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AWS Scaling Study

Let's now give the agent the choice to optimize, telling it that it has a much better selection of instance types. We will first test build and deploy, and then 4 nodes, and then a scaling study.

1. Experiment

We are going to build and deploy with separate plans. We will use the same image we used for our first study for a fair comparison.

# If you are testing:
kind create cluster --config ./kind-config.yaml 
# Experiment
eksctl create cluster --config-file ./eksctl/nodes-arm.yaml
aws eks update-kubeconfig --region us-east-1 --name fractale-arm-cluster

For the experiment, on a node (e.g., Google Cloud node) ensure you have fractale, flux-mcp, and hpc-mcp installed. Start the server:

pip install hpc-mcp mcp-serve kubernetes-asyncio fractale-agents
mcpserver start --config ./servers/kubernetes-job.yaml --port 8089

LAMMPS

Ask the agent to build lammps. Don't forget to export GEMINI_API_KEY

# We are using the container from the first experiments for consistency

for i in {1..5}; do
  echo "Iteration number $i"
  kubectl get pods -o json > .fractale/pods-{i}.json
  kubectl delete miniclusters --all
  fractale run --database json ./plans/deploy-lammps.yaml
done

# One test with a starting size (suffix with optimize)
fractale run --database json ./plans/optimize-lammps.yaml

# And save nodes for run
kubectl get nodes -o json > nodes.json

AMG2023

for i in {1..5}; do
  echo "Iteration number $i"
  kubectl get pods -o json > .fractale/pods-{i}.json
  kubectl delete miniclusters --all
  fractale run --database json ./plans/deploy-amg.yaml
done
kubectl get nodes -o json > nodes.json

Kripke

for i in {1..5}; do
  echo "Iteration number $i"
  kubectl delete miniclusters --all
  fractale run --database json ./plans/deploy-kripke-down.yaml
done
kubectl get nodes -o json > nodes.json

Laghos

Laghos logs were so long we use a function that shorts by way of unique lines.

mcpserver start --config ./servers/kubernetes-job-laghos.yaml --port 8089
for i in {1..5}; do
  echo "Iteration number $i"
  kubectl get pods -o json > .fractale/pods-{i}.json
  kubectl delete miniclusters --all
  fractale run --database json ./plans/deploy-laghos.yaml
done
kubectl get nodes -o json > nodes.json

OSU All Reduce

for i in {1..5}; do
  echo "Iteration number $i"
  kubectl get pods -o json > .fractale/pods-{i}.json
  kubectl delete miniclusters --all
  fractale run --database json ./plans/osu-allreduce.yaml
done
kubectl get nodes -o json > nodes.json

OSU Latency

for i in {1..5}; do
  echo "Iteration number $i"
  kubectl get pods -o json > .fractale/pods-{i}.json
  kubectl delete miniclusters --all
  fractale run --database json ./plans/osu-latency.yaml
done
kubectl get nodes -o json > nodes.json

Note that for each run, I did them separately and checked files, then moved into a results directory named by the application.

Clean up

eksctl delete cluster --config-file ./eksctl/nodes-arm.yaml --wait

2. Discovery Agent

Let's use an agent to work with data, and more specifically, to discover what we need for an analysis. Deploy a local server, with your GEMINI_TOKEN

export GEMINI_TOKEN=xxxxxxxxx
HPCMCP_FILESYSTEM_RESULT_ROOT=$(pwd)/scaling-study-final
HPCMCP_FILESYSTEM_SANDBOX=True
HPCMCP_FILESYSTEM_DATA_ROOT=$(pwd)/scaling-study/
mcpserver start --config ./servers/discover-agent.yaml --port 8089
# Different terminal with GEMINI key
fractale run --database json ./plans/discover-results.yaml

And run fractale targeting the Discovery Agent, with our prompt of interest.