finally, add the key to the .env.local file you created in Step 3 of AWS Setup.

Setting Up Development Environment

  • Please note that you can pull at most 200 images in 6 hours from docker hub, so don't delete images and the cache when you don't need to. The solution is a set of Docker containers which collect Docker metrics, host the Prometheus time series database, host Grafana for custom dashboard, host the PostgreSQL database and a Next.js web application and API which interacts with the containers.

Docker

Building the Docker containers, Starting the Docker containers, Starting the Web application.

Creating the Docker containers for the first time.

# To build the Morpheus docker containers. Only needs to be done the first time.

make morpheus-new

Starting the Docker containers & the Web application. Do this after the first time and when you don't need to rebuild the containers and just want to start from the solution.

# To start the Morpheus containers

make morpheus-dev

cd nextui
npm install
npm run dev

Stopping the solution Docker containers.


# To stop the Morpheus containers

make morpheus-down

Kubernetes

Building the Kubernetes environment. The solution creates a four node Kubernetes cluster for testing within the Docker environment. Kubernetes in Docker is used for the development environment setup.

Creating the Kubernetes environment for the first time.

# To build the Morpheus docker containers. Only needs to be done the first time.

cd k8sconfig

# Step 1 - Install kind for testing kubernetes integration
make install-kind

# Step 2 - Install kubectl for interacting with kubernetes
make install-kubectl

# Step 3 - Create cluster
make kind-create-cluster

# Step 4 - change control context to k8s-morpheus
make change-cluster-context

# Step 5 -  Configure monitoring in kind-k8s-morpheus cluster
#           Create the namespace and CRDs, and then wait for them to be
#           available before creating the remaining resources
make config-k8s-monitoring

# Step 6 - Use kubectl to wait until all pods are Running in monitoring namespace
make get-monitoring-pods

# Step 7 - Port forward to grafana within the k8s environment
port-forward-k8s-grafana:
	kubectl -n monitoring port-forward svc/grafana 45556:3000

Developer Workflow

The main branch is only for production. Developers will work only in the dev branch. The following workflow goes through the process of creating a feature branch through submitting a pull request.


# Start in the dev branch

git checkout dev

# Sync latest dev branch changes from GitHub dev (from other developers) into your local

# dev repo

git pull origin dev

# Create your feature branch, this will also switch you into your new feature branch

git checkout -b [your-name/feature-name]

# Work on your new feature files and when you are ready at least stage all of the changes

# you've made before proceeding.

#

# At this point in the workflow, you are ready to push your new feature branch to GitHub.

# Before pushing your feature to GitHub you will need to sync with the GitHub dev branch

# to bring your local dev repo up to date with latest changes.

#

# So now go back to your local dev branch.

git checkout dev

# Sync the latest changes from GitHub dev to bring your local dev up to date.

git pull origin dev

# Now go back to your feature branch

git checkout [your-name/feature-name]

# Now merge any changes that have happened in the dev branch into your feature branch

# This is where merge conflicts may occur

# You will have to resolve any/all merge conflicts, but hopefully there are none

git merge dev

# After merging and resolving any conflicts you can push your feature branch to GitHub

git push origin dev

# At this point your feature branch should be in GitHub and you will need to create a pull request

# specifically for your feature branch so that the branch can be merged into the GitGub dev branch.

#

# The pull request is necessary so that GitHub dev gets your feature changes and other developers

# can pickup your changes.

#

# repeat as needed, happy coding!!

Troubleshooting

Network Capacity

By default, Docker can only create 31 networks. If you try to create more, you would encounter the following error:


Error response from daemon: could not find an available, non-overlapping IPv4 address pool among the defaults to assign to the network

To expand network capacity, add the following to /etc/docker/daemon.json


{
"default-address-pools" : [
{
"base" : "172.17.0.0/12",
"size" : 20
},
{
"base" : "192.168.0.0/16",
"size" : 24
}
]
}

For details, please read The definitive guide to docker's default-address-pools option

Troubleshooting

If you encounter issues: