Cloud Deployment (Multi-VM)

This guide walks through deploying a Titan cluster across two cloud VMs — one running the Master and one running a Worker. The same steps apply to any cloud provider (GCP, AWS, Azure) or bare-metal machines on a LAN.

Local setup first?

If you haven't run Titan locally yet, start with the 5-Minute Quickstart. This guide assumes you have a working local build.

---

Architecture

flowchart LR
    subgraph Local["Your Machine (Mac / Linux)"]
        SDK["SDK / CLI"]
    end

    subgraph MasterVM["Master VM"]
        Master["Titan Master\n(port 9090)"]
        Store["TitanStore\n(port 6379)"]
        Dash["Dashboard\n(port 5000)"]
    end

    subgraph WorkerVM["Worker VM"]
        Worker["Titan Worker\n(port 8080)"]
    end

    SDK -->|"Job Submission (TCP)"| Master
    Master <-->|"AOF / State"| Store
    Master -.->|"Stats Stream"| Dash
    Master -->|"Dispatch (TCP)"| Worker

    style Local fill:#1e293b,stroke:#64748b,color:#ffffff
    style MasterVM fill:#1e293b,stroke:#1de9b6,color:#ffffff
    style WorkerVM fill:#1e293b,stroke:#f9a826,color:#ffffff

The SDK and CLI run locally and submit jobs to the remote Master over TCP. The Master dispatches to Workers using their internal IPs.

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Prerequisites

• Two VMs with Java 17+ (Ubuntu 22.04 recommended)
• Ports 9090, 8080, and 5000 open in your cloud firewall
• A local build of Titan (mvn clean package -DskipTests)

GCP firewall rule

gcloud compute firewall-rules create allow-titan \
  --allow tcp:9090,tcp:8080,tcp:5000 \
  --source-ranges 0.0.0.0/0

For AWS/Azure, open the same three ports in your Security Group / NSG inbound rules.

---

Step 1 — Package the Bundles

Run this once from your local project root. It builds the JAR (if needed) and produces two zip files:

chmod +x package_cloud.sh
./package_cloud.sh

File	Size	Purpose
titan-master-bundle.zip	~2.3 MB	Everything needed to run the Master on a cloud VM
titan-worker-bundle.zip	~120 KB	Worker JAR + titan_sdk for a remote worker node

Master bundle contents:

titan-master-bundle/
├── perm_files/
│   ├── titan-orchestrator-1.0-SNAPSHOT.jar
│   ├── TitanStore.jar
│   ├── Worker.jar
│   ├── server_dashboard.py
│   ├── hitl_gate.py
│   └── Titan_logo.png
├── uploads/          ← job zip staging (AssetManager reads here)
└── start_master.sh   ← one-command startup

Worker bundle contents:

titan-worker-bundle/
├── Worker.jar
├── titan_sdk/        ← so job scripts can import titan_sdk
├── setup.py
└── start_worker.sh   ← one-command startup

---

Step 2 — Set Up the Master VM

2.1 Install Java on the Master VM

sudo apt update && sudo apt install -y openjdk-17-jdk python3-pip

2.2 Upload and start

From your local machine:

scp titan-master-bundle.zip <USER>@<MASTER_EXTERNAL_IP>:~/

SSH into the Master VM, then:

unzip titan-master-bundle.zip
cd titan-master-bundle
chmod +x start_master.sh
./start_master.sh

start_master.sh installs Flask if missing, creates the uploads/ directory, and starts TitanStore, Master, and Dashboard as background processes.

2.3 Verify the Master is up

tail -f ~/titan-master-bundle/master.log

You should see:

Clock Watcher Started...
Scheduler Core starting at port 9090
[OK] SchedulerServer Listening on port 9090
[INFO] Titan Auto-Scaler active.

Open http://<MASTER_EXTERNAL_IP>:5000 in your browser to see the dashboard.

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Step 3 — Set Up the Worker VM

3.1 Install Java and the Python alias

sudo apt update && sudo apt install -y openjdk-17-jdk python-is-python3 python3-pip

python-is-python3 is required

Titan Workers execute scripts using the python command. Ubuntu ships only python3 by default — without this package the worker will fail to execute any Python job.

3.2 Upload and start

From your local machine:

scp titan-worker-bundle.zip <USER>@<WORKER_EXTERNAL_IP>:~/

SSH into the Worker VM. Use the Master's internal IP (10.x.x.x) for VM-to-VM communication:

unzip titan-worker-bundle.zip
cd titan-worker-bundle
chmod +x start_worker.sh
./start_worker.sh <MASTER_INTERNAL_IP>

start_worker.sh installs titan_sdk, exports TITAN_HOST/TITAN_PORT, and starts the worker. Default type is GPU on port 8085.

Specialised worker configurations

# GENERAL worker on port 8086
./start_worker.sh <MASTER_INTERNAL_IP> 8086 GENERAL

# Additional GPU worker on port 8087
./start_worker.sh <MASTER_INTERNAL_IP> 8087 GPU true

You should immediately see in the Master VM's master.log:

Incoming connection from /WORKER_INTERNAL_IP Port...
[INFO] New Worker Registered: WORKER_INTERNAL_IP:8085 [PERMANENT] [GPU]

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Step 4 — Configure the SDK on Your Local Machine

4.1 Point the SDK at the remote Master

export TITAN_HOST=<MASTER_EXTERNAL_IP>
export TITAN_PORT=9090

Add these to ~/.zshrc (or ~/.bashrc) to make them permanent:

echo 'export TITAN_HOST=<MASTER_EXTERNAL_IP>' >> ~/.zshrc
echo 'export TITAN_PORT=9090'                 >> ~/.zshrc
source ~/.zshrc

4.2 Verify the SDK picks them up

python -c "from titan_sdk.titan_sdk import TITAN_HOST, TITAN_PORT; print(TITAN_HOST, TITAN_PORT)"

Must print your GCP external IP — not 127.0.0.1.

4.3 Install the SDK

pip install -e .

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Step 5 — Submit Your First Remote Job

Option A: Python SDK

No pre-staging needed — scripts are sent inline with the payload:

python titan_test_suite/examples/dynamic_dag_custom/complex_etl_pipeline/etl_pipeline.py

This submits a 7-job ETL pipeline (fan-out → fan-in) to the remote cluster. Watch it execute in the dashboard at http://MASTER_EXTERNAL_IP:5000.

Option B: YAML CLI

python titan_sdk/titan_cli.py deploy titan_test_suite/examples/yaml_based_static_tests/dag_structure_test/agent.yaml

The CLI zips the project folder and uploads it to the Master before submitting the DAG.

Option C: DAG Constructor

Open http://MASTER_EXTERNAL_IP:5000/dags/new, build your pipeline in the browser, and use the Upload button in the Constructor to stage your scripts directly — no SCP or pre-staging required. Once your scripts are uploaded, click Deploy to submit to the cluster.

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Managing the Cluster

Stop all services

pkill -f "TitanStore.jar" ; pkill -f "TitanMaster" ; pkill -f "server_dashboard.py"

View live logs

# From ~/titan-master-bundle/ on the Master VM
tail -f master.log      # Master activity and dispatch
tail -f store.log       # TitanStore / persistence
tail -f dashboard.log   # Flask dashboard

# Filter for key events only
grep "UPLOAD\|ARCHIVE\|DISPATCH\|ERROR\|Registered" master.log | tail -20

Redeploy after a code change

# 1. On your local machine — rebuild and repackage
mvn clean package -DskipTests
./package_cloud.sh

# 2. Upload new master bundle to the VM
scp titan-master-bundle.zip <USER>@<MASTER_EXTERNAL_IP>:~/

# 3. On the Master VM — replace and restart
unzip -o ~/titan-master-bundle.zip -d ~/
pkill -f "TitanMaster"
sleep 1
cd ~/titan-master-bundle && bash start_master.sh

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IP Address Reference

Connection	Which IP to use
Worker → Master (registration, heartbeats)	Master internal IP (10.x.x.x)
SDK / CLI → Master (job submission)	Master external IP
Browser → Dashboard	Master external IP
Worker VM SSH	Worker external IP

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Troubleshooting

Worker registers but jobs never dispatch

Check that python resolves on the Worker VM:

python --version  # must work; if not: sudo apt install -y python-is-python3

Dashboard shows jobs as separate pipelines instead of one grouped pipeline

The SDK pushes the manifest to the dashboard automatically after every submission. Ensure port 5000 is reachable from your local machine and the dashboard is running.

DAG_ACCEPTED returned but nothing appears in master logs

The SDK is likely connecting to a local master instead of the remote one. Verify:

python -c "from titan_sdk.titan_sdk import TITAN_HOST; print(TITAN_HOST)"
# Must print the remote IP, not 127.0.0.1

YAML project: true submission accepted but job never runs

Confirm you are running the latest JAR on the Master (built after May 2026). Earlier builds had a directory mismatch bug in AssetManager that caused uploaded zips to be unfindable at dispatch time.