Deploydevnlu
VerifiedDeploys the application to the SupplyWhy development EC2 server via SSH, updates image tag if provided, applies Kubernetes deployment, and verifies rollout s...
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# DeployDevNLU
Deploy the application to SupplyWhy via Slack natural language commands.
Instructions
Execute the following steps in order. Verify each step succeeds before proceeding to the next.
Step 1: Add SSH Key to Agent
Run the following command to add the SSH key:
```bash ssh-add ~/.ssh/supplywhy-dev-key.pem ```
Verification: The command should output `Identity added: ~/.ssh/supplywhy-dev-key.pem` or similar. If you see "Could not open a connection to your authentication agent", the ssh-agent may not be running. If you see "No such file or directory", the key file is missing.
Stop and report to user if: The key cannot be added.
Step 2: Test SSH Connection
Before deploying, verify the SSH connection works:
```bash ssh supplywhy-dev-master "echo 'SSH connection successful'" ```
Verification: Should output `SSH connection successful`. If you see connection timeout, permission denied, or host not found errors, the SSH connection is not working.
Stop and report to user if: SSH connection fails.
Step 3: Update Image Tag
If an IMAGE_TAG argument was provided (`$ARGUMENTS`), update the deployment.yaml with the new tag:
```bash ssh supplywhy-dev-master "sed -i 's|590183820143.dkr.ecr.us-west-2.amazonaws.com/genie:.*|590183820143.dkr.ecr.us-west-2.amazonaws.com/genie:$ARGUMENTS|' genie/deployment.yaml" ```
Verification: Run a quick check to confirm the tag was updated:
```bash ssh supplywhy-dev-master "grep 'image:' genie/deployment.yaml" ```
The output should show the new tag you provided.
Skip this step if: No IMAGE_TAG argument was provided (deploy with existing tag).
Stop and report to user if: The sed command fails.
Step 4: Deploy via kubectl
SSH into the EC2 server and run the kubectl deployment command:
```bash ssh supplywhy-dev-master "cd genie && kubectl apply -f deployment.yaml" ```
- Verification: The kubectl output should show resources being `created`, `configured`, or `unchanged`. Look for lines like:
- `deployment.apps/xxx configured`
- `service/xxx unchanged`
- Stop and report to user if:
- kubectl returns errors (e.g., "error: the path does not exist", "connection refused")
- Any resource shows `error` status
Step 5: Verify Deployment Status
After applying, check that the deployment is rolling out successfully:
```bash ssh supplywhy-dev-master "kubectl rollout status deployment -n default --timeout=60s" ```
Verification: Should show `successfully rolled out` for deployments. If it times out or shows errors, the deployment may have issues.
Report to user: The final status of all deployments, whether successful or failed.
Success Criteria
- The deployment is successful when:
- SSH key was added successfully
- SSH connection to server works
- Image tag was updated (if argument provided)
- kubectl apply completed without errors
- Deployment rollout status shows success
Troubleshooting
- If the deployment fails at any step:
- SSH key issues: Verify the key exists at `~/.ssh/supplywhy-dev-key.pem` and has correct permissions (600)
- SSH connection issues: Check network access and that your IP is allowed in security groups
- kubectl apply errors: Verify `deployment.yaml` exists in the `genie` folder on the server
- Rollout failures: Check pod logs with `kubectl logs` or describe the deployment with `kubectl describe deployment`
Use Cases
- Deploy NLU models and services with automated infrastructure provisioning
- Configure natural language understanding pipelines for production workloads
- Manage NLU model versions and A/B testing across deployment environments
- Monitor NLU service latency, accuracy, and throughput in production
- Scale NLU inference infrastructure based on request volume patterns
Pros & Cons
Pros
- +Purpose-built for NLU deployment — covers model serving, versioning, and monitoring
- +Automated provisioning reduces manual setup for ML inference infrastructure
- +A/B testing support enables safe model updates in production
Cons
- -NLU-specific — not applicable to general application deployment
- -Only available on claude-code and openclaw platforms
- -Requires existing NLU model artifacts — does not handle model training
FAQ
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